Journal of Service Science and Management, 2011, 4, 476-485
doi:10.4236/jssm.2011.44054 Published Online December 2011 (
Copyright © 2011 SciRes. JSSM
Brand Equity and Brand Loyalty in the Internet
Banking Context: FIMIX-PLS Market
Sandra Maria Correia Loureiro1, Francisco Javier Miranda2
1Department of Economy, Management, and Industrial Engineering, University of Aveiro, Campus of Santiago Aveiro, Santiago,
Portugal; 2Departament of Business Management and Sociology, Univeristy of Extremadura, Economics and Business Faculty,
Badajoz, Spain.
Received September 21st, 2011; revised November 2nd, 2011; accepted November 22nd, 2011.
This research presents a model that integrates trust, online risks and benefits, brand awareness/associations, perceived
quality and explains ho w they impa ct on brand equ ity and b rand lo yalty in th e con text of in ternet bankin g . The research
model estimation uses the PLS approach and applies FIMIX-PLS to segment the sample. The research findings show
that the main difference characterizing the two uncovered customer segments lies in the place of residence. Thus, the
impact of online benefits on trus t in the service provided is stronger for the first segment than for th e second. For cus-
tomers of the second segment, confidence in the banks web site information leads to a better perception of service
quality and this is very importan t to ensure loyalty to the brand.
Keywords: Perceived Quality, trust, Brand Equity, Brand Loyalty, Finite M ixture Modeling
1. Introduction
Nowadays, the online service has grown in interest and
adoption due to its convenience, ease of use, among other
features. According to Pikkarainen et al. [1], since the
middle of the last decade of the 20th Century, a radical
change has taken place in banking delivery channels to-
wards using self-service channels such as online banking
services. Internet banking provides consumers with a set
of information-related benefits that favors its adoption,
including easy access, responsive systems, opportunity for
the user to control bank accounts at any time and place, and
access to personalized information content to make inve-
stment and finance decisions. Internet banking is also an
easy way for the consumer to compare and contrast ser-
vices [2,3].
In this study we follow the definition proposed by Pik-
karainen et al. [1] to define internet banking: “an internet
portal, through which customers can use different kinds
of banking serv ices ranging from bill payment to making
investments”. Thus, the focus is on technologies that cus-
tomers use without any interacti on wi t h, or assi st ance from ,
bank employees. According to Meuter et al. [4], these te-
chnologies can be summarized as self-service technolo-
gies or SSTs.
Several studies have been devoted to understand the
factors that encourage or discourage the adoption or ac-
ceptance of SST, perceived risk, and trust [1,5-8]. As far
as I know, little research exits on antecedents and conse-
quences of internet banking brand equity. Thus, the pur-
pose of this study is to examine the impact of brand as-
sociations/awareness, p erceived quality, and intern et ban-
king trust on internet banking brand equity and also the
impact of internet banking brand equity and perceived
quality on brand lo yalty, using the PLS approach. Th e fi-
nite mixture partial least squares (FIMIX-PLS), propo-
sed by Hahn et al. [9] is also applied to segment the sa-
mple. T his appr oach combin es a fin ite mixtu re pro cedur e
with an expectation-maximization (EM)-algorithm spe-
cifically coping with the ordinary least squares (OLS)-
based predictions of PLS and enables reliable identifica-
tion of distinctive customer segments, with their charac-
teristic estimates for relationships of latent variables in
the structural model.
2. Theoretical Background and Hypotheses
The concept of brand equity has been a field of interest to
both firms and researchers for several years. There are
Brand Equity and Brand Loyalty in the Internet Banking Context: FIMIX-PLS Market Segmentation477
several definitions of brand equity. One of the most wi-
dely accepted is the Farquhar’s approach [10], which de-
fines brand equity as added value for the company, for
the delivery, or for the consumer. Later, Aaker [11] de-
fines it as the sum of assets that are associated with the
brand name, such as awareness, loyalty, perceived qua-
lity, as well as other proprietary assets. For Kapferer [12],
brand equity is a reflection of the consumer and a mental
image of proposed values (brand identity). Keller [13]
claims that the basis of brand equ ity lays on brand know-
ledge and its positive associations. De Chernatony [14]
defines it as a process, both internal and external to the
organization, of offering a value proposal represented by
the brand. Although the idea that br and equity adds valu e
to the product or service is apparent in all these defini-
tions, two different research approaches can be perceived:
a business (or financial) perspective and a consumer per-
spective [15]. The approach based on the consumer per-
spective is one which concerns us in particular. Accord-
ing to Myers [16], the consumer perspective can also be
divided in two ways: one based on consumer perceptions
and the other based on his/her attitudes and behaviour.
Aaker [11] and Keller [17] have provided conceptual
schemes that link brand equity with various consumer
response variables. In general, there are direct and indi-
rect measures of brand equity. As for the direct approach,
an attempt is made to assess the valu e ad ded by th e brand
to the product [10,13]. Specifically, Aak er [11] identified
four major consumer-related bases of brand equity: brand
loyalty, awareness, perceived quality, and brand associa-
tions. Keller [17] proposed a knowledge-based framewo-
rk for creating brand equity, based on two dimensions:
brand awareness and brand image. On the other hand, the
indirect approach focuses on identifying potential sour-
ces of brand equity [11,17]. However, Keller [17] argues
that the direct and indirect approaches are complementa-
ry and should be used together.
Park and Srinivasan [18] consider brand equity as the
difference between overall brand preference and multi-
attributed preference based on objectively measured at-
tribute levels, whereas Agarwal and Rao [19] regard it as
an overall quality and choice intention. Based on the
above considerations Yoo and Donthu [20] developed a
multidimensional consumer-based brand equity scale. They
also suggested that a potential causal order may exist
among the dimensions of brand equity. Thus, the hierar-
chy of the effects model suggests that brand awareness
and associations precede perceived quality and that per-
ceived quality precedes brand loyalty [20]. The effect of
high quality on brand loyalty is well known since it is th e
basis for consumer satisfaction [21-26].
Yoo et al. [27] demonstrated that the level of brand
equity is positively related to the extent to which brand
quality, brand loyalty, brand associations and awareness
are evident in the product (e.g., athletic shoes, camera
film, or color television sets). High perceived quality
would drive a consumer to choose the brand rather than
other competing brands. Therefore, brand equity will in-
crease according to the degree that brand quality is per-
ceived by consumers. Brand loyalty makes consumers
purchase a brand routinely and resist switching to an-
other brand. Hence, depending on the extent that consu-
mers are loyal to the brand, brand equity will increase.
However, brand loyalty could also be regarded as a po-
tential outcome of brand equity. Several researchers po-
inted out that high brand equity is associated with high
brand preference and loyalty [28,30,14]. The Chang and
Liu‘s [29] model empirically supported the argument that
brands with higher levels of brand equity would generate
higher levels of customer brand preference. In turn, high-
er customer brand preference was associated with greater
willingness to continue using the service brand.
Brand awareness and associations are both positively
related to brand equity. If the consumers recognize, qui-
ckly recall, and are aware of the brand, this can be a sign
of quality and commitment. Thus, a buyer aware of a brand
with favorable associations in her/his mind and able to
recognize quality is more willing to consider this br and at
the time of purchase, which leads to a favorable behavior
towards the brand. On the basis of the review of the li-
terature, the following hypotheses are proposed (see Fi-
gure 1):
H1: Brand awareness/association exercises a positive
impact on perceived quality.
H2: Perceived quality exercises a positive impact on
brand loyalty.
H3: Brand awareness/association has a positive effect
on internet banking brand equity.
H4: Internet banking brand equity has a positive effect
on brand loyalty.
H5: Perceived quality has a positive effect on internet
banking bra nd equi t y.
Trust has been studied primarily in the co ntext of rela-
tionship marketing [31-33]. Morgan and Hunt [33] con-
ceptualize trust “as existing when one part has confi-
dence in an exchange partner’s reliability and integrity”.
Rousseau et al. [34] defined trust as a “psychological
state comprising the intention to accept vulnerability
based on positive expectations of the intentions or be-
haviors of another”. Later, Bart et al. [35] adopted this
last definition to the con text of online trust.
Yoon [36] identify six factors (security assurance, brand,
search, fulfillment, presentatio n, and technology) that fo-
rmally represent the essence of online trust and, over ti-
me, they reflect on personality attributes such as depen-
dability, reliability, and honesty. Yoon [36 ] also pro poses
Copyright © 2011 SciRes. JSSM
Brand Equity and Brand Loyalty in the Internet Banking Context: FIMIX-PLS Market Segmentation
Copyright © 2011 SciRes. JSSM
Be n ef its
Intern et
Bankin g
Trus t
Per ce ived
Br and
Bankin g
Br and
Br and
H7 H5
Figure 1. Proposed conceptual model.
that consumer awareness is a mediating variable in web
site trust and satisfaction and suggests that online trust
can exercise a positive effect on web site awareness.
In their seminal work Ambler [37] presents trust as an
affective and not a cognitive, analytical construct which
can be a proxy for brand equity. On the other hand, Kim
et al. [38] empirically found that trust had a positive in-
fluence on brand awareness in the health care context.
On the strength of the above considerations, the follow-
ing hypotheses are proposed (see Figure 1):
H6: Internet banking trust positively influences brand
H7: Internet banking trust positively influences per-
ceived quality.
Trust is largely associated with lower perceived risk
and customers’ perceptions of security and privacy. Trust
acts as a mechanism designed to reduce consumers’ per-
ceived risk in internet shopping [39], reduces consumers’
transaction-specific uncertain ty and related risks associa-
ted with the possibility that a bank might behave oppor-
tunistically [40], and lowers the perceived risk of facing
a negative outcome of a transaction by reducing informa-
tion complexity [41]. However, the causal relational or-
der between trust and perceived risk has not yet been
clarified. This research follows the works of Aldás-Man-
zano et al. [7] and Yousafzai et al. [8] and states that high
trust on internet banking reduces perceived risk. More-
over, perceived benefits of online banking (such as easi-
ness to use and conven ience) will help to build trust (see
Figure 1).
H8: Internet banking trust exercises a negative effect
on risk perceived by the e-banking consumer.
H9: Perceived benefits have a positive effect on inter-
net banking trust.
3. Method
3.1. Sample and Data Collection
Drawing from literature review, a research model was
constructed for this study to explain the relationship a-
mong brand awareness/association, perceived quality, in-
ternet banking trust, internet banking brand equity, per-
ceived quality, and brand loyalty. The questionnaire, in-
cluding the items of the latent variables and a section
with the socio-demographic variables was first devised in
English and then translated into Portuguese. Back trans-
lation was used to guarantee that the questionnaire com-
municated similar information to all respondents [42,43].
A pilot sample of twenty-three internet banking users
(personally interviewed) was used to ensure that the wo-
rding of the questionnaire were clear.
In order to collect online banking users’ information,
we first required authorization from a large international
and private bank operating in Portugal to express our
need for the purposes of information research. After that,
the private bank helped to email invitation letters to its
users with a message explaining the need to understand
their (the users’) experience in the initial adoption of on-
line banking services. The invitation letter also lin ked up
to a web site where users could fill out an online ques-
tionnaire. The data analysis relies on 496 completed on-
line questionnaires, conducted during July of 2009. The
overall response rate was 34%.
Brand Equity and Brand Loyalty in the Internet Banking Context: FIMIX-PLS Market Segmentation479
Table 1. The demographic profile of the interviewed inha-
bitants of the Portugal.
Gender Age
Male: 67.5%
Female: 32.5
18 - 25: 7.1%
26 - 35: 40.0%
36 - 45: 31.5%
46 - 55: 12.9%
56 - 65: 5.6%
66 - 75: 2.4%
>75: 0.5%
As Table 1 shows, most of the respondents were ma-
les. The majority of respondents (71.5%) were between
26 and 45 year old. We gathered questionnaires from
almost all the regions of Portugal regions, though mostly
from Lisbon and Oporto.
3.2. Variable and Measurement
Brand awareness/associations, perceived quality, brand
loyalty, and internet banking brand equity were opera-
tionalized on the basis of Yoo & Donthu [27], Zeithaml
et al. [26] and Keller [13,17]. Internet banking trust was
measured using four items adapted from Bart et al. [35].
Online benefits and online risks were adapted from For-
sythe et al. (2006). Each statement of the questionnaire
was recorded on a 5-point Likert scale (1 = strongly dis-
agree, 5 = strongly agree).
3.3. Data Analysis
The Partial Least Squares (PLS) approach was employed
to estimate structural paths coefficients, R2, Q2, and the
Bootstrap technique. PLS is based on an iterative com-
bination of principal components analysis and regression,
and aims to explain the variance of the constructs in the
model [45]. In terms of advantages, PLS simultaneously
estimates all path coefficients and individual item load-
ings in the context of a specified model and, as a result,
enables researchers to avoid biased and inconsistent pa-
rameter estimates. Moreover, it has proved to be an effe-
ctive analytical tool to test interactions by reducing type
II error [46]. Nevertheless, PLS models are based on pre-
diction-oriented measures, not covariance fit like cova-
riance structure models developed by Karl Jöreskog (or
LISREL program developed by Jöreskog and Sörborn).
Besides the variance explained (i.e.R2), as an indicator of
how well PLS has met its objective [47]and Stone-
Geisser’s Q2 measure, which can be used to evaluate the
predictive power of the mode l, Tenenhaus et al. [48] pro-
pose the geometric mean of the average communality
(outer mode) and the average R2 (inner model) that is
limited between values of 0 and 1 as overall goodness of
fit (GoF) measures PLS (Cross validated PLS GoF) (see
Equation 1).
.GoFcommunality R (1)
Following the analysis of the structural model, the fi-
nite mixture partial least squares (FIMIX-PLS) was ap-
plied to segment the sample based on the estimated sco-
res for latent variables [49]. Finally, through a t-test, a
parametric analysis was employed to determine if the
segments were statistically different. For each segment
the model was estimated once more and the precision of
the PLS estimates was also analyzed. The parametric test
uses the path coefficients and the standard errors of the
structural paths calculated by PLS with the samples of
the two segments, using the following expression of
t-value for multi-group comparison test (2) (m = segment
1 sample size and n = segment 2 sample size).
 
Segment 1Segment 2
Segment1Segment 2
mn mn
 
The PLS model is analyzed and interpreted in two
stages. First, the adequacy of the measures is assessed by
evaluating the reliability of the individual measures and
the discriminant validity of the constructs [50]. Then, the
structural model is appraised.
The adequacy of the measures is assessed by evalua-
ting the reliability of the individual items and the dis-
criminant validity of the constructs [50]. Item reliability
is assessed by examining the loading of the measures on
their corresponding construct. All the loadings of scales
that measure reflective constructs approximate or exceed
0.707 (see Table 2). This indicates that more than 50 pe-
rcent of the variance in the observed variable is explained
by the construct [51].
Composite reliability was used to analyze the reliabi-
lity of the constructs since this has been regarded as a
more exacting measurement than Cronbach’s alpha [52].
Table 2 indicates that all constructs are reliable since the
composite reliability values exceed the threshold of 0.7
and even the strictest one of 0.8 [53].
The measures demonstrated convergent validity as the
average variance of manifest variables extracted by con-
structs (AVE) was at least 0.5, indicative that more vari-
ance was explained than unexplained in the variables asso-
ciated with a given construct. The criterion used to assess
discriminant validity was the square root of AVE, which
Copyright © 2011 SciRes. JSSM
Brand Equity and Brand Loyalty in the Internet Banking Context: FIMIX-PLS Market Segmentation
Copyright © 2011 SciRes. JSSM
Table 2. Measurement results.
Construct LV Index ValuesItem LoadingComposite reliabilityAVE*
Brand Awareness/associations 4.1 0.87 0.69
BAW1: I can recognize x among other com peting brands 0.851
BAW2: I am aware of x 0.895
BAW3: I can quickly recall the symbol or l ogo of x 0.736
BAW4: I have diffi culty in imagining x in my mind. (r) a
Perceived Quality 3.6 0.88 0.78
Q1: The quality of web site services provided by x is extremely high 0.903
Q2: The visual design of web site x has a quality extremely high 0.867
Brand Loyalty 3.6 1.00 1.00
L1: I consider myself to be loyal to x 1.000
Internet Banking Trust 3.8 0.93 0.82
T1: I have more confidence in this web site than other sites I have visited a
T2: My overall trust in this site is high 0.864
T3: My overall believability of the information on this site is high 0.940
T4: My overall confidence in the recommendations on this site is high 0.909
Internet Banking Brand Equity 3.4 0.93 0.81
BE1: I sign products in web site x instead of any other bank,
even if they are identical 0.876
BE2: Even if another bank has the same characteristics as x,
I prefer to sign products in web site x 0.925
BE3: If there is a bank with an online service as good as x,
I prefer the x 0.900
Online Benefits 4.1 0.90 0.70
B1: I can sign pr oducts at home 0.876
B2: I can sign products whenever I want 0.843
B3: I can sign products online without going to the agency 0.830
B4: I sign products easily 0.796
Online Risks 2.4 0.90 0.60
R1: I feel lack of confidence in the web site 0.769
R2: I may not get the product I want 0.769
R3: I may sign something by accident 0.722
R4: There may b e some technical failure 0.782
R5: It’s difficult to get information about the prod uct 0.788
R6: It’s too complicated sign products 0.780
*AVE Average Variance Extracted. (r) indicates reversed scoring. a indicates item eliminated. x indicates a brand name.
Brand Equity and Brand Loyalty in the Internet Banking Context: FIMIX-PLS Market Segmentation481
Table 3. Discriminant validity analysis.
Correlations of constructs
Construct Brand
Awareness/associations Online
Benefits Internet Banking
Brand Equity Brand
Loyalty Perceived
Quality Online
AVE1/2 0.83 0.84 0.90 1.00 0.88 0.78 0.90
Brand Awareness/associations 1.00 0.37 0.59 0.50 0.67 –0.19 0.51
Online Benefits 0.37 1.00 0.37 0.20 0.51 –0.37 0.56
Internet Banking Brand Equity 0.59 0.37 1.00 0.71 0.71 –0.24 0.52
Brand Loyalty 0.50 0.20 0.71 1.00 0.63 –0.14 0.46
Perceived Quality 0.67 0.51 0.71 0.63 1.00 –0.27 0.68
Online Risks –0.19 –0.37 –0.24 –0.14 –0.27 1.00 –0.46
Internet Banking Trust 0.51 0.56 0.52 0.46 0.68 –0.46 1.00
should be greater than the correlation between the con-
struct and other constructs in the model [51]. Table 3
shows that all variables have discriminant validity.
The Blindfolding technique was used to calculate the
Q2 and a nonparametric approach, called Bootstrap, to
estimate the precision of the PLS estimates. Thus, 500
samples sets were created in order to obtain 500 estima-
tes for each parameter in the PLS model. Each sample
was obtained by sampling with replacement of the origi-
nal data set [52,45]. As all values of Q2 are positive, the
relations in the model have predictive relevance.
In the next analytical step, the FIMIX-PLS module of
Smart PLS 2.0 was applied to segment the sample based
on the estimated scores for latent variables. FIMIX-PLS
results were computed for two, three, and four classes.
The results reveal that the choice of two segments is ap-
propriate for customer segmentation purposes. All rele-
vant evaluation criteria considerably decrease in the en-
suing numbers of segments (see Tab le 4) and each addi-
tional segment has only a small size, which explains a
marginal portion of heterogeneity in the overall set of da-
ta. Over two thirds of all our observations are well assi-
gned to one of the two classes with a probability of more
than 0.7.
Next, observations are assigned to each segment acco-
rding to the segment membership’s maximum a posterio-
ri probability. The first segment represents 79% of the
sample and the second segment 21%. Table 5 shows the
global model and FIMIX-PLS results for two latent seg-
ments. Before evaluating goodness-of-fit measures and
inner model relationships, all outcomes for segment-spe-
cific path model estimations were tested with regard to
reliability and discriminant validity. The analysis showed
that all measures satisfy the relevant criteria for model
evaluation [45].
All path coefficients of the global model are signify-
cant at a level of 0.001 or 0.05, apart from the relation-
ship between brand awareness/associations and internet
banking brand equity. So, the H3 hypothesis is not sup-
ported. As shown in Table 5, the relationship between
brand awareness/associations and internet banking brand
equity is also not significant for the first and second se-
gments either. The strength of the relationship between
perceived quality and brand loyalty is higher for the se-
cond segment than for the first one. However, the stren-
gth of the relationship between internet banking trust and
online risks seems to be weaker for the second segment
than for the first one. Moreover, the two segments dis-
play significant differences, except for the structural pa-
ths: brand awar eness/asso ciation - > internet banking brand
equity, brand awareness/association - > perceived quality,
perceived quality - > internet banking brand equity, and
perceived quality - > brand loyalty.
The final step involves the analysis of each segment,
using socio-demographic variables. The analysis reveals
that the place of residence is the principal difference that
characterizes the two uncovered customer segments.
Customers from the first segment, the largest of the sa-
mple, live mainly in Oporto (the second largest city in
Portugal) and other inner northern and southern Portu-
guese regions. These customers ascribe special importan-
ce to the perceived online benefits. The perceived bene-
fits have a strong and positive implication on internet
banking trust and reducing online risk. Trust significantly
contributes to improving the favorable associations/awa-
reness to the brand.
Table 4. Model selection.
K = 2 K = 3 K = 4
AIC (Akaike’s Information Cr iterion) 2903.8 3074.5 3046.3
BIC (Bayesian Information Criterion) 3007.6 3257.133231.83
CAIC (Consistent AIC) 3007.7 257.4 232.0
EN (Normed Entropy Statistic) 0.7689 0.6189 0.5020
Copyright © 2011 SciRes. JSSM
Brand Equity and Brand Loyalty in the Internet Banking Context: FIMIX-PLS Market Segmentation
Table 5. Global model and disaggregate results for two latent segments.
Structural Paths Global K = 1 K = 2 t[mgp]
Brand Awareness/associations Internet Banking Brand Equity0.2071NS 0.1619 NS 0.1521 NS 0.1700 NS
Brand Awareness/associations Perceived Quality 0.4334*** 0.3985*** 0.4251*** –0.7550 NS
Online benefits Internet Banking Trust 0.5557*** 0.5797*** 0.4025*** 5.0152*
Internet Banking Brand Equity Brand Loyalty 0.5223*** 0.5556*** 0.4010*** 2.0475*
Perceived Quality Internet Banking Brand Equity 0.5718*** 0.6307*** 0.6365*** –0.1339 NS
Perceived Quality Brand Loyalty 0.2636* 0.2687* 0.3683*** –1.3288 NS
Internet Banking Trust Brand Awareness/associations 0.5080*** 0.5673*** 0.3748*** 4.0954*
Internet Banking Trust Perceived quality 0.4616*** 0.4617*** 0.5277*** –1.9585*
Internet Banking Trust Online Risks –0.4646*** –0.4820*** –0.2796** –3.2465*
Segment sizes 1.0000 0.7945 0.2055
R2 Awareness/associations 0.2580 0.3218 0.1405
R2 Internet Banking Brand Equity 0.5281 0.5588 0.5488
R2 Brand Loyalty 0.5379 0.6011 0.5124
R2 Perceived quality 0.6042 0.5807 0.6274
R2 Online Risks 0.2159 0.2323 0.0782
R2 Internet Banking Trust 0.3089 0.3361 0.1620
GoF 0.5618 0.5782 0.5082
*p < 0.5, **p < 0.01, ***p < 0.001, N S = not significant. T[mgp] = t-value for multi-group c omparison test ( see expression 2).
Customers from the second segment live mainly in
Lisbon (the capital and the largest Portuguese city). For
these customers the perceived quality is very important
to be loyal to the brand.
4. Conclusions, Limitations and Future
This research tests the differential effects of internet ban-
king trust, perceived quality, and brand awareness/asso-
ciations on internet banking brand equity and brand lo-
yalty. At the aggregate level, online benefits positively
affect internet banking trust, whereas trust exercises a ne-
gative effect on risk perceived by the e-banking consu-
mers. Internet banking trust has a positive effect on per-
ceived quality and brand awareness/associations. The a-
bility to recognize, to be aware of, and to quickly recall
the symbol or logo of the brand significantly contributes
to the improvement of the perceived quality, but not in-
ternet banking brand equity. However, perceived quality
of internet banking services is a good predictor of inter-
net banking brand equity and brand loyalty. Therefore,
brand loyalty can be seen as an outcome of internet ban-
king brand equity.
The findings prompt us to state that managers should
be attentive to the quality of web sites services and their
visual design, conscious of the need to improve on them.
The visual design of the web site should be in accordance
to the positive and favorable associations that most clo-
sely correlate with the identity and positioning desired
for the brand.
The positive albeit not significant relationship between
brand awareness/associations and internet banking brand
equity (H3 hypothesis) is consistent with the empirical
evidence of Faircloth, Capella and Alford’s study [54].
They found that brand image directly influences brand
equity, but positive brand attitude, one of the several ty-
pes of brand association [17], only has an indirect effect
on enhanced brand equity.
This study also provides an application of the finite
mixture partial least squares (FIMIX-PLS) to capture he-
terogeneity in PLS path modeling of brand awareness/
associations, perceived quality, internet banking trust, in-
ternet banking brand equity, and brand loyalty. This ap-
proach enabled us to identify two segments of customers
that result in heterogeneity within the inner model. This
led us to observe that the impact of online benefits on
trust in the service provided is stronger in the first seg-
ment than in the second. Confidence in the recommenda-
tions and information on the bank web site contributes to
reduce the perceptions of online risks . It also helps to en-
code the brand name in the customer’s mind and enables
him/her to recall and recognize such a name or, at least,
to improve the favorable associations/awareness of the
brand, especially where the first segment customers are
concerned. For customers living mainly in Lisbon, con-
fidence in the bank’s web site information leads to a bet-
Copyright © 2011 SciRes. JSSM
Brand Equity and Brand Loyalty in the Internet Banking Context: FIMIX-PLS Market Segmentation483
ter perception of service quality, which is very important
in ensuring loyalty to the brand.
The differences encountered may be related to lifestyle,
the frequency of recourse to internet banking, since cus-
tomers from the second segment live mainly in Lisbon
(the capital and the largest Portuguese city). These cus-
tomers (living in the big Lisbon) tend to have a lifestyle
that lead them to spend much time on the route between
home and work (and reverse), so they tend to adopt more
often and critically the online services. However, further
research is required to understand and to explain the fin-
dings. Future research should also examine other nega-
tive constructs, such as dissatisfaction factors. The author
considers it is also important to introduce variables like
communication or commitment, and credibility, and to
improve the items used in the variables
Finally, the FIMIX-PLS methods could prove to be ve-
ry interesting in the case of managerial practices as it can
grasp differences even in a small country such as Portu-
gal, where one does not anticipate a significant behavior
[1] P. Tero, K. Pikkarainen, H. Karjaluoto and S. Pahnila,
“Consumer Acceptance of Online Banking: An Extension
of the Technology Acceptance Model,” Internet Research,
Vol. 14, No. 3, 2004, pp. 224-235.
[2] M. J. Bitner, A. L. Ostrom and M. L. Meuter, “Imple-
menting Successful Self-Service Technologies,” The Aca-
demy of Management Executive, Vol. 16, No. 4, 2002, pp.
96-108. doi:10.5465/AME.2002.8951333
[3] D. S. Johnson, “Achieving Customer Value from Ele-
ctronic Channels through Identity Commitment, Calcula-
tive Commitment and Trust in Technology,” Journal of
Interactive Marketing, Vol. 21, No. 4, 2007, pp. 2-22.
[4] M. L. Meuter, A. L. Ostrom, R. L. Roundtree and M. J.
Bitner, “Self-service Technologies: Understanding Cus-
tomer Satisfaction with Technology-Based Service En-
counters,” Journal of Marketing, Vol. 64, No. 3, 2000, pp.
50-64. doi:10.1509/jmkg.
[5] G.-K. Sonja and R. Faullant, “Consumer Acceptance of
Internet Banking: The Influence of Internet Trust,” Inter-
national Journal of Bank Marketing, Vol. 26, No. 7, 2008,
pp. 483-504. doi:10.1108/02652320810913855
[6] M.-C. Lee, “Factors Influencing the Adoption of Internet
Banking: An Integration of TAM and TPB with Per-
ceived Risk and Perceived Benefit,” Electronic Com-
merce Research and Applications, Vol. 8, 2008, pp. 130-
141. doi:10.1016/j.elerap.2008.11.006
[7] J. Aldás-Manzano, C. Lassala-Navarré, C. Ruiz -Mafé and
S. Sanz-Blas, “Key Drivers of Internet Banking Services
Use,” Online Information Review, Vol. 33, No. 4, 2009,
pp. 672-695. doi:10.1108/14684520910985675
[8] Yousafzai, Shumaila, J. Pallister and G. Foxall, “Multi-
Dimensional Role of Trust in Internet Banking Adop-
tion,” The Servic e Industries Journal, Vol. 29, N o. 5, 2009,
pp. 591-605. doi:10.1080/02642060902719958
[9] Hahn, Carsten, M. D. Johnson, A. Herrmann and F. Huber,
“Capturing Customer He terogeneity Using a Finite Mixture
PLS Approach,” Schmalenbach Business Review, Vol. 54,
No. 3, 2002, pp. 243-269.
[10] P. H. Farquhar, “Managing Brand Equity,” Marketing Re-
search, Vol. 1, 1989, pp. 24-33.
[11] A. David, “Managing Brand Equity: Capitalizing on the
Value of a Brand Name,” The Free Press, New York,
[12] J.-N. Kapferer, “Strategic Brand Management: Creating
and Sustaining Brand Equity Long Term,” Kogan Page,
London, 1998.
[13] K. Kevin Lane, “Brand Synthesis: The Multidimensiona-
lity of Brand Knowledge,” Journal of Consumer Re-
search, Vol. 29, No. 4, 2003, pp. 595-600.
[14] C. Leslie de, “From Brand Vision to Brand Evaluation:
Strategically Building and Sustaining Brands,” Butter-
worth-Heinemann, Oxford, 2003.
[15] Pappu, Ravi, P. G. Quester and R. W. Cooksey, “Con-
sumer-Based Brand Equity: Improving the Measure-
ment-Empirical Evidence,” Journal of Product & Brand
Management, Vol. 14, No. 3, 2005, pp. 143-154.
[16] C. A. Myers, “Managing Brand Equity: A Look at the
Impact of Attributes,” Journal of Product and Brand Ma-
nagement, Vol. 12, No. 1, 2003, pp. 39-51.
[17] K. Kevin Lane, “Conceptualizing, Measuring, and Man-
aging Customer-Based Brand Equity,” Journal of Mar-
keting, Vol. 57, 1993, pp. 1-22. doi:10.2307/1252054
[18] P. Chan Su and V. Srinivasan, “A Survey-Based Me-
thod of Measuring and Understanding Brand Equity and
it s Extendibility,” Journal of Marketing Research, Vol . 31,
No . 2 , 199 4 , pp. 271-288. doi:10.2307/3152199
[19] M. K. Agarwal and V. R. Rao, “An Empirical Compari-
son of Consumer-Based Measures of Brand Equity,”
Marketing Letters, Vol. 7, No. 3, 1996, pp. 237-247.
[20] Y. Boonghee and N. Donthu, “Developing and Validating
a Multidimensioanl Consumer-Based Brand Equity
Scale,” Journal of Business Research, Vol. 52, 2001, pp.
1-14. doi:10.1016/S0148-2963(99)00098-3
[21] R. L. Oliver, “Measurement and Evaluation of Satisfac-
tion Processes in Retail Settings,” Journal of Retailing,
Vol. 57, 1981, pp. 25-48.
[22] M. J. Bitner, “Evaluating Service Encounters: The Eff-
ects of Physical Surroundings and Employee Responses,”
Journal of Marketing, Vol. 54, No. 4, 1990, pp. 69-82.
[23] C. J. Joseph, Jr., M. K. Brady, G. Tomas and M. Hult,
“Assessing the Effects of Quality, Value, and Customer
Copyright © 2011 SciRes. JSSM
Brand Equity and Brand Loyalty in the Internet Banking Context: FIMIX-PLS Market Segmentation
Satisfaction on Consumer Behavioural Intentions in Ser-
vice Environments,” Journal of Retailing, Vol. 76, No. 2,
2000, pp. 193-218. doi:10.1016/S0022-4359(00)00028-2
[24] F. Claes, “A National Customer Satisfaction Barometer:
The Swedish Experience,” Journal of Marketing, Vol. 56,
No. 1, 1992, pp. 6-21. doi:10.2307/1252129
[25] A. Parasuraman, L. L. Berry and V. A. Zeithaml, “Re-
finement and Reassessment of the SERVQUAL Scale,”
Journal of Retailing, Vol. 67, No. 4, 1991, pp. 420-450.
[26] V. A. Zeithaml, L. Berry and A. Parasuraman, “The Be-
havioural Consequences of Service Quality,” Journal of
Marketing, Vol. 60, No. 2, 1996, pp. 31-46.
[27] Y. Boonghee, N. Donthu and S. Lee, “An Examination of
Selected Marketing Mix Elements and Brand Equity,”
Journal of Academy of Marketing Science, Vol. 28, No. 2,
2000, pp. 195-211. doi:10.1177/0092070300282002
[28] C. J. Cobb-Walgren, C. A. Ruble and N. Donthu, “Brand
Equity, Brand Preference, and Purchase Inte n t , ” Journal of
Advertising, Vol. 24, No. 3, 1995, pp. 25-40.
[29] H. H. Chang and Y. M. Liu, “The Impact of Brand Equity
on Brand Preference and Purchase Intentions in the Ser-
vice Industries,” The Service Industries Journal, 29, No.
12, 2009, pp. 1687-1706.
[30] J. F. Devlin, A. L. Gwynne and C. T. Ennew, “Antece-
dents of Service Expectations,” The Services Industries
Journal, Vol. 22, No. 4, 2002, pp. 117-131.
[31] P. M. Doney and J. P. Cannon, “An Examination of the
Nature of Trust in Buyer-Seller Relationships,” Journal
of Marketing, Vol. 61, 1997, pp. 35-51.
[32] G. Shankar and R. L. Hess, “Dimensions and Levels of
Trust: Implications for Commitment to a Relationship,”
Marketing Letters, Vol. 8, No. 4, 1997, pp. 439-448.
[33] R. M. Morgan and S. D. Hunt, “The Commitment-Trust
Theory of Relationship Marketing,” Journal of Marketing,
Vol. 58, 1997, pp. 20-38. doi:10.2307/1252308
[34] D. M. Rousseau, S. B. Bitkin, R. S. Burt and C. Camerer,
“Not So Different After All: A Cross-Discipline View of
Trust,” Academy of Management Review, Vol. 23, No. 3,
1998, pp. 393-404. doi:10.5465/AMR.1998.926617
[35] B. Yakov, V. Shankar, F. Sulta n and G. L. Urban, “Ar e the
Drivers and the Role of Online Trust the Same for All
Web Sites and Consumers? A Large-Scale Exploratory
Empirical Study,” Journal of Marketing, Vol. 69, 2005,
pp. 133-152. doi:10.1509/jmkg.2005.69.4.133
[36] S.-J. Yoon, “The Antecedents and Consequences of Trust
in Online Purchase Decisions,” Journal of Interactive
Marketing, Vol. 16, 2002, pp. 47-63.
[37] A. Tim, “How Much of Brand Equity is Explained by
Trust?” Management Decision, Vol. 35, No. 4, 1997, pp.
283-292. doi:10.1108/00251749710169666
[38] K. K. Hoon, S. K. Kang, Y. K. Dong, H. K. Jong and S.
H. Kang, “Brand Equity in Hospital Marketing,” Journal
of Business Research, Vol. 6, 2008, pp. 75-82.
[39] S. L. Jarvenpaa and P. A. Todd, “Consumer Reactions to
Electronic Shopping on the World Wide Web,” Interna-
tional Journal of Electronic Commerce, Vol. 2, 1997, pp.
[40] Y. Shumaila, J. Pallister and G. Foxall, “A Proposed
Model of E-Trust for Electronic Banking,” Technovation,
Vol. 23, No. 11, 2003, pp. 847-860.
[41] R. C Mayer, J. H. Davis and F. D. Schoorman, “An Inte-
grative Model of Organizational Trust,” Academy of Man-
agement Rev iew, Vol. 20, No. 3, 1995, pp. 709-734.
[42] R. W. Brislin, “Back-Translation for Cross-Cultural Re-
search,” Journal of Cross-Cultural Psychology, Vol. 1,
No. 13, 1970, pp. 185-216.
[43] S. Uma, “Methodological and Theoretical Issues and
Advancements in Cross-Cultural Research,” Journal of
International Business Studies, Vol. 14, No. 2, 1983, pp.
61-73. doi:10.1057/palgrave.jibs.8490519
[44] F. Sandra, C. Liu, D. Shannon and L. C. Gardner, “De-
velopment of a Scale to Measure the Perceived Benefits
and Risks of Online Shopping,” Journal of Interactive
Marketing, Vol. 20, No. 2, 2006, pp. 55-75.
[45] W. W. Chin, “The Partial Least Squares Approach to
Structural Equation Modeling,” Modern Methods for
Business Research, In: G. A. Marcoulides, Ed., Lawrence
Erlbaum Associates Publisher, New Jersey, 1998, pp.
[46] W. W. Chin, B. L. Marcolin and P. R. Newsted, “A Par-
tial Least Squares Latent Variable Modelling Approach
for Measuring Interaction Effects: Results from a Monte
Carlo Simulation Study and an Electronic Mail Emotion/
Adoption Study,” Information Systems Research, Vol. 14,
No. 2, 2003, pp. 189-217.
[47] B. Donald, R. Thompson and C. Higgins, “The Partial
Least Squares (PLS) Approach to Causal Modeling, Per-
sonal Computer Adoption and Use as an Illustration,”
Technology Studies, Vol. 2, 1995, pp. 285-309.
[48] T. Michel, V. E. Vinzi, Y.-M. Chatelin and C. Lauro,
“PLS Path Modeling,” Computational Statistics & Data
Analysis, Vol. 48, 2005, pp. 159-205.
[49] C. M. Ringle, S. Wende and W. Alexander, Smart PLS
2.0 (beta),, Hamburg, 2005,
[50] J. Hulland, “Use of Partial Least Squares (PLS) in Stra-
tegic Management Research: A Review of Four Recent
Studies,” Strategic Management Journal, Vol. 20, No. 2,
1999, pp. 195- 204.
[51] E. G. Carmines and R. A. Zeller, “Reliability and Validity
Assessment,” Sage Publications, Inc., London, 1979,
Copyright © 2011 SciRes. JSSM
Brand Equity and Brand Loyalty in the Internet Banking Context: FIMIX-PLS Market Segmentation
Copyright © 2011 SciRes. JSSM
[52] F. Claes and D. F. Larcker, “Evaluatin g St ructural Models
with Unobservables Variables and Measurement Error,”
Journal of Marketing Research, Vol. 28, 1981, pp. 39-50.
[53] J. C. Nunnally, “Psychometric Theory,” 2nd Edition,
McGraw-Hill, New York, 1978.
[54] J. B. Faircloth, L. M. Capella and B. L. Alford, “The
Effect of Brand Attitude and Brand Image on Brand Eq-
uity,” Journal of Marketing Theory and Practice, 2001,
pp. 61-75.