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ORIGINAL EMPIRICAL RESEARCH
The service quality-satisfa ction link revisited:
exploring asymmetries and dynamics
Tomas Falk & Maik Hammerschmidt &
Jeroen J. L. Schepers
Received: 5 June 2008 / Accepted: 25 May 2009 / Published online: 30 July 2009
#
The Author(s) 2009. This article is published with open access at Springerlink.com
Abstract This study provides deeper insight in the link
between service quality and customer satisfaction. The
traditional assumption of a linear relationship is challenged
by explor ing asymmetries and dynamics. The simultaneous
influence of service quality and customer experience on
satisfaction is examined by means of nonlinear structural
equation modeling. Results show that functional-utilitarian
quality attributes (availability, efficiency, fulfillment, and
privacy) lose their capability to delight customers as the
customer relationship matures. In contrast, hedonic quality
attributes (design, enjoyment, and image) only exhibit an
increasing effect on satisfaction for more experienced
customers. These insights are vital for service managers as
they help to improve the efficiency of quality investments.
Keywords Customer satisfaction
.
Service quality
.
Asymmetric effects
.
Customer experi ence
.
Nonlinear structural equation modeling
Introduction
While researchers generally agree that favorable service
quality perceptions lead to improved satisfaction, in
practice many quality initiatives fail to deliver anticipated
results (Anderson and Mittal 2000). Such disappointing
outcomes have undermined service marketing’s credibility
and periled quality management as a distinct capability of
firms (Rust and Chung 2006). With service quality being
the fundament of the satisfaction-profit chain, firms have to
solidify their unde rstanding of this const ruct and its
consequences. This is a necessary first step for assuring
profitable outcomes of service efforts. Therefore, the call
for developing a deeper comprehension of servic e quality’s
influence on customer satisfaction represents a hotbed for
both business practitioners and marketing scholars (Anderson
2006).
Assuming a linear relationship between service quality
and sati sfaction, many service providers continuously add
new features to improve their offerings (Thompson et al.
2005). However, recent findings suggest that this relation-
ship may be nonlinear and thus quality improvements may
have an asymmetric effect on satisfaction. That is, the
negative effect of a one-unit decrease of service quality on
overall satisfaction could be greater than the positive effect
of a corresponding unit of increase or vice versa (Anderson
and Mittal 2000; Mittal et al. 1998). In addition, service
managers have to be aware that customer relationships
unfold over time (Bolton and Lemon 1999). Quality
attributes that impact the satisfaction of newly acquired
customers may have little influence on the satisfaction of
long-term clients. Understanding such dynamics in customer
judgments is an important prerequisite for successfully
addressing customer needs across the stages of a relation-
ship (Rust et al. 1999). Research even calls for developing
separate strategies for newly acquired and more established
customers (Mittal and Katrichis 2000). Surprisingly, while
the quality-satisfaction relationship is both nonlinea r and
dynamic, these issues have been studied in isolation. Yet, as
J. of the Acad. Mark. Sci. (2010) 38:288–302
DOI 10.1007/s11747-009-0152-2
T. Falk
:
M. Hammerschmidt (*)
University of Mannheim,
L 5, 1, 68131 Mannheim, Germany
e-mail: maik.hammerschmidt@bwl.uni-mannheim.de
T. Falk
e-mail: tomas.falk@bwl.uni-mannheim.de
J. J. L. Schepers
Eindhoven University of Technology,
Den Dolech 2, Pav. L 07, Postbus 513, 5600 MB Eindhoven,
Netherlands
e-mail: j.j.l.schepers@tue.nl
also called for by Slotegraaf and Inman (2004), an
integrative perspective is needed in order to prevent serious
resource misallocation. Taking an integrated view is
necessary as service providers improving their service
quality may not do a good job if they continue to invest
in quality factors that show diminishing returns to quality
(i.e., negative asymmetr ies) for lo ng-term customer s.
Hence, it is important to identify attributes that augment
satisfaction at an increasing rate as a customer relationship
progresses (i.e., that exhibit positive asymmetries).
This knowledge is pivotal for e-service provi ders given
their recent experiences that not all investments in website
features are equally reflected in customer behavior benefi-
cial to the firm (Thompson et al. 2005). On the Internet,
both effort and funds are invested in a wide array of
customization, privacy and community-related features, but
the key issue is whether these website attributes really
continuously improve custo mer evaluations. For instance,
Cheung and Lee (2005) identify positive and negative
asymmetric effects of website attribute performance on
customer satisfaction. Yet, they find some effects opposite
to the ir hypothesized directio n and in general report
inconclusive results. These authors therefore call to inves-
tigate the conditions under which asymmetries have a
positive or negative valence. Technology adoption literature
suggests that time may be such a condition, as customer
technology perceptions vary with experience (Venkatesh et
al. 2003). Considering the active role and broad integration
of customers as part-time employees in the production of e-
services (Rust and Kannan 2002), such experience-related
effects should occur especially for services on the Internet.
Therefore, as a first contribution this paper takes an
integrative perspective in studying asymmetries and dy-
namics in an e-service context. More precisely, we inves-
tigate whether improvements of service quality attributes
enhance satisfaction at increasing or decreasing rates as a
customer relationship progresses.
The type of service quality attribute reflects another
condition that might influence the valence of asymmetries.
Recent studies stress the importance of hedonic service
attributes like enjoyment as drivers of satisfaction in both
offline and online environments (Chitturi et al. 2008;Van
Dolen et al. 2007). Indeed, with the increasing convergence
of functional-utilitarian service attributes such as fast-
loading webpages, the impact of hedonic elements could
be significant (Dhar and Wertenbroch 2000). Therefore,
as a second contribution, we compare the impact of
functional-utilitarian quality attributes to the impact of
hedonic quality attributes on customer satisfaction. More
specifically, we expect functional-utilitarian attributes to
evolve in a way that their ability to trigger dispropor-
tionally high levels of customer satisfaction (i.e., cus-
tomer delight) fades over time, while hedonic factors
reveal their delight-creating potential only in later stages
of a relationship.
Third, previous studies considering asymmetries have
applied a dummy-variable regression method ology
(Cheung and Lee 2005; Matzler et al. 2004). Here, dummy
variables are created based on median or quartile splits of
the data. Yet, this methodological approac h focuses on the
extremes rather then the entire spectrum of possible values
causing a loss of information concerning the independent
variables. As a response to this problem, we take a more
robust approach by applying nonlinear structural equation
modeling. More specifically, we test for latent quadratic
effects of functional-utilitarian and hedonic e -service
quality on customer satisfaction (Marsh et al. 2004).
We structure this paper as foll ows. We first establish our
conceptual framework and formulate our hypotheses by
deriv ing important insights from relevant theories and
literature. We then exa mine the nature of the service
quality-satisfaction relationship based on quantitative data
for two electronic service settings. We conclude wi th
theoretical and practical implic ations.
Conceptual framework
Symmetric versus asymmetric effects of quality
Traditionally, research on customer satisfaction has used the
expectancy-disconfirmation paradigm (Oliver et al. 1997),
where satisfaction is the discrepancy between expectations
and perceived quality (i.e., performance). Expectations act
as a reference point. Performance outcomes poorer than
expected are rated below the reference point and lead to
dissatisfaction (negative disconfirmation). Performance out-
comes can also meet the reference point (confirmation)or
even exceed it (positive disconfirmation), both leading to
satisfaction. A shortcoming of the disconfirmation para-
digm is that while exceeding or falling below a base point
will affect the satisfaction judgment in different directions,
the effect sizes of these performance deviations are
assumed to be identical. As a consequence, symmetric
effects of quality on customer satisfaction would have to be
expected. The idea of symm etric effects is challenged by
the model of Kano et al. (1984) identifying three types of
quality attributes: (1) basic attributes, (2 ) performance
attributes, and (3) excitement attributes.
The presence of attributes falling in the first category has
only a minor satisfaction enhancing impact, but their
absence or a poor performance in these areas exhibits a
strong negative influence on customer satisfaction. These
attributes can be regarded as having a negative asymmetry
(Anderson and Mittal 2000). That is, a one-unit decrease in
the level of quality has a larger influence on satisfaction
J. of the Acad. Mark. Sci. (2010) 38:288–302 289
compared to a one-unit incre ase for the same attribute. The
second category depicts the traditional view of attributes
exhibiting symmetric effects on satisfaction: A one-unit
decrease in attribute performance has an equivalent impact
on satisfaction as a one-unit increase. Third, excitement
attributes are not explicitly articulated by the customer and
can result in high levels of customer satisfaction. Yet, their
absence does not lead to dissatisfaction. They can therefore
be portrayed as exhibiting a positive asymmetry, so that a
one-unit increase in quality has a larger influence on
satisfaction compared to a one-unit decrease in quality for
the same attribute.
Theoretical underpinnings of static asymmetries
The theoretical logic for the existence of negative asymme-
tries lies in prospect theory which suggests that people
judge new options with a degree of reference dependence
and loss aversion (Kahneman and Tversky 1979). Gains or
losses result from a comparison to a reference point;
outcomes above this point are regarded as gains, outcomes
below this point are treated as losses. Loss aversion means
that a one-unit loss is weighted more than an equal amount
of gain. With satisfaction judgments being reference
dependent too (Homburg et al. 2005), prospect theory
proposes that a one-unit decrease in attribute performance
has a larger imp act on overall satisfaction than an equal
amount of performance increase in the same attribute.
Positive asymmetries on the other hand, find their roots
in customer delight theory (Oliver et al. 1997). Customer
delight is “a profoundly positive emotional state generally
resulting from having one’s expectations exceeded to a
surprising degree” (Rust and Oliver 2000, p. 86). Factors
are delight-creating if no generally accepted standard of
performance exists. They have no downside and an
unlimited upside. Previous studies have applied these
underpinnings to the evaluation of asymmetric effects in
the relationship between service quality and customer
satisfaction in offline (Mittal et al. 1998) as well as online
contexts (Cheung and Lee 2005; Zhang and Von Dran
2001). Nevertheless, these studies examine asymmetries
only from a static point of view. The theories underlying
static asymmetries should be expanded to make predictions
on how asymmetries evolve over time. In the following
section, we develop an integrative framework on the
dynamics of asymmetries.
Theoretical underpinnings of dynamic asymmetries
The reference point customers use to assess satisfaction is
primarily shaped by prior usage of the service (Parasuraman
et al. 1991). Hence, quality expectations are influenced by
experience and therefore they are likely to change over
time. Empirically, Mittal et al. (1999) and Slotegraaf and
Inman (2004) provide evidence that product attribute
weights for forming customer satisfaction change over time
for automotive customers. Mittal et al. (2001 ) show that for
financial and educational services, different service features
contribute differentially to dynamic consumers’ consump-
tion preferences. Anderson et al. (2008) find that in the
airline industry, experts place more emphasis on physical
amenities asso ciated with air travel compared to core
service performance.
1
Finally, Dagger and Sweeney
(2007) show that for healthcare services, tangible elements
of a service are more important to inexperienced customers
whereas provider expertise and professionalism are more
important to experienced users. Yet, the described dynamics
concern linear quality-satisfaction relationships and thus
symmetric effects, while we examine how asymmetries
evolve over the relationship cycle.
As discussed earlier, the basis for a positive asymmetry
lies in customer delight attributes. However, with increasing
knowledge on how a service provider performs, the element
of surprise fades. Consequently, service attributes formerly
recognized as new, interesting, and challenging, lose their
ability to trigger customer delight. This is accompanied by
rising expectations, because expectations are known to
track performance evaluations (Rust and Oliver 2000). As
the customer cognitively develops minimum standards with
regard to the attribute’s performance, the attribute will no
longer display an unlimited upside. Negative effects on
satisfaction will emerge if the attr ibute displays low quality
levels. Therefore, the initial positive asymmetry has con-
verted into a symmetric effect that mirrors a performance
attribute in the Kano model. After this institutionalization,
an attribute can spread to become a market standard by
means of word-of- mouth and vicarious experience (File et
al. 1994), but also by technological advances. For exa mple,
in the late nineties, Internet service providers (ISPs) could
gain competitive advantage by offering high connection
speeds. Yet, due to the fierce competition among ISPs, high
speed Internet has rapidly become a market standard. Now,
barring some exceptions,
2
ISPs offering low speed Internet
like dial-up connections no longer exist. Hence, after the
delight element has faded and symmetric effects are observed,
the attribute further develops into a negative asymmetry: Its
presence adds little to overall customer satisfaction, but when
absent, substantial dissatisfaction arises.
1
We thank an anonymous reviewer for this suggestion.
2
E.g., NetZero.com occupies a niche position by offering US-wide
dial-up connections to occasional Internet users. However, recognizing
the current market standards, fast DSL connections are also on offer. We
thank an anonymous reviewer for pointing us to this example.
290 J. of the Acad. Mark. Sci. (2010) 38:288–302
In sum, over the course of time, customer delight
principles would predict that the effects of quality change
their valence from a positive asymmetry to a negative
asymmetry. This is consistent with empirical insights on
both product-related and service-related attributes (Nilsson-
Witell and Fundin 2005). However, the “wear-out process”
suggested by delight theory might not hold for all types of
quality attributes. In line with this, customer satisfaction
formation has shown to be primarily based on cognition in
the early stages of a customer relationship. In contrast,
affect dominates customer satisfaction judgments as clients
build a stable relationship towards a provider (Johnson et
al. 2006). Accordingly, we propose that asymmetries evolve
in different patterns, depending on the subset of quality
attributes considered.
Two subsets of attributes: functional-utilitarian and hedonic
Consistent with theoretical insights from diverse disci-
plines, we make the distinction between two subsets of
quality attributes in our e-services context: functional-
utilitarian and hedonic service quali ty attributes. Voss et
al. (2003, p. 310, emphasis added) state that “investigation
of hedonic and utilitarian components has been suggested
in such diverse disciplines as sociology, psychology, and
economics.” In marketing, a series of articles report similar
observations (Dhar and Wertenbroch 2000; Okada 2005).
These studies show that the concept ual distinction between
a functional-utilitarian and a hedonic attribute subset is
essential as both subsets reflect different consumption needs.
As such, functional-utilitarian attributes are thought to
provide “instrumental and practical benefits” whereas
hedonic components provide “aesthetic, experiental, and
enjoyment-related benefits” (Chitturi et al. 2008, p. 49).
In order to investigate how the quality effects for both
types of attributes evolve, we draw on Maslow’s hierarchy
of needs framework which states that most basic human
needs must be satisfied before higher-level human needs are
pursued (Maslow 1954). Translated to an e-services
context, functional-utilitarian attributes are more fundamen-
tal than hedonic attributes (Valacich et al. 2007). Since
inexperienced customers are earlier in the consumption life
cycle, they focus more on satisfying fundamental, func-
tional needs. In contr ast, experts take the fulfillments of such
needs for granted and are more concerned with higher level,
hedonic needs. Thus, we suggest a sequential importance of
both subsets in a way that a website first has to satisfy the
needs of structural firmness (e.g., response time, security)
and func tional convenience (e.g., ease of use), before
hedonic elements are considered (e.g., appealing layout
and design).
Previous work seems to support the proposed sequential
patterns. In a more general product setting, Chitturi et al.
(2008) show that consumers attach greater weight to
hedonic dimensions only after a necessary level of func-
tionality is obtained. With respect to e-services, Childers
et al. (2001) find that enjoying aspects of the service
encounter (“shoppi ng as fun”) are less important for first-
time customers of online shopping than for experienced
ones. Moreover, recent studies have identified instrumental
motives as the primary determinants for initially choosing
an online service channel as they fulfill economic needs for
adopting e-services (Parasuraman et al. 2005; Valacich et al.
2007
). In view of this , we suggest that individuals first
target their cognitive efforts towards functional-utilitarian
attributes and only later they divert their attention to the
consideration of hedoni c elements.
In sum, we expect that functional-utilitarian and hedonic
service quality attributes display a different potential to
exhibit positive asymmetries (i.e., to create customer
delight) across the customer relationship cycle. On the
one hand, functional-utilitarian attributes evolve in a way
that their ability to exhibit positive asym metries fades over
time. In other words, the likelihood of developing negative
asymmetries increases as the relationship matures. On the
other hand, hedonic factors reveal their ability to exhibit
positive asymmetries only for more experienced users. In
other words, the likelihood of developing positive asym-
metries increases. Hence, we hypothesize:
Hypothesis 1. As consumers become more experienced,
functional-utilitarian e-service quality attri-
butes have a greater potential to exhibit
negative asymmetric effects on satisfaction.
Hypothesis 2. As consumers become more experienced,
hedonic e-service quality attributes have a
greater potential to exhibit positive asym-
metric effects on satisfaction.
Research and study design
Research setting
In order to improve the generalizability of our findings, we
collected data in two e-service settings. First, a German
market research institute distributed a questionnaire to
randomly selected members of its database. Following the
procedure suggested by Parasuraman et al. (2005), partici-
pants were asked to recall a recently used online shop and
refer to that experience regarding their answers. Among the
online shops most commonly referred to were typical
Internet retailers such as Amazon.com (30%) and Bol.com
(14%). The se shops offer a multitude of products including
books, electronics, household appliances and clothing.
Second, users of one of the largest Internet portal sites in
J. of the Acad. Mark. Sci. (2010) 38:288–302 291
Germany were targeted in close cooperation with t he
respective provider. An online questionnaire was presented
to randomly selected users of the price and product
comparison function of the respective portal. Portal visitors
could participate in the survey by clicking on a flash
banner. While we make a conceptual distinction between
functional and hedonic elements of an e-service in our
paper, we do not treat them as mutually exclusive. Hence,
both services considered in our study are bundles of
hedonic and functional characteristics.
Since the data for this study were collected from
customers only, we tried to minimize common method bias
a priori by evaluating response styles (Weijters et al. 2008).
We integrated two pairs of reversed items: “I knew exactly
what I would buy beforehand” / “I decided upon what to
buy while I was shopping” (online shop), and “I knew
exactly what my product of interest would cost” / “I learned
about the price of my product of interest while I was
comparing products” (Internet portal). This allowed us to
identify acquiescent respondents (Weijters et al. 2008).
Specifically, 15 respondents scored high on both statements
in the online shop sample and 18 in the Internet portal
sample. These respondents were deemed to respond in an
inconsistent and acquiescent way and were not included in
the analyses. Resulting from this procedure, we obtained a
final sample size of 456 online shoppers and 558 portal
users. The charact eristics of respondents are reported in
Table 1.
Measurement
The assessment of the functional-utilitarian attributes of e-
service quality was conducted following the E-S-QUAL
operationalization put forward by Parasuraman et al.
(2005). They distinguish four dimensions of e-service
quality. System availability denotes the correct technical
functioning of the website and is tapped by 3 items.
Efficiency denotes the ease and speed of accessing and
using the website, and is measured with 3 items. Fulfillment
is defined as the extent to which the website’s promises
about order delivery and item availability are fulfilled.
However, respondents were only able to order items in the
online shop, not on the Internet portal. As the information
presented should be accurate and complete, fulfillment
indicates whether the information pres en ted here wa s
perceived to be accurate and helpful for online shopping
activities. Fulfillment is assessed using 3 items. These items
were tailored to the specific context of the online shop and
Internet portal respectively. Finally, privacy reflects the
degree to which the website is safe and protects customer
information, and is measured with 3 items.
To decide on attributes for capturing the hedonic aspects
of e-service quality, we studied existing scales. Wolfinbarger
and Gilly (2003), Parasuraman et al. (2005), and Bauer
et al. ( 2006) all point to the importance of website design
denoting the visual appeal of the virtual interface. We
therefore consider this hedoni c factor and measure it with 3
items, adapted from Wolfinbarger and Gilly (2003). In
addition, in affecting individual’s attitudes towards tech-
nology, literature has repeatedly stressed the importance of
enjoyment. This dimension captures the entertaining aspect
of using the Internet as a service channel and is assessed
with 3 items (Childers et al. 2001). Apart from that, studies
emphasize the role of image, reflecting the degree to which
the use of an innova tion is perceived to enhance one’s status
in the social system (Venkatesh and Davis 2000). Image was
measured with 3 items derived from Venkatesh and Davis
(2000). Overall customer satisfaction was assessed using 3
items suggested by Szymanski and Hise (2000)and
Hennig-Thurau et al. (2002). Apart from items adapted
from Szymanski and Hise (2000) which were measured
based on two semantic differentials (very dissatisfied-very
satisfied and very displeased-very pleased), participants
indicated their (dis)agreement with a statement using a 7-
point Likert scale that ranged from strongly disagree to
strongly agree. Finally, customer experience was surveyed
by asking about the time period the particular online shop
or Internet portal has been used. A complete list of all items
and their psychometric properties appears in the Appendix.
Our philosophy for capturing both functional-utilitarian
and hedonic aspects of e-service quality is based on the
Table 1 Sample characteristics
Variable Sample 1 Sample 2
Online shoppers Internet portal users
n=456 n=558
Age (years)
Mean 34.6 35.0
Standard deviation 12.6 13.6
Gender (percentage)
Female 45.0 52.0
Male 55.0 48.0
Net household income (percentage)
Less than 25.000 Euros 47.0 47.8
25.000– 49.999 Euros 32.2 34.7
50.000– 75.000 Euros 14.3 11.9
More than 75.000 Euros 6.5 5.6
Customer experience (percentage)
Less than 6 months 5.7 6.2
6 months to 2 years 15.6 13.9
2–3 years 28.0 25.1
3–4 years 14.1 15.8
More than 4 years 36.6 39.0
292 J. of the Acad. Mark. Sci. (2010) 38:288–302
item-parceling approach that has been suggested as an
appropriate way to reduce the complexity of const ructs
measured through a large numbe r of indicators (Bagozzi
and Edwards 1998; Little et al. 2002). A parcel represents
an aggrega te-level indicator comprised of the average of
two or more items. In line with the existing literature, we
conceptualize e-service quality as a multidimensional
construct of higher order (Brady and Cronin 2001;
Fassnacht and Koese 2006). Therefore, we construct
domain-representative parcels. This approach “accounts
for multidimensionality by creating parcels that encompass
not only the common variance (…), but also the reliable
unique facets of multiple dimensions” (Little et al. 2002,
p. 167). In particular, domain-representative parcels are
created by joining and averaging items from different
dimensions. The operationalization and validation of the
domain-representative parcel s will be described in more
detail in the results section.
Nonlinear structural equation model ing
In line with Mittal et al. ( 1998) and Agustin and Singh
(2005), asymmet ry inherently implie s nonlinearity in the
functional relationship between service quality and satis-
faction. Tha t is, a negative asymmetry involves a function
with diminishing returns to quality while a positive
asymmetry involves increasing returns to quality. Literature
suggests exploring decreasing or increasing returns to scale
by introducing squared terms in the regression equations
(Mittal et al. 1998). Given the advantages of stru ctural
equation modeling (SEM), we set out to test the hypothe-
sized asymmetric effects by means of a nonlinear SEM
approach including latent quadratic variables (Kenny and
Judd 1984; Ping 1996). While several approaches have
been suggested for testing stru ctural equation models with
latent qua dratic terms (e.g., Ping 1996; Marsh et al. 2004),
nonlinear effects are rarely implemented because of the
inherent problems in the model specification a nd the
technical complexity of analysis.
We apply the unconstrained approach proposed by
Marsh et al. (2004) as it offers important advantages such
as smaller bias and robustness regarding violations of
multivariate normality assumptions. According to this
approach, latent interaction terms are added to the base
model. These terms are specified based on quadratics of
the indicator s for the latent variables in question. In
contrast to the constrained approach, factor loadings and
measurement error varia nces are allowed to be estimated
freely rather than constrained (Jaiswal and Niraj 2007). In
addition, all indicator variables as well as customer
experience as the moderating variable are mean-centered
to reduce multicollinearity and to augment informational
value of significance tests (Algina and Moulder 2001). The
square of the centered indicators is used to define the latent
quadratic term. For example, if ξ
1
represents a latent
construct and x
1
and x
2
are its indicators, then the terms
x
2
1
and x
2
2
are specified as the indicators of the latent
quadratic factor x
2
1
. In particular, the indicator loading l
x
2
1
and measurement error q
x
2
1
for the indicator x
1
of the
quadratic f actor are freely estimated in the fo llowing
equations where d
x
2
1
represents the error term:
x
2
1
¼ l
x
2
1
x
2
1
þ d
x
2
1
ð1Þ
q
x
2
1
¼ var d
x
2
1
ð2Þ
We model functional-utilitarian and hedonic e-service
quality as reflective first-order constructs encompassing
three domain-represent ative item parcels each. The respec-
tive quadratic factors are represented by building parcels of
the mean-centered squared items (Little et al. 2002).
Moreover, we include the i nteraction effect between
functional and hedonic e-service quality. Not simultaneous-
ly considering the quadratic terms and the interaction of the
underlying linear terms may entail misleading conclusions
(Ganzach 1997). We constructed the described interaction
effect by using the products of the first item parcels, second
item parcels and third item parcels of the two e-service
quality constructs as indicators of the latent interaction
variable (Ping 1996).
Finally, customer experience is hypothesized to alter the
nonlinear relationship between our independent variables
and customer satisfaction. To model this effect, we
introduce two nonredundant components in the structural
equation model (Cohen et al. 2003, Luo and Donthu 2006):
(1) a lower-order term representing the linear by linear
interaction and (2) a higher-order term representing the
nonlinear (quadratic) by linear interaction.
3
The nonlinear
by linear interaction can only be tested if all the other terms
(including the linear by linear interactions) are integrated in
the model represented by equation (3). To sum up, we use
the following equation for testing the asymmetric effects of
e-service quality on customer satisfaction
SAT ¼ a þ b
1
FQ þ g
1
FQ
2
þ b
2
HQ
þg
2
HQ
2
þ g
3
FQ
*
EXP þ g
4
FQ
2
*
EXP
þg
5
HQ
*
EXP þ g
6
HQ
2
*
EXP þ g
7
FQ
*
HQ þ z
ð3Þ
3
We thank an anonymous reviewer for the suggestion to test for
nonlinear by linear interaction effects.
J. of the Acad. Mark. Sci. (2010) 38:288–302 293
In equation (3), SAT represents customer satisfaction, α
represents the constant term, β
i
(i=1,2) are the linear
effects, γ
i
(i=1,…,7) are the quadratic and interaction
effects respectively, FQ reflects functional-utilitarian and
HQ represents hed onic e-service quality, FQ
2
and HQ
2
are
the corresponding quadratic constructs, EXP represents
customer experience and ζ indicates the error term.
Results
Data pooling
A general problem involved in applying nonlinear SEM is
the nonnormal distribution of the quadratic term of an
indicator even in the case of normally distrib uted raw data
(Marsh et al. 2004). As a consequence, the distributional
assumptions of maximum likelihood (ML) estimation are
violated. This is especially the case for smaller to medium
sample sizes increasing the likelihood of type II-errors when
testing relationships between the constructs (Schermelleh-
Engel et al. 1998). In order to minimize type II-errors, we
tested for pooling the data obtained in both empirical
settings (De Wulf et al. 2001). To decide whether we are
able to pool data across service settings, we test for
measurement invariance. In other words, we test whether
the relation between indicators and latent variables is the
same across both samples (Steenkamp and Baumgartner
1998). We apply mul tigroup analysis to compare the fit of
two nested measurement models: (1) a free model in which
item loadings are freely estimated across both samples, and
(2) an equ al mode l in which two item loadi ngs per
construct are set equal across the two samples. The models
include all first-order constructs, i.e., the four functional-
utilitarian quality co nstructs, the three hedonic quality
constructs and c ustomer satisfaction (Ping 2 004). In
assessing the differences in fit, the χ²-difference test is
performed first. We find a si gnificant χ²-deterioration
(p<.001) between the free model and the equal model
(χ²
free
(448)=1281.3 and χ ²
equal
(464)=1321.6). However, no
significant deterioration is found in alternative fit measures
(CFI
free
=.96, CFI
equal
=.96; TLI
free
=.95, TLI
equal
=.95;
RMSEA
free
=.043, RMSEA
equal
=.043). In view of this, we
further evaluate the modification indices (MIs) of the
constrained parameters, looking for MIs that are large and
exceptional (Cheung and Rensvold 2002; Steenkamp and
Baumgartner 1998). Based on an investigation of the
indices of local misfit, the loadings of two items with the
highest MIs are released. In particular, two items (FUL2
and FUL3) for assessing fulfillment are set free. This
seems appropriate as these items were adapted to the
specific online shopping and Internet portal context.
Compared to the free model, the resulting partial invariant
model shows no significant or substantial deterioration in
fit and partial metric invariance is therefore accepted
(χ²
partial
(462)=1299.6; CFI
partial
=.96, TLI
partial
=.95,
RMSEA
partial
=.043). Consequently, we decide to pool the
data. Finally, and in line with Parasuraman et al. (2005),
pooling of the data is also suitable from a conceptual
perspective in order to produce results that can be
generalized across a variety of electronic services.
Second-order model
To reassure multidimensionality of our e-service quality
meas ure and the construction of domain-r epresentative
parcels, we follow the procedure conducted by Voss et al.
(2003). They develop a two-dimensional scale for capturing
functional-utilitarian and hedonic aspects o f consumer
attitudes. Accordingly, we first test whether the seven e-
service quality dimensions are reflective of the two
correlated higher-order constructs, namely func tional-
utilitarian and hedonic e-service quality. Calculating a
second-order m odel lends support to our contention
(χ²(181)=930.7, CFI=.96, TLI=.95, RMS EA=.064). The
second-order factor loadings are significantly large and
positive, ranging from .80 to .92 (p<.001) for functional-
utilitarian quality and from .63 to .84 (p<.001) for hedonic
quality. Furthermore, the correlation between the two e-
service quality constructs is substantive and significant
(standardized ϕ=.65, p<.001). Next, we carefully examine
discriminant validity between the two second-order
constructs by applying the Fornell and Larcker (1981) test.
This test requires the average variance extracted (AVE) of
each construct to exceed the squared correlation shared
between the latent constructs. The AVE estimates are .91
for functional-utilitarian quality and .89 for hedonic quality
both exceeding squared correlation between the constructs
of .42. Finally, we compare two models where the corre-
lation between functional-utilitarian and hedonic quality
is freely estimated in the first, and constrained to unity in
the second model (Anderson and Gerbing 1988). The
χ²-difference test is significant (Δχ²(1)=6.1, p<.05) and
suggests that the correlation between both constructs
significantly differs from 1. In sum, functional-utilitarian
and hedonic e-servic e quality are distinct and capture
different information, but are tied to a common higher-
order construct.
Validity and reliability of the domain-representative parcels
After having established e-service quality as a multidimen-
sional higher-order construct, we are able to build the domain-
representative parcels. The first parcel representing
294 J. of the Acad. Mark. Sci. (2010) 38:288–302
functional-utilitarian e-service quality consists of the
averaged sum of the first items from each of the four
dimensions (i.e., system availability, efficiency, fulfillment,
and privacy). The second parcel includes the averaged
sum of the second items from each of the four
dimensions. Finally, the third parcel encompasses the
averaged sum of the third items from each of the four
dimensions. A similar logic was applied with respect to
hedonic e-service quality. In this manner, each parcel
reflects all of the dimensions inherent within the set of
indicators. As a result, we obtain two factors captured by
three item parcels each.
To test the validity and reliability of the parcel-based
measures, we use confirmatory factor analyses. Following
Homburg et al. (2008), item reliabilities and construct
reliabilities are asse ssed based o n a full meas urement
model. Overall, the results point to desirable psychometric
properties of our measures. In particular, individual item
reliabilities are well above the suggested minimum value
of .50 (Anderson and Gerbing 1988). Additionally, the
composite reliability values are also well above the suggested
minimum value of .70 (Nunnally 1978). The AVE for each
construct exceeds the t hreshold value of .50 (Baumgartner
and Homburg 1996). Finally, overall fit measures display
a good fit of the applied measurement models. The
details o f our analyses are reported in Table 2.
In line with Ping (2004), we regard the measurement of
the quadratic latent constructs as reliable and valid as the
underlying e-service quality constructs show high levels of
reliability and validity themselves.
Test of hypotheses
We tested our hypotheses by estimating equation (3) with
the unconstrained approach in a structural equation model
using the maximum likelihood estimation procedure (Marsh
et al. 2004 ). The fit statistics point to an acceptable fit of the
structural model with the empirical data (χ²(360)=1760.4,
CFI=.94, TLI=.93, RMSEA =.062) and correspond to
values reported in similar approaches (e.g., Agustin and
Singh 2005).
4
Overall, our model explains 72% of the
variance in customer satisfaction.
To test our hypotheses, we interpret the path coefficients
of the structural model, given the presence of all other
terms in the equation (Cohen et al. 2003). As a first
observation, β
1
and γ
1
are both significant and point to an
asymmetric effect of functional-utilitarian quality on satis-
faction. As both path coefficients are positive (β
1
=.65,
p<.001 and γ
1
=.15, p<.05), the main effect (i.e., without
considering customer experience interaction) for functional-
utilitarian qualit y on s atisfacti on represents a posi tive
asymmetry. Additionally, for hedonic e-service quality the
linear parameter estimate is significant a nd positive
(β
2
=.29, p<.001) while the quadratic parameter estimate
is significant and negative (γ
2
=−.22, p<.01). Hence, the
main effect of hedonic quality on satisfaction can be
typified as negative asymmetric. Note that the inclusion
of the interaction between both quality attributes assures
the correct interpretation of our findings (Table 3).
Hypothesis 1 proposes decreasing returns to quality of
functional-utilitarian website attributes as customer expe-
rience increases. Accordingly, the linear by linear
interaction between quality and experience should be
significantly positive while the nonlinear by linear
interaction between the squared quality construct and
experience is significantly negative (Cohen et al. 2003).
We find exactly this pattern in our data (γ
3
=.20, p<.05;
γ
4
=−.22, p<.05). As the quadratic parameter estimate for
functional-utilitarian quality is positive while the estimate
for the interaction between the quadratic construct and
experience is negative, the positive asymmetry shifts to a
negative asymmetry with enhancing experience. Hence,
hypothesis 1 is supported.
Hypothesis 2 posits increasing returns to quality of
hedonic website features as customer experience increases.
Thus, in order to support this hypothesis, the linear
interaction between quality and experience as well as the
nonlinear by linear interaction between the squared quality
construct and experience have to be significantly positive
(Cohen et al. 2003). Indeed, both interaction effects turn out
to be significant and positive (γ
5
=.16, p<.05 and γ
6
=.15,
p<.05). For hedonic quality, the negative asymmetry
converts into positive asymmetry for long-term customers
as the quadratic parameter estimate is negative while the
estimate for the interaction between the quadratic con-
struct and experience is positive. That is, high levels of
experience appear to overcome the decreas e in satisfaction
at high hedonic quality. Thus, hypothesis 2 is supported.
Figure 1 depicts the results of our analyses.
Conclusion
This paper set out to provide deeper insight in the
fundament of the satisfaction-profit chain by exploring the
influence of service quality on satisfaction from an
asymmetric and dynamic perspective. Building on customer
delight theory and a hierarchy of needs framework, we
4
The standardized first-order (second-order) factor loadings of the
model constructs are all significant (p<.001) and range from .93 to .96
(from .88 to .95) for functional-utilitarian e-service quality and from
.91 to .95 (from .79 to .96) for hedonic e-service quality.
J. of the Acad. Mark. Sci. (2010) 38:288–302 295
developed two hypotheses in order to compare the role of
functional-utilitarian and hedonic quality in affecting
satisfaction for customers varying in experience with an
e-service. As an important first insight, our study shows
that the nature of the quality-satisfaction link is nonlinear
and asymmetric. Testing our hypotheses using a nonlinear
SEM approach including latent quadratic terms, we detect
positive asymmetric main effects of functional-utilitarian
website featur es on c ustomer satisfaction. In contrast,
hedonic quality characteristics exhibit negative asymmetric
effects on customer satisfaction. These results extend
previous findings in offline and online contexts. For
instance, Mittal et al. (1998) demonstrate only negative
asymmetric effects of attribute performance on customer
Table 3 Relationships between e-service quality and customer satisfaction
Independent variable Dependent variable: customer satisfaction
Coefficient
a
t-value Conclusion Hypotheses
test
Functional-utilitarian e-service quality .65 8.55***
Functional-utilitarian e-service quality
2
.15 2.23* Positive asymmetric effect
Functional-utilitarian e-service quality x experience .20 2.04*
Functional-utilitarian e-service quality
2
x experience −.22 −2.16* Negative asymmetric effect as
customer experience increases
H1 supported
Hedonic e-service quality .29 4.22***
Hedonic e-service quality
2
−.22 −2.96** Negative asymmetric effect
Hedonic e-service quality x experience .16 1.99*
Hedonic e-service quality
2
x experience .15 1.96* Positive asymmetric effect as
customer experience increases
H2 supported
Functional-utilitarian x hedonic e-service quality .09 1.51
n.s.
Meaningful interpretation of the
quadratic terms assured
Overall fit measures: χ²(360)=1760.4, CFI=.94, TLI=.93, RMSEA=.062
*p<.05, **p<.01, ***p<.001, n.s.=not significant
a
Standardized estimates
Table 2 Measurement model results for the latent constructs
Construct
Item
Mean (SD) Item reliability Composite reliability Average variance extracted
Functional-utilitarian e-service quality .97 .91
FQ1 (SAV1+EFF1+FUL1+PRIV1) 5.38 (1.29) .94
FQ2 (SAV2+EFF2+FUL2+PRIV2) 5.36 (1.29) .92
FQ3 (SAV3+EFF3+FUL3+PRIV3) 5.28 (1.30) .89
Hedonic e-service quality .96 .89
HQ1 (WEBDES1+ENJOY1+IMAG1) 5.17 (1.38) .94
HQ2 (WEBDES2+ENJOY2+IMAG2) 4.92 (1.25) .84
HQ3 (WEBDES3+ENJOY3+IMAG3) 4.93 (1.27) .91
Customer satisfaction .93 .82
SAT1 5.30 (1.60) .83
SAT2 5.05 (1.75) .71
SAT3 5.26 (1.56) .77
Overall fit measures: χ
2
(24)=69.3, CFI=.99, TLI=.99, RMSEA=.045
See appendix for item wordings matching the abbreviations.
296 J. of the Acad. Mark. Sci. (2010) 38:288–302
satisfaction with medical services. Cheung and Lee (2005)
find both negative and positive asymmetric effect of web
site attribute performance on satisfaction, but do not consider
hedonic attributes.
Second, the identified asymmetries in the quality-
satisfaction link shift over time. Regarding the initial
positive asymmetry for functional-utilitarian attributes, we
witness a significant decline in their potential to spark
high levels of satisfaction as c ustomer experience
increases. Figure 2, Panel A plots the described relationship
between functional-utilitarian quality, customer experience
and satisfaction. Interpreting the plot in Fig. 2,PanelA,
satisfaction returns to functional-utilitarian quality rise with
increasing experience to a point. Then, with higher experi-
ence, the impact of functional quality on customer satisfac-
tion is incrementally lowered. Put differently, for customers
low in experience, we observe increasing returns for
functional quality, while this relationship turns into one
with decreasing returns to scale for experienced customers.
Therefore, the graph of the satisfaction function is S-shaped
with a turning point at which the asymmetry changes
valence from positive to negative.
Consequently, for inexperienced customers, service pro-
vision should be convenient, accurate, and reliable. For
instance, providers should offer well-structured content and
high ease of use. Additionally, the quality of firewalls, the
provision of privacy policies, and the explicitness of security
measures should receive ample attention to overcome the
“zone-of-intolerance” which precludes customers from
engaging in a relationship with the service provider.
Nevertheless, for experienced users, functional-utilitarian
aspects of e-service quality show a negative asymmetric
influence on satisfaction. Obviously, by engaging in an
enduring relationship with the service provider, their
fundamental needs have been satisfied and their focus has
shifted to evaluating higher-order hedonic factors. A service
provider investing in attributes creating structural firmness
or functional convenience does not gain higher satisfaction
Customer
satisfaction
FQ
FQ
1
FQ
2
FQ
3
HQ
HQ
1
HQ
2
HQ
3
FQ x HQ
FQ
1
HQ
1
FQ
2
HQ
2
FQ
3
HQ
3
FQ
2
FQ
1
2
FQ
2
2
FQ
3
2
HQ
2
HQ
1
2
HQ
2
2
HQ
3
2
FQ x EXP
FQ
1
EXP
FQ
2
EXP
FQ
3
EXP
FQ
2
x EXP
FQ
1
2
EXP
FQ
2
2
EXP
FQ
3
2
EXP
HQ x EXP
HQ
1
EXP
HQ
2
EXP
HQ
3
EXP
HQ
2
x EXP
HQ
1
2
EXP
HQ
2
2
EXP
HQ
3
2
EXP
1
= .65***
2
= .29***
7
= .09
n.s.
1
= .15*
2
= -.22**
4
= -.22*
5
= .16*
3
= .20*
6
= .15*
SAT1
SAT2
SAT3
Figure 1 Asymmetric and dynamic effects of e-service quality on
customer satisfaction.
* p<.05, ** p<.01, *** p<.001, n.s.=not significant.
Notes: FQ=functional-utilitarian quality, HQ=hedonic quality, FQ x
HQ=functional-utilitarian - hedonic quality interaction, FQ
2
=functional-
utilitarian quality quadratic term, HQ
2
=hedonic quality quadratic term,
FQ x EXP=functional-utilitarian quality - experience interaction, HQ x
EXP=hedonic quality - experience interaction, FQ
2
x EXP=functional-
utilitarian quality quadratic - experience interaction, and HQ
2
x
EXP=hedonic quality quadratic - experience interaction.
J. of the Acad. Mark. Sci. (2010) 38:288–302 297
scores over the course of time. However, performing
below market standard on aspects like clear navigation
menus, on-time delivery, relia ble page availability or
Secure Socket Layer (SSL) data protection is a critical
flaw. To sum up, exceeding functional-utilitarian quality
expectations does not pay off, while underperforming may
be dangerous.
Third, our results provide striking evidence for a
time-dependent shift in the dom inance of delight creating
factors from functional t o hedonic attributes. We find
hedonic e-service quality attributes to exhibit an upward
pattern from negative to positive asymmetries. T he plot
in Fig. 2, Panel B illustrates simultaneous effects of
hedonic quality and experience on customer satisfaction.
Interpreting the plot, the satisfaction function forms an
inverted S-shaped surface. With growing customer expe-
rience, the initially decreasing returns to hedonic quality
shift to increasing returns after having reached a turning
point. As a c onsequence , higher-order hedonic nee ds
gain importance for experienced customers and t he
corresponding quality attributes only then acquire the
potential to spark delight. The potential of hedonic
elements to delight only more experienced customers
corresponds to insights in the recent trend of service
providers developing online customer communities. Com-
munity users carry pride in being recognized as users of an
Internet portal (Mathwick et al. 2008), but getting social
support and building social capital only works out for long-
term users. Customers need to be long-standing and active
users of the community in order to build their expert image
vis-à-vis other customers and leverage their (online) social
network.
In sum, customers tend to start wi th online shopping as
an alternative channel to offline shopping. However, as
customers get familiar with all functionalities, they open
themselves for the fun mode of shopping. More specifical-
ly, inexperienc ed customers gain little satisfaction from
customizable layouts, large community networks and fun
experiences such as trivia’s, games and chat functions. In
fact, these elements have the ability to boost satisfaction for
the more experienced e-service consumer. This contrasts
suggestions based on flow theory stating that enjoyment
is a general precondition for using the Internet and that
the Internet is basically an “entertainment mediu m”
(Orwall 2001).
Managerial implications
Our results emphasize the necessity of considering both
functional-utilitarian and hedonic quality attributes
throughout the e-service provision. Moreover, they con-
tradict the erroneous beliefs of many service managers
sticking to the linear quality-satisfaction relationship
paradigm. The tenacity of belief in this paradigm is a
possible explanation for the frustration of many e-service
providers with the outcomes of their quality management.
Some of them have even called for abandoning service
quality management as a means for enhancing satisfac-
tion (Anderson and Mittal 2000). Instead, if asymmetries
are taken into account, overspending for “negative” and
underspending for “positive ” attributes can be avoided.
Thus, our approach helps e-service managers to coexist
with finance and operations by mapping service-related
expenditures.
Functional-utilitarian quality
Experience
Customer satisfaction
A: Functional-utilitarian quality
Hedonic quality
Experience
B: Hedonic quality
Customer satisfaction
Figure 2 Asymmetric and dynamic effects of e-service quality on customer satisfaction.
Note: The three-dimensional graphs are partial plots over the observed domain of the two independent variables included.
298 J. of the Acad. Mark. Sci. (2010) 38:288–302
Functional aspects of using e-services can trigger delight
for inexperienced customers and should at least be up-to-
standard when targeting new customers. While “newbies”
are easily impressed by features like well arranged content,
order fulfillmen t, low downtime s and state-of-the-art data
protection, the delight-creating potential o f functional-
utilitarian attributes continuously decreases over time.
These structural elements of a website comprise the most
basic needs of online consumers (Valacich et al. 2007). As
hedonic attributes acquire a customer focus only later, this
calls for dynamically personalizing a website. In "person-
alization 2.0", the information provided by the customer,
together with his/her purchase history and privacy settings
form a customer scenario, which a website can use to match
a customer to marketing activity (Needles 2008). For
instance, p ortal users often log in first for checking their
email. Usage information is consequently registered in the
user ’s profile, which could include visit frequency, click-
stream behavior, or transactions (Winer 2009).
Regarding the content of customization, inexperi-
enced customers should be exposed to features like
easy access website policies, product information, quick
links or guided tours by means of avatars (i.e., virtual
characters). After a learning period and gaining experi-
ence with the e-service, m ore hedonic elements m ay be
added to generate emotional responses. Possibilities
include chat functions with other customers, custom-
izable website look and feel, displaying products in a
3D virtual walk-through shop instead of scrollable
listings, or lotteries for free products. However, it has
to be noted that firms should not lose sight of
functional-utilitarian attributes when improving the he-
donic elements as advancing technical features as well
might be necessary as a response to competitors’ moves.
In other words, if a competitor raises the bar of
performance, this might well grow to be a market
standard (File et al. 19 9 4 ). Performing under the market
standard on a negative asymmetric attribute will be met
with a large amount of customer dissatisfaction. In
addition, exposing a customer to new and improved
features on a too regular basis may cause feature fatigue.
Thompson et al. (2005) show that mental exhaustion and
stress can be caused by products that come with a too
large number of f eatures.
Limitations and future research directions
Our study opens several interesting avenues for future
research. First, this study is among the first to statistically
test asymmetries in the relationship between service
quality and customer satisfaction by means of a nonlinear
structural equation modeling approach. H owever, while
we chose robustness over detail when specifying our
structural model using second-order constructs, future
studies could make a contribution by assessing asymme-
tries on first-order level. Second, where we focus on the
relati ons hi p between s er v ic e quality a nd customer satisfac-
tion, Anderson and Mittal (2000) suggest that more
relationships i n the satisfacti on-profit cha in may be
nonlinear. Consequently, future studies could statistically
substantiate the asymmetric nature of the customer
satisfaction-customer profitability link. A service pro-
vider ’s capability of converting satisfaction assets into
profitable outputs i s an aspect gaining increased impor-
tance (Gupta and Zeithaml 2006). By examining a cause-
and-effect chain leading from service quality investments
via satisfaction to cust omer profit ability, the tot al effect
of service quality initiatives on a f irm’s financial
performance can be traced. Third, with respect to the
survey method, a self selection bias might exist.
However, empirical results show that this bias is weaker
than typically proposed in the literature. Deutskens et al.
(2006) f ound that in many cases generalizability bias is
negligible. Fourth, acquiescence is one but not the only
source of common method bias. Therefore, future
studies could explicitly incorporate a common method
factor along with an acquiescence factor in their models
(Agustin and Singh 2005). Finally, while using a
customer experience variable is a statistically sound
method for drafting dynamic effects, future studies could
monitor each specific customer over the course of time by
applying a longitudinal design.
J. of the Acad. Mark. Sci. (2010) 38:288–302 299
Appendix Scale items for construct measures
Acknowledgement The authors would like to thank the editor and
four anonymous reviewers for their valuable comments on earlier
drafts of the manuscript.
Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which per-
mits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.
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Construct Item Item
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Efficiency This website makes it easy to get anywhere on the site. (EFF1) .64 .89 .73
This website loads its pages fast. (EFF2) .79
This website is simple to use. (EFF3) .76
Fulfillment This website delivers orders when promised. (FUL1 Shop) .77 .89 .72
This website gives accurate price and product comparisons as promised. (FUL1 Portal)
This website sends out the items ordered. (FUL2 Shop) .72
This website delivers relevant price and product comparisons. (FUL2 Portal)
This website makes accurate promises about delivery of products. (FUL3 Shop) .67
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Privacy This website protects information about my web-shopping behavior. (PRIV1) .79 .89 .73
This website does not share my personal information with other sites. (PRIV2) .76
I can trust this website. (PRIV3) .62
Website design This website is visually appealing. (WEBDES1) .76 .85 .66
This website’s appearance is professional. (WEBDES2) .74
This website has innovative features. (WEBDES3) .48
Enjoyment Shopping at this website is exciting. (ENJOY1) .85 .92 .80
Shopping at this website is interesting. (ENJOY2) .85
Shopping at this website is enjoyable. (ENJOY3) .71
Image People in my personal environment who use this website have more prestige than those
who do not. (IMAG1)
.87 .95 .86
People in my personal environment who use this website have a high profile. (IMAG2) .87
Using this website is a status symbol in my personal environment. (IMAG3) .86
Customer
satisfaction
Overall, how do you feel about your experience with the online shop? .93 .83
Very dissatisfied (= 1) to very satisfied (= 7) (SAT1) .90
Very displeased (= 1) to very pleased (= 7) (SAT2) .73
I think I did the right thing when I decided to use this online shop. (SAT3) .84
Note: Results are based on a full confirmatory factor analysis containing functional-utilitarian and hedonic quality as second-order constructs and
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