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Perceived Electronic Service Quality: Some Preliminary Results From a Cross-National Study in Mobile Internet Services

Authors:
  • Alba Graduate Business School, The American College of Greece
  • Quryon Korea

Abstract and Figures

Work on how consumers evaluate electronic service quality is both topical and important due to the well accepted criticality of electronic channels in selling products and services. However, the extant research on electronic research quality is preoccupied with the web site internet context and most of the studies are single-country studies, inhibiting conclusions of robustness and generalizability. Theoretically rooted in the Nordic Model of perceived service quality the study uses an e-service quality scale to measure mobile internet service quality and most importantly it does so in different national settings. Consistent with the extant e-service quality literature, results indicate that e-service quality is a second-order factor, with three reflective first-order dimensions: efficiency, outcome, and customer care. Most importantly, cross-validation investigations using samples drawn from Korean, Hong-Kong and Japanese mobile internet user populations support the factorial structure invariance of the construct. Following Cheung and Reynolds' (2002) suggestions, we tentatively examine factor means differences between the three countries contributing to the scarce cross-national electronic service quality literature. Findings imply that though consumers in different countries use the same dimensions so as to evaluate mobile internet services, importance weightings assigned on these dimension are not the same.
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Electronic copy available at: http://ssrn.com/abstract=1269307Electronic copy available at: http://ssrn.com/abstract=1269307
PERCEIVED ELECTRONIC SERVICE QUALITY: RESULTS FROM A CROSS-
NATIONAL STUDY IN MOBILE INTERNT SERVICES
ABSTRACT
Work on how consumers evaluate electronic service quality is both topical and
important due to the well accepted criticality of electronic channels in selling products
and services. However, the extant research on electronic research quality is preoccupied
with the web site internet context and most of the studies are single-country studies,
inhibiting conclusions of robustness and generalizability. Theoretically rooted in the
Nordic Model of perceived service quality the study uses an e-service quality scale to
measure mobile internet service quality and most importantly it does so in different
national settings. Consistent with the extant e-service quality literature, results indicate
that e-service quality is a second-order factor, with three reflective first-order dimensions:
efficiency, outcome, and customer care. Most importantly, cross-validation investigations
using samples drawn from Korean, Hong-Kong and Japanese mobile internet user
populations support the factorial structure invariance of the construct. Following Cheung
and Reynolds’ (2002) suggestions, we tentatively examine factor means differences
between the three countries contributing to the scarce cross-national electronic service
quality literature. Findings imply that though consumers in different countries use the
same dimensions so as to evaluate mobile internet services, importance weightings
assigned on these dimension are not the same.
KEYWORDS
Perceived Electronic Service Quality, Mobile Internet, Cross-National, Multi-
Sample Analysis
Electronic copy available at: http://ssrn.com/abstract=1269307Electronic copy available at: http://ssrn.com/abstract=1269307
2
INTRODUCTION
Perceived electronic service quality constitutes a well-established construct in the
e-commerce literature (e.g., Liao, Palvia and Lin 2006). According to Zeithaml et al.
(2002, p.135) electronic service quality can be defined “as the extent to which a web site
facilitates efficient and effective shopping, purchasing and delivery”.
Research on e-service quality has just started gaining a momentum and the main
research question that all relevant studies try to address pertain to the factorial structure
of the construct and measurement issues (e.g. Wolfinbarger and Gilly 2003). However,
though research on measurement issues is quite advanced, cross-national considerations
of the electronic service quality construct are scarce in the literature. Wolfinbarger and
Gilly (2003) explicitly recognize this research gap and call for more research in the
investigation of electronic service quality vis-à-vis international populations.
Additionally, discussions in the service quality literature questioning the
generalizability of service quality dimensions across different countries (Tsikriktsis
2002), renders the investigation of the stability of the e-service quality dimensionality in
different countries as topical and important.
The present research contributes to the extant e-service quality in two important
ways: First, addressing the call of Wolfinbarger and Gilly (2003) investigates e-service
quality in a cross-national context. Does the factorial structure of the construct is the
same across different nationalities? If this is the case, do consumers in different countries
assign the same rankings of importance on different e-service quality dimensions?
Second so as to further enhance the external validity of the e-service quality literature the
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study investigates the above mentioned research questions in the context of an alternative
electronic channel of services provision, namely the wireless mobile phone internet
channel. An online survey serves as the empirical vehicle of the study, while exploratory
and confirmatory factor analyses (CFA) are used to tackle the research questions under
investigation.
BACKGROUND
Theoretically, the present study builds from the Nordic Model of traditional
services perceived service quality so as to investigate the electronic service quality
construct (Brady and Cronin 2001). The Nordic model conceptualizing perceived service
quality in traditional people-oriented services differentiates between the “what” (i.e. what
the consumer receives as a result of his interaction with a service firm/technical quality)
and “how” (i.e. how he/she gets the outcome resulting from his/her interaction with the
seller/functional quality) components of the buyer-seller interaction. For example the
“what” component of the Nordic Model, is addressed using the notions of aesthetics (i.e.,
how enjoyable and visually attractive is to use the service), whereas the “how”
component is addressed using the notions of ease of use, and customer service among
others.
We do not formally hypothesize a priori propositions relating service quality
dimensions with the countries under investigation. However, building from the scarce
cross-national e-service quality literature (e.g. Tsikriktsis 2002), suggesting culture as
influencing e-service quality dimensions, we hypothesize the existence of cross-national
differences in service quality perceptions. This is consistent with the extant traditional
service quality literature (e.g. Furrer, Liu and Sudharshan 2000). Arguably, the
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theoretical foundation for proposing cross-national electronic service quality is not strong
enough. Therefore, we view the potential for cross-national differences as a tentative
position that may be explored with the data at hand. If the results are promising,
researchers may be encouraged to theorize these cross-national differences rigorously.
Besides contributing to the scarce cross-national e-service quality literature this
article is novel in that it uses the e-service quality construct so as to measure perceptions
of service quality in an alternative e-commerce channel namely the mobile phone internet
services channel.
Mobile Internet, defined as the wireless access to Internet content via mobile
devices, such as mobile phones and personal digital assistants, has advanced
astonishingly both in terms of user population and technology developed (Kim et al.
2002). Wireless Internet services via mobile phone devices became available in Japan,
Korea and Hong-Kong in 1998 (Kim et al 2004) and debuted in Europe in 2002, mainly
through NTT DoCoMo’s i-mode and Vodafone’s Live!, and are rapidly gaining end-user
acceptance throughout the world.
Currently, only few studies have investigated consumers’ reactions to mobile phone
internet services. These studies have not directly investigated these consumer reactions in
the realms of the e-service quality literature. Chae et al. (2002), employing an on-line
survey and structural equation analysis, found four second-order factors of information
quality for wireless Internet services: connection quality, content quality, interaction
quality and contextual quality. However, their research focuses on perceived information
quality rather than perceived service quality, which is a wider construct. Bruner and
Kumar (2005), employing the Technology Acceptance Model, proposed and tested an
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extended consumer TAM for wireless Internet. The core model constructs are those of
usefulness, ease of use and fun. The authors have found that usefulness and fun (directly)
and ease of use (indirectly) influence attitudes toward adopting mobile commerce
services.
On the other hand the literature pertaining to web site wireline internet service
quality is much more advanced. Zeithaml, Parasuraman and Malhotra (2002) suggests
that electronic service quality can be decomposed into four dimensions, namely
efficiency, fulfillment, privacy and technical reliability. Loiacono et al. (2007) propose
twelve dimensions and 3 higher-order constructs, that is ease of use, usefulness and
entertainment. Finally, Wolfinbarger and Gilly (2003) conceptualize electronic service
quality using four dimensions, namely fulfillment/reliability, website design,
privacy/security and customer service.
Admittedly, most research efforts made to measure consumers’ evaluations of
electronic service quality, conclude in giving us extended measurement scales that though
content valid, are practically difficult to use due to their excessive length (e.g. e-
SERVQUAL, consists of 22 items). This is especially the case in the context of the
present study namely handheld internet services, where limited input and output
resources of access devices inhibit the use of extended consumer evaluation instruments.
The present study proposes an abbreviated consumer evaluation instrument that is
practically useful, managerially relevant and psychometrically sound, theoretically
building from the well accepted dual-factor model of perceived service quality (Brady
and Cronin 2002). The study uses seven measures, so as to measure electronic service
quality in mobile internet services. These measures relate to Ease of Use, Usefulness,
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Aesthetics, Content, Privacy, Customization and Customer Service (see Table 1).
Conceptually these measures, tap the theoretical dimensionality of perceived service
quality, namely the functional and technical service quality components proposed by the
Nordic Model (i.e. the “what” and “how” components) (Brady and Cronin 2001).
-Insert Table 1 about here-
RESEARCH METHODOLOGY
The work presented in this paper is part of a wider research project conducted by a
worldwide research consortium, known as the Worldwide Mobile Internet Survey
(WMIS). This paper reports results from WMIS in Korea, Japan and Hong Kong.
First, we develop the service quality scale in the Korean sample employing a split-
sample analysis procedure. We explore the structure of the service quality construct
employing exploratory factor analysis (in the first split sample) and then validate this
structure in the second split sample using confirmatory factor analysis. So as to
investigate the generalizibility of the findings we then move on to investigate the stability
of the service quality dimensionality in two fresh samples collected in two additional
Asian countries, namely Hong-Kong and Japan. We selected these three Asian countries
for two reasons: due to high penetration rates of mobile phone devices in these countries,
(77.2% in Hong Kong, 79.3% in Japan, and 39.8% in Korea) and due to high penetration
rates for mobile internet services usage (e.g. Japanese and Korean markets accounted for
73% and 23% of the total mobile internet market in Asia, respectively (Kim et al 2004)).
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Korean Sample
To collect data efficiently and increase the validity of the empirical data, we
constructed a research consortium consisting of every mobile operator in Korea, major
Internet portals and mobile Internet application developers. Μobile operators verified the
survey data and provided funding to our project.
Data were collected employing a large scale online survey and potential
respondents were given participation incentives. Two major concerns in Internet-based
surveys are the respondents filling out the survey multiple times and “random walk-ins”
(Deutskens et al., 2004; Ilieva et al., 2002). Mobile operators checked whether the phone
numbers self-reported were legitimately registered and whether the owners of the phone
numbers had accessed the mobile Internet at least once in the past.
A total of 15,516 people participated in the survey. Those who did not pass the test
were deleted from the data set. The number of the final effective respondents was 8,912.
This data-collection procedure increases the external validity of the results, since
participants belong in the actual customer base of large mobile operators. The sample is
almost split between men and women, though women are slightly more represented than
men. The age of respondents ranged from 12 to 80 years, with a median of 24 years. Most
of the respondents were in their early 20s (50.3%). In terms of gender, age, and
occupation distributions, our sample may be considered as representative of mobile
Internet users in Korea (Sir et al. 2003).
Hong-Kong Sample
So as to collect data from the Hong-Kong sample we employed an online survey
methodology-as was the case with the Korean sample. The questionnaire was
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administered on a non-profit public website run by the Hong Kong government. An e-
mail soliciting participation in the survey was sent to registered members of the website.
Also, a banner advertisement of the survey was made available on the website over a
period of four weeks. To reduce the possibility that a respondent participated in the
survey more than once, each respondent was required to provide his/her mobile phone
number in the survey. To encourage participation, incentives of the latest models of
mobile phones and MP3 players were offered as lucky draw prizes. A total of 1826 valid
responses were collected from the current user group. In total, there were 8941
respondents who successfully completed the questionnaires; of which 7045 were
potential users and 1826 were current users. 817 respondents were males (44.7%) and
1009 were females (55.3%). The age of respondents ranged from 13 to 76 years, with a
median of 25 years. Most of the respondents were in their 20s (53.1%) and 30s (18.0%).
The length of experience with using mobile Internet ranged from 1 to 44 months, with 15
months as the median, and 17.2 months as the mean.
Japanese Sample
A research center administered the data collection process in Japan. Online panel
members were solicited via email requests. More specifically the questionnaire was
uploaded on the homepage of MIN1, and e-mails were sent to ECOM2 members-along
with requests to other relevant parties through ECOM- and to MIN monitors (e.g., i-mode
monitors), asking to access the questionnaire page.
1 Marketing Interactive Network-a marketing website that enables the creation of online panels consisting
of mobile internet (e.g. MIN i-mode monitors) and stationary internet users. MIN is an official research
partner of the Electronic Commerce Promotion Council (ECOM) of Japan.
2 Electronic Commerce Promotion Council of Japan
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A total of 3,310 people participated at the survey. The effective number of
respondents was 2,151 (number of respondents self-reporting currently using mobile
internet services). The length of experience with using mobile Internet was 79 months
(median value), indicating Japanese respondents as quite experienced with mobile
internet services. The sample was somewhat balanced between men (47%) and women
(53%) and the age of respondents ranged from 14 to 73 years old with a median of 34
years. Three out of ten respondents reported being less than 30 years old. Therefore
compared to the Korean and Hong-Kong data sets, Japanese respondents reported
belonging in older age groups.
METHOD OF ANALYSIS
Internal Analyses
A random split-sample approach was employed (Babakus et al. 2004;
Diamantopoulos and Siguaw 2000). In the first half sample (N= 4,456) Exploratory
Common Factor Analysis was used as a first step in identifying the factor structure of the
electronic service quality construct. Then, in the second half of the sample alternative
first-and second-order factor models were tested through Confirmatory Factor Analysis
to examine which model of perceived electronic service quality is superior in fitting the
sample data.
Measurement Invariance
Measurement invariance determines if items used in survey-type instruments mean
the same things to members of different groups (Cheung and Rensvold 2002). We
conduct tests for both category 1 and category 2 invariance (Cheung and Rensvold 2002).
We conduct configural, metric, scalar and invariance tests so as to investigate the
10
generalizibility of the results found in the Korean sample, in two more samples and then
tentatively examine the existence of latent mean differences in these three countries
employing mean structure analysis procedures (MACS) (Arbuckle and Woethke 1999)
Issues in Assessing Measurement Invariance
An important issue in measurement invariance tests relates to the choice of criterion
to be used so as to assess measurement invariance. Two categories of criteria exist (Chen,
Sousa and West 2005): the first one utilizes purely statistical criteria namely the χ2
difference test-whereas the second criterion involves practical criteria namely fit indices.
Currently the most widely used criterion is the χ2 difference test. However the
likehood ratio test is sensitive to non-normality and has substantial power in large
samples to detect small discrepancies between groups that may be of no practical
importance (Chen, Sousa and West (2005).
Currently the best available guidelines for the usage of practical fit indices in
testing for measurement invariance are those proposed by Cheung and Rensvold (2002).
They concluded that a difference of larger than .01 in CFI would indicate a meaningful
change in model fit when testing for measurement invariance.
RESULTS
Power Analysis
Statistical power is the probability of rejecting a false model when it is false
(McQuitty 2004). If models do not have adequate power, then their contribution to
knowledge is uncertain . In general an accepted level of power is 0.80 (McQuitty 2004) .
So as calculate statistical power we used a stand-alone DOS program called NIESEM
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written by Paul Dudgeon3. The NIESEM program is based on the work of
MacCallum,Browne and Sugawara (1996). Power is close to unity, indicating almost
zero probability of conducting type II error.
Exploratory Factor Analysis (Korean Sample)
Common Factor Analysis with varimax rotation was employed to determine the
factor structure of the perceived electronic service quality construct (Gorsuch 1990)..
However, Component Analysis with varimax rotation was also tested to confirm the
emerging factor structure.
We tested a two-, three-, and four-factor model. Based on the percentage of
variance criterion (Hair et al. 1998), the analysis revealed the three factor solution as
more appropriate, explaining 52.5 percent of the total variance (see Table 2). All
measures load clearly to the three factors extracted. Component analysis revealed the
same factor structure, explaining 73 percent of the total variation, while all seven factor
loadings were greater than .75.
-Insert Table 2 about here-
The first factor, explaining 23 percent of the total variance, constitutes the
Efficiency Quality dimension and is related to the ease of use and the usefulness of the
wireless Internet service. Zeithaml et al. (2003, p.365) suggest that efficiency constitutes
a dimension of electronic service quality, defining efficiency as “the ability of the
customers to get to the web site, find their desired product and information associated
with it and check out with minimal effort”. In the human computer interaction (HCI)
literature, efficiency quality refers to whether the consumer perceives that the task is
3 available at http://rubens.its.unimelb.edu.au/~dudgeon/
12
performed without making mistakes or putting too much effort (Sing 2003). In both
definitions it is suggested that efficiency has to do with ease of use and usefulness.
The second factor, accounting for 16 percent of the total variance, is Outcome
Quality. This factor encompasses emotional benefits (i.e. enjoyment), visual
attractiveness (i.e., aesthetics) and content variety (i.e., functional benefits) provided by
the use of wireless Internet services. Outcome quality reflects the “product” of the service
act itself, or in other words what the customer receives in the service encounter (Brady
and Cronin 2002).
The third factor, Customer Care Quality, explains 13 percent of the total variance.
Customer care quality relates to adapting the wireless service to user preferences (level of
personalization), to minimization of personal data provided (privacy issues), and to the
customer service provided, especially when consumers experience a problem while using
wireless Internet services. Gounaris and Dimitriadis (2003) propose the dimension of
customer care in their work related to service quality in business-to-consumer Internet
portals. They found that this dimension encompasses issues like privacy of shared
information and customer service.
Based on these findings, the proposed research model is depicted in Figure 1.
Perceived wireless Internet electronic service quality is suggested to be a second-order
factor, with three first-order factors, namely efficiency quality, outcome quality, and
customer care quality. The proposed research model is strengthened by the growing
stream of evidence conceptualizing perceived service quality as a multidimensional
construct of hierarchical nature (Brady & Cronin 2001, Dabholkar, Thorpe and Rentz et
al. 1996).
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-Insert Figure 1 about here-
Confirmatory Factor Analysis (Korean Sample)
Following the methodology employed by Doll, Xia and Torkzadeh (1994) and
Sommers et al. (2003) we tested the proposed second-order factor model against three
other possible alternative factor structures (see Figure 2). More specifically, we tested the
proposed research model against a one first-order factor model, a three-factor model with
orthogonal factors, and a three-factor model with correlated factors.
-Insert Figure 2 about here-
We used the Maximum Likelihood (ML) estimation method to estimate the
parameters of the models, since ML-based fit indices outperform those obtained from
other methods (Hu and Bentler 1998 ).Based on this guideline, the CFI, Delta 2, RMSEA,
chi-square and standardized RMR fit indices for all four alternative factor structures are
reported in Table 3( Schumacker and Lomax 2004, Hu and Bentler 1999)
Insert Table 3 about here
Based on the fit indices, the one first-order factor model is far from being
acceptable. The uncorrelated three-factor model is also not supported by the data
covariance matrix. Finally, the correlated three-factor model has the same fit indices with
the second-order factor model, indicating adequate fit. However, theory in the domain of
perceived service quality suggests the existence of a second-order factor that accounts for
the common variance of the first-order factors. This model seems to be theoretically more
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interesting than the correlated three-factor model4 Doll, Xia and Torkzadeh (1994). With
the exception of the chi-square statistic, the proposed second-order factor structure fits
the data reasonably well (χ2 (11) = 223.91 and p=0.00, CFI=.97, Delta 2=.97, SRMR=.03
and RMSEA=.066).
The internal structure of the proposed model was also examined. First, we
examined the parameter estimates and the accompanying tests of significance (Bagozzi
and Yi 1988). Convergent validity is implied by the magnitude of the factor loading of
each measure on its suggested latent variable (Mathwick et. al 2001; Dabholkar, Thorpe
and Rentz 1996). All λ’s are greater than the .60 level proposed by Bagozzi and Yi
(1988), except λ42 (aesthetics measure) which is marginally below .60. Moreover, all λ’s
are significant since all t-values are above the |2.00| level suggesting convergent validity.
The results are summarized in Figure 3.
-Insert Figure 3 about here-
Discriminant validity can be demonstrated by calculating covariance confidence
intervals (plus or minus two standard deviations) around the factor covariances. In our
case all confidence intervals computed do not include the value of 1.00 suggesting
discriminant validity (Mathwick et. al 2001; Dabholkar,Thorpe and Rentz1996).
Regarding measurement model fit, we used the Composite Reliability (ρc) and
Average Variance Extracted (AVE) criteria. Bagozzi and Yi (1988) propose that ρc and
AVE should be greater than 0.6 and 0.5 respectively. These cut off points hold for the
4 Further support for the superiority of a second-order factor model can be found in the structural equation
modeling literature. Chen, Sousa and West (2005) point to the next set of advantages: (a) a second-order
model can test whether a hypothesized second-order factor can actually account for the pattern of relations
between the first-order factors, (b) puts a structure on the pattern of covariance between the first-order
factors, (c) separates variance due to specific factors (these specific factors are represented by the
disturbance of each first-order factor), leading to a theoretically error-free estimation of the specific factors
and (d) can provide useful simplification of complex multitrait-multimethod models
15
three constructs, except from the Outcome Quality construct with an AVE of .44. The
composite reliability of Outcome Quality is .61 exceeding the suggested cut-off point (see
Table 4)
-Insert Table 4 about here-
Confirmatory Factor Analysis (Japanese and Hong-Kong Samples )
Japanese Sample
CFA results for the Japanese sample indicate acceptable fit indices values with the
exception of the RMSEA index (see Table 4). More specifically χ2 (11)= 284, 41 (p<.00,
CFI and Delta 2 equal .945, marginally less than the cut-off criterion of .95 suggested by
Hu and Bentler (1999)(see Table 5). SMRS is .045 less than the established .05 level.
RMSEA equals .11 indicating poor fit5.
Altogether these results suggest that the measures used are unidimensional. All λ’s
are greater than the .60 level (Bagozzi and Yi 1988). Moreover, all λ’s are significant
since all t-values are greater than the |2.00| level indicating convergent validity (see Table
6). Additionally covariance confidence intervals computed do not include the value of
1.00 indicating discriminant validity6 ( Schumacker and Lomax 2004) . Composite
Reliability, and AVE are greater than 0.6 and 0.5 respectively with the marginal
5 Regarding the high RMSEA value, we follow Parasuraman, Zeithaml and Malhotra (2005) and the
structural equation modeling literature and point that the interpretation of any fit index in isolation could be
problematic because trade-offs between Type I and Type II errors call for the interpretation of
combinations of indexes in various model contexts. Another related issue is statistical power which have to
be taken into account when interpreting fit indices. In studies where power is overly great (i.e., > 0.9-as is
the case with the present study may require a more relaxed interpretation of fit than is typical. Conversely,
a more stringent interpretation of fit statistics is required when power is low (McQuitty 2004). The high
statistical power of the present study and the acceptable values for the CFI and SRMR indices, seem to
mitigate the somewhat high root mean square error of approximation (RMSEA) values (Parasuraman,
Zeithaml and Malhotra 2005)
6 We also checked for discriminant validity employing the most stringent criterion of Fornell and Larcker
(1981) namely we tested whether AVE from each latent is greater than its shared variance with the other
two latents (γ2).Results indicate that for each pair of latents AVE> γ2 , though the AVE of “customer care”
quality is marginally greater than squared correlation of “customer care” quality and “efficiency”.
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exception of the “customer care” factor (AVE=.49) (see Table 6). Figure 4 depicts the
standardized, second-order mobile internet perceived service quality path diagram for the
Japanese sample.
-Insert Figure 4 about here-
Hong-Kong Sample
Confirmatory factor analysis results for the Hong-Kong sample indicate a
unidimensional and reliable measurement model (see Table 4). Fit indices are in the
established acceptable ranges indicating adequate fit between the model and the data at
hand (χ2 (11) = 148, 21 (p<.00), CFI=.97, Delta 2=.97, SRMR=.039) (see Table 5), with
the possible exception of the RMSEA index, which is marginally greater than the .08
criterion indicating reasonable fit Schumacker and Lomax (2004). Pertaining to the
somewhat high RMSEA value, the acceptable values of CFI and SRMR in association
with the high statistical power of the present study seems to mitigate this problem
(Zeithaml, Parasuraman and Malhotra 2005). Altogether these results indicate acceptable
unidimensionality.
The composite reliability index for the three first-order factors is greater than .70
surpassing the .60 criterion suggested by Bagozzi and Yi (1988) and AVE is greater than
the established .50 cut-off value. Additionally correlation confidence intervals (plus or
minus two standard deviations) computed for the three first-order factors do not include
the value of 1.0 indicating discriminant validity. Altogether these results are indicative of
reliability, convergent and discriminant validity (see Table 6). Figure 5 depicts the
second-order mobile internet service quality construct for the Hong-Kong sample.
17
-Insert Figure 5 about here-
Measurement Invariance
So as to test for the measurement invariance of the second-order service quality
model across the three countries we follow the general procedures suggested by Byrne
(2004) and Chen, Sousa and West (2005), essentially testing for a series of increasingly
constrained hierarchical nested models.
Configural Invariance (Model 1)
Testing for this form of invariance requires the specification of an unrestricted
baseline model. The simultaneously estimated model provides the baseline value against
which all subsequently specified (increasingly constrained models) are compared. This
multi-sample analysis yields only one set of fit statistics (Byrne 2004). Altogether fit
indices for this unconstrained model indicate acceptable fit (χ2 (33) = 650,390 (p<.00),
CFI=.97, Delta 2=.97, RMSEA=.047, SRMS=.028). These results support the validity of
the hypothesized three-factor service quality model across Korea, Japan and Hong-Kong.
Invariance of first-order loadings (Model 2)
In testing for this level of factorial invariance, all first-order loadings were
constrained to be equal across the three countries. This model is nested within the fully
unconstrained model (model 1). The chi-square difference test is significant (∆χ2 (df=8)
=56,320 (p=.00)), indicating non-invariance of the first-order factor loadings across the
three countries. However, given that the test was based on a large sample size, and due to
no substantial difference in CFI (CFI=.003, .966 vs. .963) we concluded that there was
no appreciable difference between the unconstrained model and the first-order
18
measurement weights constrained model (Chen, Sousa and West 2005; Cheung and
Rensvold 2002).
Invariance of second-order factor loadings (Model 3)
Testing for this level of invariance necessitates that all first-order and second-order
factor loadings to be constrained to be equal across the three groups. This model is nested
within model 2. The chi-square difference test is significant (∆χ2 (df=4) =18,622
(p=.00)), indicating non-invariance of the second-order factor loadings across the three
countries. However, the difference in CFI was not substantial (CFI=.001, .963 vs.
.962), therefore we concluded that there was no appreciable difference between model 2
and 3.
Invariance of intercepts of observed variables (Model 4)
In model 4, in addition to the constraints imposed on first-order and second-order
factor loadings in model 3 the intercepts of the observed variables were constrained to be
equal across the three countries. This is a prerequisite for comparing latent means across
groups Chen, Sousa and West (2005).The fit of this model is not good (∆χ2 (df=14)
=2626,280 (p=.00), CFI=.146). Following Steenkmamp and Baumgartner (1998) and
Ueltchy et al. (2004) we examined for partial scalar/intercept invariance. We do so
examining each pair of the three participating countries. As Steenkamp and Baumgartner
(1998, p. 81) point: “…at least one item besides the marker item has to have…invariant
intercepts in order for cross-national comparisons of factor means to be meaningful”.
Japan- Hong-Kong
The specific strategy employed so as to test for partial scalar invariance is the one
suggested in Byrne (2004, p. 285). Tests for scalar invariance pertaining to these two
19
countries indicate partial scalar invariance based on Steenkamp and Baumgartner (1998).
More specifically we find partial scalar invariance for the “customer care” and “outcome
quality” factors (for both factors only one intercept of an observed variable-customization
and content correspondingly-is found to be invariant). The comparison of the model
having constraints for the first-order and second-order factor loadings with the model
further imposing invariance constraints on two intercepts indicates a .01 difference in CFI
(∆χ2 (df=2) =94,183 (p=.00), the level Cheung and Renvold (2002) suggested as
indicative of practical invariance. These results indicate that we are entitled to test for
difference in factors means between these two countries but only for the customer care
and outcome quality constructs, since we do not find partial scalar invariance for the
efficiency factor.
Japan- Korea
Assuming model 3 to be correct (invariance of first-order and second-order factor
loadings) the model with contstraints on the full list of measured variables invariance do
not fit the data (∆χ2 (df=7) =2117,86 (p=.00), CFI=.161). Once again we start
investigating for partial invariance following Byrne (2004). Tests for the “customer care”
subscale (all three intercepts are posed to invariant) are not good (∆χ2 (df=3) =1083,380
(p=.00), CFI=.082). Following these results we started relaxing intercept invariance
constraints. Results are not good for the “customer service” observed variable (∆χ2
(df=2) =624,598(p=.00), CFI=.047), though the results for CFI are less than the
benchmark (<.05) suggested by Little (1997), but high above the suggestions of Cheung
and Rensvold (2002). Increasingly relaxing constraints do not improve the CFI criterion
to be less than the Cheung and Rensvold (2002) cut-off. Therefore results suggest that we
20
are not entitled to conduct a means difference test for the customer care latent in this pair
of countries, following Cheung and Rensvold (2002) but we can do so if we rely on Little
(1997). Results for the outcome quality latent, indicate partial scalar invariance.
Constraining both observed variables indicate intercept invariance following Little (1997)
since CFI=.024<.05. Relaxing one of the observed variables indicates a CFI less than
.01. Results for the efficiency factor suggest a CFI equal to .013 marginally greater than
the Cheung and Rensvold (2002) criterion.
Hong-Kong- Korea
Results for fully constraining intercepts of measured variables for this pair of
countries indicate non-invariance (∆χ2 (df=7) =265,335 (p=.00), CFI=.02). However
CFI equals.02, which is greater than the Cheung and Rensvold (2002) criterion
(CFI<.01) but much less than Little’s (1997) suggestion (<.05).Results for subscales
indicate partial scale invariance for the customer care factor ((∆χ2 (df=2) =77,347
(p=.00), CFI=.006) and the outcome quality (∆χ2 (df=4) =98,923 (p=.00),
CFI=.008). Results for the efficiency factor are marginal (∆χ2 (df=5) =139,401
(p=.00), CFI=.011)
Summary
In summary, results for the invariance of measured variables intercepts (along with
results pertaining to first-order factor loadings), indicate that we are entitled to compare
all factor means in the Hong-Kong –Korea pair, outcome quality and customer care
means for the Japan- Hong-Kong pair and the “outcome quality” means for the Japan-
Korea pair. Whatsoever in the latter pair, due to the marginality of results we will
tentatively report the means differences tests for the efficiency factor too.
21
Means Structure Analyses
Means structure analyses are required so as to investigate latent mean differences
between groups. So as to obtain estimates of the differences between the first-order
factors in the three groups, in each pair of countries one was chosen as a reference or
baseline group and its first-order factor means was set to zero. Then the latent means of
the other group was estimated; this value is the difference between the factor means of
the two groups/countries. The significance test (z value) indicates whether there is a
statistically significant difference in the latent means of the two countries analyzed.
So as to directly compare first-order factor means between pairs of countries we
specified a correlated first-order factor model of perceived service quality. As Chen,
Sousa and West (2005, p. 485) note: “the first-order factors means are conditional on the
higher-order factor mean (s) in a hierarchical model, and thus cannot be directly
compared.” We considered the possibility of second-order mean comparisons, but such a
test was inappropriate due to non-invariant second-order intercepts. Results are reported
in Table 5.
Hong-Kong- Korea
Invariance of first-order factor loadings and intercepts of measured variables was
imposed on the Korean and Hong-Kong samples. The Korean sample was chosen as the
baseline group and its latent mean was set to zero. There was a significant mean
difference between the two countries in all three factors. More specifically results
indicate that Hong-Kong scores lower on the importance of efficiency (-.24, z=-8,43)
22
p<=.00), outcome quality (-.32, z=-9,90, p<=.00), and customer care (-.23, z=-6,30,
p<=.00).
Japan- Hong-Kong
Based on the invariance test conducted, factor means difference tests for this pair of
countries was conducted for the customer care and outcome quality constructs. There was
a significant mean difference between the two countries only in the “customer care”
factor. More specifically results indicate that Hong-Kong scores higher on the importance
of customer care (.417 z=9,74, p<=.00). There was no difference on the outcome quality
factor (.02, z=,50, p<=.62).
Japan- Korea
Based on the invariance test conducted, factor means difference tests for this pair of
countries was conducted for the “outcome quality” latent. More specifically results
indicate that Koreans-compared to Japanese-believe the outcome quality factor as being
more important when experiencing mobile services (.12 z=,40, p<=.out 00). Due to the
marginal results obtained when investigating intercept invariance for the efficiency
factor, we tentatively report the means difference results for this factor too. It seems that
Koreans assign significantly less important than Japanese in the efficiency factor (.13 z=-
6,58, p<=.00)
-Insert Table 5 about here-
DISCUSSION
This study uses the established e-service quality literature so as to measure
perceived service quality in the mobile phone internet services context. Next, following
discussions in the service quality literature questioning the generalizibility of service
23
quality dimensions across different countries and that technology acceptance may differ
across countries (Straub, Keil and Brenner 1997), it iinvestigates the stability of the
proposed dimensionality in two new samples drawn from different countries.
Results imply that the same dimensionality holds for the three countries
investigated. Configural invariance results imply that participants belonging in the three
countries investigated conceptualize the construct of service quality in the same way.
Finally an important contribution of this research effort pertains to the differing relative
importance that different countries assign on service quality dimensions in the context of
mobile internet services.
Though we did not formally hypothesize, a priori specific propositions relating
service quality dimensions with the countries under investigation, our results confirm
findings in the scarce cross-national e-service quality literature (Tsikriktsis 2002),
suggesting culture as influencing e-service quality dimensions. The results indicate that
companies should take into account these different importance weightings when
allocating resources for improving service quality in different countries.
Even though the services literature suggests information-based services as easier to
standardize across nations7, our results indicate that this may not be the case. The reader
should take into account though, that we did not directly account for the influence of
cultural dimensions on perceived mobile e-service quality dimensions. In the context of
this study, countries are considered as cultural characteristics proxies. This logic, is
strengthened from the worqk of Hofstede (1980), and evidence purporting the three
countries sampled in this study as scoring differently in three dimensions of national
7 Compared to people-processing services and possession-processing services (Furrer, Liu and Sudharshan
2000)
24
culture (1980), namely masculinity, individualism and uncertainty avoidance (see Kim et
al. 2004) for a discussion on these specific scores).
Though we expect the relative importance of mobile e-service quality dimensions
to be different across the three Asian countries investigated we consider our results as
tentative on this matter and call for more research involving strong a priori hypothesis
linking specific dimensions as more or less influenced by differing cultural
characteristics.
Whatsoever, we believe that a post-hoc effort to explain differences found on
relative importance assigned on different mobile e-service quality dimensions is
worthwhile. This strategy has precedence in the literature (e.g. see Straub, Keil and
Brenner 1997). Therefore using post-hoc explanations, we build our discussion on the
work of Kim et al. (2004) who tried to explain differences in the usage of mobile internet
services in the Japan, Hong-Kong and Korea based on cultural dimensions (masculinity,
uncertainty avoidance and individualism) and economic factors (gross national income,
internet penetration rates, broadband internet penetration rates).
In this study, so as to provide explanations for means differences found, we make
use of the uncertainty avoidance index and of the reported differences in economic
factors characterizing the three countries (Kim et al. 2004)
We start the discussion with the findings indicating Koreans as assigning more
importance in all three factors when compared with Hong-Kong respondents. The finding
that Koreans assign more importance on ease of use and usefulness issues might be
explained by differences found in economic factors (lower income and maturity of the
broadband stationary internet) (Kim et al. 2004). More specifically, this can be explained
25
by the high penetration of broadband internet in Korea, compared to Hong-Kong, and
more specifically on the notion of relative advantage (Kim et al. 2004). Mobile internet
via handheld devices was less readily adopted in Korea compared to Hong-Kong (and
Japan) due to the relative advantage of the stationary internet (i.e. much richer
information environment at a less cost). Therefore, one can hypothesize that Koreans
would like to have mobile internet services that are more easy to use based on the
following reasoning: difficult to use services might increase the cost of using such
services (at least in the case of a time-based revenue business model) and cost is a much
more important factor for Koreans, due to lower gross-national income and the cheaper
stationary internet.
Most importantly Koreans, compared to Hong-Kong residents, score higher in the
uncertainty avoidance cultural dimension. This entails ease of use as more important
since, easy to use services reduces the possibility of service failure and therefore
underscores higher confidence levels for the service used.
In the same vein, Koreans assign more importance on usefulness (i.e. a service that
satisfies given task), due to cost reasons (i.e. they are not that willing to pay for mobile
internet in the case it does not provide useful content, since they can satisfy their needs
cheaper using stationary internet). Generally speaking one can explain greater importance
assigned on all three factors of service quality from Koreans on the fact that they have
greater service quality expectations due their prior experience with high-speed mature
stationary internet services (e.g., they seem to assign more importance on the outcome
quality -namely the content depth and width-along with aesthetic appeal- when compared
26
to Hong-Kong respondents, something that might be due to their prior experience with a
much richer internet environment both in terms of content variety and visual elements).
Finally a possible explanation supporting the greater importance Koreans assign on
customer care may be found on the higher uncertainty avoidance scores of Koreans (Kim
et al. 2004). Uncertainty avoidance is the extent to which, the member of a culture feel
threatened by uncertain or unknown situations (Hofstede 1991). Therefore in these
cultures uncertainty associated with a possible service failure has to be reduced by the
guarantee of a quick solution to the problem” (Furrer, Liu and Sudharshan 2000, p. 360.
Therefore the existence of a customer service department, though admittedly important in
both countries, might be more important for cultures exhibiting higher levels of
uncertainty avoidance. The same reasoning might be employed for the privacy observed
variable. One could expect cultures with high uncertainty avoidance, to exhibit higher
wariness levels when it comes to privacy concerns.
Finally, greater relative importance imposed on customization might be also
explained by economic factors. Providing customization mechanisms in a mobile internet
services context is important , since it allows for a more efficient way of fulfilling desired
tasks and therefore requires less expenses (i.e. in terms of money paid for navigating the
service-time based revenue business model-and in terms of system resources, i.e., battery
resources)
We now move on to discuss the significant differences found in the Japan-Korea
pair of countries. Mean structure analysis indicated Japanese as assigning more
importance than Koreans on the efficiency factor, whereas it seems that Koreans assign
more importance on the core-product factor. Pertaining to the core-product factor and
27
continuing the line of reasoning explicated previously, one possible explanation for such
a state is the extensive prior experience of Koreans (when compared with Japanese) with
stationary broadband internet services. Fast stationary internet connections enable the
provision of content services that are wider both in terms of width and breadth.
Additionally Koreans seem to more favorably rate outcome quality due to their
current mobile services usage pattern. As Kim et al. (2004) point, Koreans (and Hong-
Kong residents as well) seem to prefer using mobile services that are more of a hedonistic
rather than a utilitarian character. For this kind of services it seems reasonable to say that
content depth and width as well as visual elements (aesthetics) are more important. On
the other hand Japanese, seem to more frequently use utilitarian mobile services (e.g., e-
mail, buying train tickets).To put it more bluntly, mobile services preferred by Koreans
(i.e., hedonistic services, for example downloading music content), are primarily
evaluated with criteria like content depth and width and visual/presentation elements,
therefore having a prominent status in Koreans importance weighting schemes.
On the other hand services preferred by Japanese, namely utilitarian services (e.g.
reading news, stock exchange information sending e-mails and booking train tickets), are
primary evaluated with criteria pertaining to the reliability and accuracy of the
information and not that much by presentation issues (Chae et al. 2002).
Pertaining to the greater importance assigned on ease of use and usefulness (i.e., the
efficiency factor) by Japanese when compared with Koreans, a logical assumption is that
such a state holds due to the higher-levels of uncertainty avoidance characterizing
Japanese. Hofstede (1980) argued that uncertainty avoidance relates to a general feeling
of anxiety when confronted with problems or challenges. Easy to use mobile services
28
reduce the possibility of confronting problems therefore reducing anxiety levels and
enhancing cognitions of confidence.
Further theoretical support for the relationship between uncertainty avoidance and
ease of use can be found using transaction cost theory (Devarai, Fan and Kohli 2002).
Uncertainty seems to constitute a form of transaction cost and ease of use is posited as a
mechanism for reducing such a transaction cost. Recently, Hwang (2004) found a
positive relationship between uncertainty avoidance and ease of use.
Pertaining to usefulness, it seems that Japanese may view it as more important due
to higher masculinity levels. In masculine-like societies, performing is highly valued and
useful services (i.e., services that enhance one’s performance (Davis 1989)) seem to be a
mechanism for attaining higher performance in everyday life activities.
Finally, pertaining to the Hong-Kong- Japan pair, our results imply that Hong-Kong
nationals assign more importance on customer care than Japanese. Based on the higher
uncertainty avoidance scores of Japanese when compared to Hong-Kong nationals, one
would expect a different sign in this difference. However, a potential explanation for such
a difference might be also found on the higher expectations that Hong-Kong nationals
might have for electronic service quality due to their extensive prior experience with
likewise stationary internet services. Additionally according to Kim et al. (2004), Hong-
Kong nationals seem to primarily use mobile internet services for commercial rather than
communication exchanges. It is expected therefore that due to the potential of economic
loss in their transactions, facets of service quality like privacy and customer service are
prompted as more important.
29
To sum up with it seems that mobile service providers with an active presence in
these three countries should not be guided by simplistic rules when investing resources
for improving service quality. Though, all service quality factors are important so as for
consumers to infer high service quality assumptions, the relative importance of these
factors is differential and managers should try to localize their resource allocation
strategies in the quest for high service quality ratings. Standardizing service quality
investment programs might be tempting due to cost advantages but this strategy may not
be on the right track.
LIMITATIONS AND FURTHER RESEARCH
This study is not without limitations. However these limitations present
opportunities for future research. First, the reader should take into account that our results
pertaining to mean structure analysis, and more specifically on measurement invariance,
heavily depend upon the criterion used so as to infer measurement invariance. Chen,
Sousa and West (2005) point, that currently the methodological literature is armed with
two measurement invariance criteria, namely the likelihood ratio criterion and the CFI
criterion. The former should be considered as too conservative whereas the latter should
be considered as a liberal test of measurement equivalence. This research study follows
Cheung and Rensvold (2002), who find CFI as the best performing index8 for
investigating measurement invariance.
Whatsoever, test statistics and fit indices should no replace sound judgment and
substantive expertise (Bollen 1993). Prior research on the relationship between culture
and e-service quality (e.g., Tsikriktsis 2002) along with the different patterns of using
mobile services in the three countries examined (Kim et al. 2004; Lee at al. 2002),
8 in terms of not being overly sensitive to small errors of approximation
30
increase our confidence of the results found employing the CFI criterion: relative
weighting schemes pertaining to mobile services quality dimensions are different across
countries.
Another important limitation involves employing an online survey design which
introduces self-selection bias problems (Chae et al. 2002). Self-selection bias might
create problems of sample representativeness. However, we are confident that due to the
screening procedure employed (e.g., in Korea almost half of the primary respondents
were deleted from the data set), our sample is consisted of real mobile internet users, and
based on their self-reported demographics they are representative of the mobile internet
user population in Korea (Sir et al. 2003). The problem of self-selection bias was
somewhat mitigated in the Hong-Kong and Japanese samples where along with banner
advertisements of the survey, e-mails were sent to registered users of specific web sites
that agreed to participate in the survey.
Once again, a convenience sampling methodology entails representativeness bias
concerns, but this was due to budget constraints and due to the complexity and cost of
simultaneously managing data collection in three countries. However, one should take
into account, the exploratory nature of the present study, since it is probably one of the
first rigorously investigating electronic perceived service quality differences in more than
one national markets.
Another important issue that merits discussion, due to its potential threat to our
study’s validity is concerns about the content validity of instrument used. We did not
employ measures pertaining to the technical reliability of mobile internet services (e.g.
times a mobile internet site crashes e.t.c). The dimension of technical reliability, relates to
31
QoS issues (i.e., network performance), and admittedly the measurement of such issues is
much more objective than the measurement of other potential service quality dimensions
(e.g., perceptions of usefulness). To put it differently, in this research study we consider
technical reliability as a given, as a pre-condition for a good mobile internet service. This
has precedence in the stationary internet service quality literature (Loiacono, Goodhue
and Watson 2007).
Likewise, an important research question that requires investigation is the role of
device quality perceptions in overall perceived service quality scores. To put it differently
is/or should device quality be a part of a perceived service quality in a mobile internet
context? Device quality manifestations may pertain to technical features but also to visual
elements (feel and look of the device), and one could argue that all these influence or
enable the provision of high service quality in a mobile internet context.
CONCLUDING REMARKS
This study confirms the complexity of managing service quality perceptions and
does so by providing theoretical and empirical evidence for a) the multidimensionality
and the hierarchical nature of the construct and b) for the existence of significant
differences between countries in the relative importance assigned on certain perceived
service quality dimensions.
We believe that constructs pertaining to electronic service evaluation (e.g. service
quality, satisfaction, value)- either that be a wireline internet service or a mobile internet
service- are not that different at least in terms of factorial structure. We suggest that the
electronic service context (wireline internet and mobile internet contexts) should be better
32
treated as a moderator variable that weakens, strengthens or makes insignificant
relationships pertaining to structural relationships (i.e. relationships between evaluation
constructs). For example one could hypothesize that ease of use-though salient both in a
wireline internet and a mobile internet context-is perceived as more important in
explaining an outcome variable (i.e. intention to use) in a mobile internet context due to
the well known limitations of handheld devices and due to situational characteristics of
mobile internet services consumption (i.e., consumption of mobile internet services
compared to stationary internet services seems to be an “on the run” activity”).
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Table 1. Definitions of e-SQ Measures/Constructs
Measure Definition Relevant Studies
Ease of Use
The extent to which a person
believes that using wireless
Internet services will be free of
effort and easy to learn
Loiacono, Goodhue and
Watson(2007); Sing
(2003);Wolfinbarger and Gilly
(2003);Cox and Dale
(2002);Koivumaki (2002); Zeithaml,
Parasuraman and Malhotra
(2002);Chae et al. (2002);Yang and
Peterson (2001); Jun and Kai
(2001);Mathwick, Malhotra and
Rigdon (2001); Venkatesh and
Davis (2000)
Usefulness
The extent to which a person
believes that using wireless
Internet services will enhance his
or her performance
Zeithaml, Parasuraman and
Malhotra (2003);Chaet al.
(2002);Liu and Arnett (2000);
Venkatesh and Davis (2000)
Aesthetics
The extent to which wireless
Internet services are visually
attractive and pleasant
Loiacono, Goodhue and
Watson(2007); Chae and Kim
(2003); Zeithaml, Parasuraman and
Malhotra (2003); Swinder, Trocchia
Gwiner (2002);Jun and Kai (2001) ;
Yang and Peterson (2001)
Content
The variety of content offered
(depth & width) through wireless
Internet services
Srinivasan, Anderson and
Ponnavolu (2002);Kayanama and
Black (2001)
Privacy
The respect of personal
information shared through
wireless Internet services
Wolnfinabrager and Gilly (2003);
Zeithaml, Parasuraman and
Malhotra (2003); Koivumaki
(2002); Yang and Peterson (2001)
Customization
The ability to adapt and
personalize wireless Internet
services to individual preferences
Wolfinbarger and Gilly (2003);
Srinivasan, Anderson and
Ponnavolu (2002); Kayanama and
Black (2001)
Customer
Service
Responsive and helpful service
that responds to customer
inquiries quickly
Wolfinbarger and Gilly (2003);
Zeithaml, Parasuraman and
Malhotra (2003);Koivumaki
(2002);Reibstein (2002) ];Woo and
Fock (1999)
39
Table 2. Perceived Mobile Internet Services Quality:
Exploratory Factor Analysis, N= 4, 456
Factors Measures Loadings
F1: Efficiency
Quality
(23.2 % of
variation)
Ease of use (how easily can I learn to use the service) .66
Usefulness (how useful the service offerings are to me) .67
F2: Outcome
Quality
(15.8% of
variation)
Aesthetics (how enjoyable and visually attractive is to use
the service) .63
Width/Depth of Content (for example, number of items
available to download) .57
F3: Customer
Care Quality
(13.5% of
variation)
Privacy (minimization of personal data that I need to
disclose to the service provider) .65
Level of personalization (whether I can personalize the
service to my tastes) .66
Customer service (whether the provider is able to support
me effectively in problems I might face) .75
40
Figure 1. The Perceived Service Quality Construct
Ease of Use
Customer Care
Quality
Outcome Quality
Efficiency Quality
Service Quality
Usefulness
Aesthetics
Content Variety
Customization
Privacy
Customer
Service
γ11
γ21
γ31
λ11
λ21
λ42
λ32
λ73
λ53
λ63
41
Figure 2. Alternative Factor Structures of the Perceived Service Quality Construct
Ease of Use
Customer Care
Quality
Outcome Quality
Efficiency Quality
Usefulness
Aesthetics
Content Variety
Customization
Privacy
Customer
Service
λ11
λ21
λ42
λ32
λ73
λ53
λ63
Ease of Use
Customer Care
Quality
Outcome Quality
Efficiency Quality
Usefulness
Aesthetics
Content Variety
Customization
Privacy
Customer
Service
λ11
λ21
λ42
λ32
λ73
λ53
λ63
φ23
φ12
Φ13
Ease of Use
Usefulness
Aesthetics
Content Variety
Customization
Privacy
Customer
Service
λ11
λ21
λ41
λ31
λ71
λ51
λ61
Service Quality
Three first-order factor model (Uncorrelated) Three first-order factor model (Correlated)
First-order factor model
42
Table 3. Model Fit Criteria
9 This model was unidentified, and based on the suggestions of AMOS 5.0, two parameter loadings were
fixed to one.
Korea Japan Hong-
Kong
Cut-off
point Reference
One
first-
order
factor
model
Three factor
model
(uncorrelated)
Three factor
model
(correlated)
Second-
order
factor
model
Second-
order
factor
model
Second-
order
factor
model
Chi-square
Smaller
the
better
Schumacker
and Lomax
(2004)
694.69
(p=.00) 2444.63 (p=.00) 223.91
(p=.00)
223.91
(p=.00)
284.41
(p=.00)
148.21
(p=.00)
Df 14 16
9
11 11 11 11
CFI >0.95 Hu and
Bentler (1999) .61 .70 .97 .97 .945 .97
Delta 2 >0.90 Bagozzi and
Yi (1988) .41 .70 .97 .97 .945 .97
Standardized
RMR <0.08 Hu and
Bentler 1999) .09 .23 .03 .03 .045 .039
RMSEA <0.06 Hu and
Bentler (1999) .10 .18 .066 .066 .108 .083
43
Figure 3. Standardized Parameter Estimates (Korean Sample)
Ease of Use
Customer Care
Quality
Outcome Quality
Efficiency Quality
Usefulness
Aesthetics
Content Variety
Customization
Privacy
Customer
Service
λ11=.62
λ21=.84
λ42=.58
λ32=.74
λ73=.80
λ53=.67
λ63=.72
Service Quality
γ11=.80
γ21=.81
γ31=.71
δ1
δ7
δ6
δ5
δ4
δ3
δ2
ζ1
ζ2
ζ3
.64
.66
.50
.39
.70
.54
.33
.44
.51
.64
44
Figure 4. Standardized Parameter Estimates (Japanese Sample)
Ease of Use
Customer Care
Quality
Outcome Quality
Efficiency Quality
Usefulness
Aesthetics
Content Variety
Customization
Privacy
Customer
Service
λ11=.69
λ21=.78
λ42=.75
λ32=.94
λ73=.81
λ53=.66
λ
63=.75
Service Quality
γ11=.86
γ
21=.67
γ31=.85
δ1
δ7
δ6
δ5
δ4
δ3
δ2
ζ1
ζ2
ζ3
.73
.44
.73
.47
.60
.89
.56
.43
.56
.65
45
Figure 5. Standardized Parameter Estimates (Hong-Kong Sample)
Ease of Use
Customer Care
Quality
Outcome Quality
Efficiency Quality
Usefulness
Aesthetics
Content Variety
Customization
Privacy
Customer
Service
λ11=.78
λ21=.79
λ42=.79
λ32=.72
λ73=.73
λ53=.61
λ
63=.75
Service Quality
γ11=.87
γ
21=.75
γ31=.80
δ1
δ7
δ6
δ5
δ4
δ3
δ2
ζ1
ζ2
ζ3
.76
.57
.64
.62
.62
.52
.62
.37
.56
.53
46
Table 4. Psychometric Properties of First-Order Factors
Korea Japan Hong-Kong
Construct/Measures
Standardized
Loadings and t-
values
(ρc) AVE Standardized
Loadings and t-values (ρc) AVE Standardized
Loadings and t-values (ρc) AVE
γ11 Service Quality-
Efficiency Quality .80 (*) .87 (*) .86 (*)
γ21 Service Quality-
Outcome Quality .81 (t=20.73) .75 (t=18.07) .67 (t=17.97)
γ31 Service Quality-
Customer Quality .71 (t=19.93) .80 (t=17.49) .85 (t=16.15)
Efficiency Quality .70 .54 .76 .62 .70 .54
y1 Ease of use (how easily
can I learn to use the
service)
.62 (*) .78 (*) .69 (*)
y2 Usefulness (how useful
the service offerings are to
me).
.84 (t=28.53) .79 (t=28.60) .78 (t=22.54)
Outcome Quality .61 .44 .73 .57 .84 .72
y3 Aesthetics (how
enjoyable and visually
attractive is to use the
service).
.74 (t=25.19) .79 (t=23.45) .75 (t=25.00)
y4 Width/Depth of Content
(for example, number of
items available to
download).
.58 (*) .72 (*) ..94 (*)
Customer Care Quality .77 .53 .74 .49 .78 .55
y5 Privacy (minimization of
personal data that I need to
disclose to the service
provider)
.67 (t=37.31) .75 (t=23.82) .75 (t=25.28)
y6 Level of personalization
(whether I can personalize
the service to my tastes)
.72 (*) .61 (*) .66 (*)
y7Customer service
provided (whether the
provider is able to support
me effectively in any
problem I might have).
.80 (t=38.59) .73 (t=23.60) .81 (t=26.17)
47
Table 5. Factor Means Comparisons
Efficiency Outcome Quality Customer Care
Korea- HK Korea>HK Korea>HK Korea>HK
Japan- HK n.a. n.s. HK>Japan
Japan- Korea Japan>Korea Korea>Japan n.a.
Notes: n.a.= not applicable due to non-invariance, n.s.= not statistically significant
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