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Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170)
2014 Vol: 1 Issue 2
118
www.globalbizresearch.org
Customer Interface Quality on Customer e-Loyalty and e-
Satisfaction in Malaysia with the Effects of Trustworthiness
Poh-Ming Wong Winnie,
University College of Technology Sarawak,
School of Business and Management,
Malaysia.
Email: winniewong@ucts.edu.my
___________________________________________________________________________
Abstract
With the increasing usage of e-commerce as one of the marketing channels, it has become
tremendously critical for a business to leverage Internet to extend their business via online. In
this case, an interactive customer interface plays a role as virtual representation of e-
retailers to capture more customers and retain the existing online customer. Therefore, the
primary objective of this study is to investigate the direct impact of customer interface quality
and trustworthiness on customer e-loyalty and e-satisfaction. This study also aims to examine
the indirect impact of customer interface quality on customer e-loyalty and e-satisfaction
through the mediating effect of trustworthiness. A survey method was conducted with three
hundred and night-five respondents who had online purchasing experience in the past. Data
collected from 395 useful respondents were tested against the research model using smart
partial least squares (smartPLS 2.0 (M3).The findings indicated that customer interface
quality is positively related to customer e-loyalty and e-satisfaction (at significant P<0.01
level) in the context of Malaysia. Additionally, in this study, trustworthiness has played the
role of a mediator. These findings provide several implications, limitations of the study, and
recommendations for future research are outlined.
___________________________________________________________________________
Keywords: Customer Interface Quality, e-Loyalty, e-Satisfaction, Trustworthiness
Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170)
2014 Vol: 1 Issue 2
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1. Introduction
The rapid growth of e-commerce has changed the customer shopping experience,
especially living in this electronic- or mobile-based society. More traditional, offline shoppers
are encouraged to switch to online shopping due to many advantages offered by Internet, such
as convenience, usefulness, ease-of-use, enjoyment, and so on. Perhaps, due to the time
constraint and serious traffic jam problems on the highway, they are more willing to stay at
home or office to order the needed products or services via online with a single click of
mouse. Therefore, it confirms Internet as an excellent platform (Cheng, Wang, Lin, & Vivek,
2009) and efficient advertising platform (Lim, Yap, & Lau, 2010).
The phenomenal growth in the numbers of Internet users and the perceived benefits of
Internet usage cannot be denied. However, Internet businesses should focus on the quality
interface itself to achieve customer loyalty and satisfaction. This is because technology
quality is deemed as the main determinant of customer satisfaction and customer loyalty,
especially from an online perspective. Moreover, customers in recent times are smart and are
fortunate to have the opportunity to evaluate the quality of Internet technology, based on how
they perceive the quality criteria of the particular website. Evidently, past survey study,
Genex found that 65 percent of online customers are discouraged to do online shopping due
the issue of poor customer interface (Tsao, 2005). Hence, this study generally aims to
investigate the direct impact of customer interface quality (convenience, customization,
interactivity, and character) and trustworthiness on customer e-loyalty and e-satisfaction
amongst Malaysian online shoppers. In addition, this study also examines the indirect impact
of customer interface quality on customer e-loyalty and e-satisfaction through the mediating
effect of trustworthiness.
2. Theoretical Foundations and Hypotheses Development
2.1 Customer e-Loyalty
In this study, customer loyalty is conceptualized as behavioural loyalty (e.g., repeat
purchase via positive recommendation). As customer loyalty facilitates repeat purchasing
behaviour (Anderson &Srinivasan, 2003), it is also able to increase customer retention
(Chirico & Presti, 2008; Singh, 2006), a profitable outcome for e-retailers (Srinivasan et al.,
2002). Oliver (1997) found that customer loyalty involves a higher level of commitment to
repurchase particular products or services in future (Costabile, Raimondo, & Miceli, 2010).
Correspondingly, Lin and Wang (2006) defined customer loyalty as customers, who showed
their positive attitude to enhance their repeat buying behaviour (Liao, 2012).
In the literature reviewing, Cyr (2008) had earlier defined e-loyalty as e-customers’
intention to stay with the same website (Cyr, Head, &Ivanov, 2009). As a proxy definition of
e-loyalty, e-loyalty is defined as a favourable customer attitude toward the similar e-retailer
Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170)
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that affects the repeat buying behaviour. In this study, there are three dimensions which are
proposed to examine customer e-loyalty, word-of-mouth, complaining behaviour, and future
purchase intention. As stated, online word-of-mouth refers to the content of the shared
information, such as opinions and past experiences (e.g., positive or negative) via the Internet
(Hennig-Thurau, Gwinner, Walsh, & Gremler, 2002). Accordingly, this, word-of-mouth is a
powerful and persuasive marketing tool for companies to convey the product or service
information to the customers (Liu & Payne, 2010). Positive word-of-mouth is a strong driver
in increasing the credibility of a company and its products (Grönroos, 1990). Future purchase
intention is found to have links to previous purchase intention (LaBarbera & Mazursky,
1983), because the intention to repurchase is the driver of future behaviour (Jones & Sassar,
1995). In this study, customer e-purchase intention refers to an individual’s purchase intention
via the Internet (Salisbury, Pearson, Pearson, & Miller, 2001). In addition, complaining
behaviour is defined as a negative response occurring when customers are dissatisfied with a
product or service (Chirico & Presti, 2008).
2.2 Customer e-Satisfaction
E-Satisfaction, in this study, is measured based on non-store retailing. Creating e-
satisfaction requires customers to feel comfortable shopping on websites (Szymanski & Hise,
2000), and maintain a positive attitude and response throughout the experience (Muylle,
Moenaert, & Despontin, 2004). Anderson and Sullivan (1990) found that repurchase intention
significantly influences satisfaction levels (Choi, Kim, & Kim, 2000). This finding is in line
with a study by Bolton and Drew (1991) in which customer satisfaction influenced purchase
intention and purchase behaviour. Customers are satisfied if their actual experience exceeds
their prior expectations (Anderson & Sullivan, 1993). For the purposes of this study, the
researcher has selected repurchase intention and revisit as the dimensions of customer e-
satisfaction. Repurchase intention refers to the number of e-purchases users plan to make
from a website (Reibstein, 2002). Additionally, revisit is defined as the decision to repurchase
the same products and visit the same stores regularly (Dholakia & Bagozzi, 2001), and is
associated with consumer motivation values.
2.3 Trustworthiness
Trustworthiness, generally, is defined as an attribute of trust by the trustees (Kate, 2009).
Gabarro (1978) defined trustworthiness as a complex construct that creates competence and
character of trustees and Liberman (1981) acknowledged trustworthiness as the beliefs of
trustees towards trust in lifelong relationships (Akter, D’Ambra, & Ray, 2011). In this study,
trustworthiness is represented as mediator and it is referred to the trust beliefs that eventually
lead to a consumer trusting a website. Furthermore, trustworthiness and trust are considered to
play an important role in the process of online purchase decision-making. As a result, the
Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170)
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researcher intent to apply these three specific dimensions, honest, benevolence, and
competence. As acknowledged in the literature, benevolence means the trustors, e-consumers,
feel confident that trustees are willing to provide the best services (Akter et al., 2011). The
definition of honesty is similar with integrity in this study, which is defined as the basis of
trustee-trustor relationship (Akter et al., 2011).It is the expectation that trustees will act in a
proper manner, based on satisfactory standards of honesty, towards the trustors (Ridings et al.,
2002). Despite honesty and benevolence, with the attribute of competence, e-consumers
expect to determine the quality and accuracy of information, hence it is the knowledge,
talents, and expertise required to complete the purchase (Hosmer, 1995).
2.4 Customer Interface Quality
Customer interface is interpreted as the online virtual representative of online retailers to
provide needed and relevant information to the customers (Chang & Chen, 2008). In fact,
website is a place for customers to interface with e-retailers in the digital world (Gehrke &
Turban, 1999). Based upon the review of the previous research, there are a number of
validated antecedent that has been used to measure the customer interface quality.
Swaminathan, Anderson, and Ponnavolu (2002) indicated that cultivation, choice, care,
community, convenience, customization, character and interactivity are antecedent of
customer e-loyalty (Mutum & Ghazali, 2010). Srinivasan et al. (2002) proposed eight
antecedents of loyalty namely, customization, interactivity, cultivation, care, choice, character
and convenience (Donnelly, 2009). However, for the purpose of this study, the researcher has
adapted the dimensions of customer interface quality, which contented convenience,
customization, interactivity, and character. From the customer’s point of view, convenience
means speed and ease of shopping (Seiders, Berrry, & Gresham, 2000). Convenience also
refers to ease-of- navigation and takes minimum time to sort information, and get into the
process of online purchasing (Ye & Jia, 2010). Apart from convenience, customization is
interpreted as an ability of a website to provide the ease of service, accessing availability, and
ensure satisfaction of online consumers from the website (Chang &Chen, 2008). This is also
known as customization website. Customized website eases the online consumers to create
their own website to record their history of purchase, relevant products or services
information, and their preferences (Kim, Kim, & Kandampully, 2009). Moreover, in the
literature reviewing, Fortin (1997) acknowledged interactivity as a process of interaction
among the consumers and retailers or using the network communication device to
communicate with other people such as email at any time and rexecute the information
(Dholakia, Dholakia, & Fortin, 2000). In addition, character is defined as the surface looks of
a website or the aesthetics. With an aesthetical or emotional appeal, a website captures the
attention of online consumer to stop to look for it (Beaird, 2007).
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2.5 Development of Hypotheses
Previous studies have found that customer interface factors contributed to customer e-
loyalty, these include character, customization, interactivity, and convenience (Mehta, 2005;
Chang & Chen, 2008). As stated in the literature reviewing, customization is a driver of
customer e-loyalty (Kassim & Ismail, 2008; Tarafdar & Zhang, 2008), and hence it increases
customer loyalty (Ansari & Mela, 2003). Besides, interactivity also creates positive impact on
customer e-loyalty (Ng & Matanda, 2010; Srinivasan, Anderson, & Ponnavolu, 2002).
Convenience, the third identified factor, also influences customer e-loyalty (Chang & Chen,
2008). In addition to its impact on customer e-loyalty, customer interface quality factors, such
as customization, interactivity, and character, affect customer satisfaction as well (Chang &
Chen, 2008). Rodgers et al. (2005) proposed that interactivity influences customer satisfaction
(Montoya-Weiss, Voss, & Grewal2003). Jiang and Benbaset (2007), examined the correlation
between interactivity, purchasing intention and revisit of the same website in future (Cyr,
Head, & Ivanov, 2009). Convenience is also a strong attribute affecting customer e-
satisfaction (Dholakia & Zhao, 2010; Kim et al., 2009). This finding was further concurred by
Kim, Ma, and Kim (2006), who found that convenience determined e-purchasing intention
and customer satisfaction. An empirical study by Chang and Chen (2008) discovered that
character was associated with customer satisfaction. Therefore, the existing literature implied
that factors affecting customer interface quality would have a positive impact on customer e-
loyalty and e-satisfaction in the current study. Subsequently, the following hypotheses were
formulated:
H1: Customer Interface Quality is related positively to customer e-loyalty and e-
satisfaction.
There are many examples in the literature of the influence of trust on customer e-loyalty
(Deb & Chavali, 2009; Kim, Chung, & Lee, 2010). Hart and Johnson (1999) argued that trust
generates true loyalty (Hamid, 2008) and Morgan and Hunt (1994) suggested that trust also
underpins commitment. When customers trust e-retailers, they disclose their personal
information (Kim, 2003) and e-retailers are easier to deal with in the future and money
transactions would be easier to track. Moreover, trust is an efficient marketing tool, which can
attract more customers to engage in future buying behaviour (Gefen, 2000) and influence
their e-purchasing intentions (Pennington, Wilcox, & Grover, 2003). Consequently, existing
research has shown a strong link between trust and customer e-loyalty (Ribbink, van Riel,
Liljander, & Streukens, 2004). Moreover, trust also has a similar impact on customer
satisfaction in terms of revisit intention and e-purchase intention (Yang, Zhang, & Wu, 2010;
Sam &Tahir, 2010). According to Grabner-Kräuter and Kalusha (2003), trust facilitates
customer’s intention to repurchase (Alam & Khokhar, 2006). Further, Chang, Chen (2008)
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and Ku, Liu (2010) support these findings and agreed that trust does affect repurchase
intentions in the Internet. Hence, the following hypothesis was proposed:
H2: Trustworthiness is positively related to customer e-loyalty and e-satisfaction.
Apart from the direct impact, trust also mediates the relationship between interactivity
and customer e-loyalty (Cyr et al., 2009). However, the findings of Ribbink et al. (2004) and
Kassim and Ismail (2009) identified that customization insignificantly affects trust in online
buying. Therefore, in the current study, the researcher implies that trustworthiness mediates
the relationship between customer interface quality with customer e-loyalty and e-satisfaction,
and has proposed the following hypotheses:
H3: Trustworthiness mediates the relationship between customer interface quality and
customer e-loyalty and e-satisfaction.
3. Methods
3.1 Samples and Measures
The sample data for the study was collected in Kuala Lumpur, Cyberjaya, and Putrajaya,
in Malaysia. The reason for the shortlisted sites is because these three urban areas have high
concentrations of Internet users and account for a large percentage of the total population.
According to the Ninth Malaysian Plan (2006), the population who stay in the urban area are
highly computer literate and have higher purchasing power than other areas. Besides, these
three selected states are considered as advanced technology areas and have the highest
personal computer ownership. The population of interest in this study was Internet users who
are registered with Telekom Malaysia (TM) Berhad, have an active Internet subscription and
owned a personal e-mail account. Thus, seethe participants chosen for our study are internet
users who have adequate experience in accessing the Internet. All these Internet users also
carried out an e-transaction via the Internet, at least once, and have experienced e-purchasing
once in the previous three months. These respondents were knowledgeable and computer
literate. The total sample size was 395, which fulfilled the rule of thumb of Roscoe’s (1975)
(Sekaran, 2010). A non-probability purposive sampling method was conducted to select
representative respondents.
The quantitative method was employed to collect the data through a set of questionnaire,
which was divided into two parts. Part A focused on all the predictors that had impact upon
customer e-loyalty and e-satisfaction. Part B described the respondent’s profile. A careful
review of the literature was undertaken in order to develop multi-items of contrasts and a
seven-point rating scale from ‘1’ strongly disagree to ‘7’ strongly agree. Confidentiality was
guaranteed in all cases. SmartPLS (M3) was used as the analytical tool.
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4. Findings
Three Hundred and Ninety Five (395) usable responses were obtained from the different
demographic backgrounds. Females recorded 213 (53.9%) and males 182 (46.1%), 230
respondents were single and 164 were married. Chinese were scored as the largest group,
represented by 191 samples (48.4%), Malays were 30.1 percent (n=119) followed by 8.9
percent of Indians (n=35). Majority of respondents were in the range of RM3001 to RM5000,
while 29.9 percent (n=118) were within RM1001 to RM3000, followed by 17.5 percent
(n=69) who earned RM5001 to RM7000. Academically, the respondents who had a minimum
secondary school level of qualification were only 2.5 percent (n=10). The highest percentage
were undergraduate degree holders (n=307, 77.7%) followed by Master’s degree (n=41,
10.4%) and diploma holders (n=30, 7.6%). This showed that they had higher purchasing
power parity. As for respondents age, majority were 26 to 30 years old (n=113, 28.6%), 21.5
percent (n=85) of respondents were 18 to 25 years old and 21.3 percent (n=84) were 31-35
years old. Moreover, 43 percent of respondents were categorized as others (n=170) which was
made up of university or college students, waitresses, part-time workers, blue-collar workers,
and others. The second largest group was professionals, which recorded 133 respondents
(33.7%) followed by 42 executives (10.6%). Besides, approximately, 41.5 percent (n=164) of
the respondents were recorded using the Internet for one to five hours a week, 16.2 percent
(n=64) for over 20 hours, 15.9 percent (n=63) recorded six to ten hours, and 13.7 percent
accessed the Internet 11 to 20 hours. In total, 386 respondents (97.7%) had significant e-
purchasing experience in the past. However, 390 respondents (98.7%) frequently sought
product information.
Assessment of the Measurement Model
The empirical data was analyzed using the partial least squares (SmartPLS) approach in
order to study the impact of this study. Partial Least Squares (PLS) is a soft modelling
technique developed by Herman Wold (1982, 1985) and is useful in theoretical knowledge. In
general, PLS is an exploratory analysis and confirmatory analysis method (Barroso, Carrión,
& Roldán, 2010). Wold (1979, as cited in Barroso et al., 2010) stated that PLS is a predictive-
based analysis, whereby it investigates a complex model with a large amount of variables and
the relationship between the exogenous variables and endogenous variables. It is able to judge
the formative and reflective constructs in a study’s model (Helm, Eggert, & Garnefeld, 2010).
Chin and Newsted (1999) found that PLS path modelling can provide accurate information
even with a sample size of 20 (Henseler, Ringle, & Sinkovics, 2009). In addition, PLS
provides corresponding analysis of measurement model, structural model, and interaction
relationships.
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Confirmatory factor analysis (CFA) has been conducted to test the measurement model,
which includes convergent validity, composite reliability, and discriminant validity. As shown
in Table 1 and 2, the cross-loading for all proposed items measured were loaded highly on its
own construct rather than any other constructs. The results showed that all indicators achieved
the minimum levels of AVE, 0.5, in the current study (Henseler et al., 2009). In other words,
all the latent variables in this study were able to explain more than half of the variance of
indicators, on average (Karim, 2009). Moreover, all constructs results of CR fulfilled the
recommended value, 0.7 as suggested by Gefen et al. (2000) and thus indicated that the items
of each construct could be used to measure the value with high reliability. Correspondingly,
Cronbach value for every constructs exceeded the ideal value, 0.7 as recommended by
Sekaran (2010). (see Table 2)Additionally, the square root of AVE was tested on the inter-
correlations of the constructs with the other constructs to ensure discriminant validity (Fornell
& Larcker, 1981). (see Table 4) In summary, measurement model of this study was
completely satisfactory with the evident results of reliability, convergent validity and
discriminant validity.
Table 1: Loading and Cross Loading
Constructs CIQ Trustworthiness e-Loyalty e-Sat
meanCHA 0.681 CIQ 0.575
meanCON 0.754 0.545 0.555 0.540
meanCUS 0.858 0.728 0.725 0.691
meanINT 0.712 0.583 0.591 0.486
meanBEN 0.698 0.902 0.682 0.659
meanHON 0.673 0.861 0.646 0.676
meanCOMP 0.717 0.911 0.683 0.719
meanWOM 0.692 0.688 0.858 0.724
meanFPI 0.742 0.628 0.865 0.680
meanCB 0.558 0.505 0.707 0.547
meanREP 0.710 0.732 0.727 0.944
meanRI 0.737 0.718 0.793 0.945
Note: Bold values are loadings for items that are above the recommended value 0.5
Table 2: Results of Measurement Model
Model
Construct
Measurement
Item
Cronbach
Alpha
Factors
Loading
CRa
AVEb
e-loyalty WOM 0.741 0.858 0.853 0.661
FPI 0.865
CB 0.707
e-satisfaction REP 0.880 0.944 0.943 0.893
RI 0.945
CIQ CON 0.745 0.754 0.840 0.569
CUS 0.858
CHA 0.681
INT 0.712
Trustworthiness BEN 0.871 0.902 0.921 0.796
HON 0.861
COMP 0.911
Note: a Composite Reliability (CR)=(square of the summation of the factor loadings)/{(square of the
summation of the factor loadings)+(square of the summation of the error variances)}
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b Average Variance Extracted (AVE)=(summation of the square of the factor
loadings)/{(summation of the square of the factor loadings)+(summation of the error variances)}
Table 3: Summary Results of the Model Constructs
Model Construct Measurement Item Standard estimate t-value
e-loyalty WOM 0.858 62.479
FPI 0.865 63.401
CB 0.707 18.310
e-satisfaction REP 0.944 129.295
RI 0.945 139.300
CIQ CON 0.754 31.126
CUS 0.858 63.880
CHA 0.681 21.858
INT 0.712 20.327
Trustworthiness BEN 0.902 84.411
HON 0.861 46.617
COMP 0.911 90.049
Note: Final items numbers (initial numbers)
Table 4: Discriminant Validity of Constructs
CIQ e-Loyalty e-sat Trustworthiness
CIQ 0.754
e-Loyalty 0.522 0.813
e-sat 0.666 0.805 0.945
trustworthiness 0.612 0.752 0.768 0.892
Note: Diagonals represent the square root of the average variance extracted while the other entries
represent the correlations
Assessment of Structural Model
To test path analysis and the hypotheses, bootstrapping resampling technique is used to
determine the significant t-statistic. Bootstrapping resampling technique is a statistical re-
sampling method (Kline, 2005) that determines confidence intervals (Henseler et al.,
2009).The summarized results of the structural model including path coefficients (Beta
Value), t-values, and p-values are shown in Table 5. Theoretically, a 5 percent and 1
percent significance level, with t-values: 1.96 and 2.58 respectively plays role as statistical
decision criterion. As presented in Table 5, the results summarizes that customer interface
quality was positively related to customer e-loyalty (β = 0.603; t = 15.314, p< 0.05) and e-
satisfaction (β = 0.426; t = 9.882, p< 0.05) which exceeded the recommended value, 1.96 (p<
0.05). This is supporting H1. Empirical results from the study also postulated that
trustworthiness was positively related to the customer e-loyalty (β = 0.281; t = 6.929, p< 0.05)
and e-satisfaction (β = 0.435; t = 9.698, p< 0.05). The path is significant and the coefficient is
positive. Hypothesis 2, H2, was therefore supported. According to McKinnon, Warsi, and
Dwyer (1995), the mediating effects only exist when
(a) Independent variables have a significant impact on mediator;
(b) Independent variables have a significant impact on dependent variables in the absence
of mediator;
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(c) Mediator has a significant impact on dependent variables; and lastly
(d) The effect of independent variables and dependent variables become smaller with the
existence of mediator (Ramayah, Samat, & Lo, 2011).
Based on the rule of thumb, customer interface quality had a significant impact on
trustworthiness (β = 0.781, p< 0.01) as well as trustworthiness on customer e-loyalty (β =
0.281, p< 0.01) and e-satisfaction (β = 0.435, p< 0.01). Next, the result also showed that
customer interface quality have direct effect of customer e-loyalty and e-satisfaction with
regard to H1. Eventually, H3 is proved.
Goodness of Fit (GoF) is used to measure the overall modelling fit (Tenenhaus et al.,
2005), and is defined as the geometric mean of average communality and the average of R2
endogenous LVs (Chin, 2010). The formula to calculate GoF is represented in the following
chart. The recommended value of GoFsmall= 0.1, GoFmedium= 0.25, and GoFlarge= 0.36 (Akter,
D’Ambra & Ray, 2011). In current study, GoF value was 0.575 (R2 = 0.707, average AVE =
0.661) for customer e-loyalty and 0.624 (R2
= 0.660, average AVE = 0.893) for customer e-
satisfaction. These figures show that GoF values for both customer e-loyalty and e-
satisfaction exceeded the largest cut-off value of 0.36. This is therefore to conclude that the
proposed model of this study has accurate prediction capability.
Table 5: Path Coefficient and Hypothesis Testing
H Relationship Coefficient
e-loyalty
Coefficient
e-sat
t-value
e-loyalty
t-value
e-sat
Supported
H1 CIQ e-loyalty and e-satisfaction 0.603 0.426 15.314 9.882 YES
H2 TWORTH e-loyalty and e-
satisfaction
0.281 0.435 6.929 9.698 YES
H3 CIQ trustworthiness e-loyalty
and e-satisfaction
0.781 - - - YES
Note: t-value >1.96 (p < 0.05*); t-value >2.58 (p < 0.01**)
Figure 1: Research Model with T-Value
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4.1 Discussion
This study examines the effect of customer interface quality and trustworthiness on
customer e-loyalty and e-satisfaction. From the results of simultaneous analysis, customer
interface quality (convenience, character, interactivity, and customization) and
trustworthiness related positively to customer e-loyalty and e-satisfaction in the context of
Malaysia. The findings of this study proved the importance of interface itself to build up the
strong online social network. As such, it is able to create and achieve success of the potential
B2B or B2C market in this turbulent e-marketplace. In consistence with this, Lee (2001)
highlighted that a website should enact as a promotion channel and also as an online network
building to collect the information from customer in the competitive online marketing
community (Tsao, 2005). Moreover, the results of this study was consistent with the findings
of Mehta (2005), and Chung and Shin (2008) that convenience plays a prominent role as a
motivator for the Internet shopping (Constantinides, 2004). As stated in the literature
reviewing, online consumers have few impediments, such as, no time limit, no traffic matters,
and no queues during the process of transactions via the Internet (Surjadjaja, Ghosh, &
Antony, 2003) and also have the convenience of credit cards rather than using cash (Li, Ward,
& Zhang, 2010).
Furthermore, the survey of Catalog Age’s consumer shopping behaviour found that 67
percent of the consumers were concerned with the convenience factors of online shopping
(Kau, Tang, & Ghose, 2003). In the current study, majority of the respondents were 26 to 30
years old and considered as the technology savvy group because this age group people intent
to use up-to-date technology (e.g., smart phone, iPhone, and iPad). Besides, this millionaire
generation prefer to use the Internet to make purchases rather than to visit the traditional
retailers. Thereby, these customers can save time because of ease of access, fast search, quick
transaction, and destination convenience. Consequently, as revealed by Alreck and Settle
(2002), using the Internet saves time and encourages e-buyers to purchase more (Bosnjak,
Galesic, & Tuten, 2007).
Additionally, providing good customized information supports the interactions between
service providers and e-customers and provide e-entertainment to customers (Fiore & Jin,
2003). E-service providers, who offer various interactive services to online customers, such as
providing downloadable software, electronic form inquiry, toll-free calls, and customer
feedback or comments, convince e-customers to feel it is convenient to use the online services
(Lim & Dubinsky, 2004). In addition, it enhances customers’ knowledge of the e-retailer
(Srinivasan et al., 2002) and encourage them to browse and persuade more e-customers to
participate in e-purchasing (Gehrke & Turban, 1999).
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Despite direct impact, as expected, the indirect findings disclosed that there was an
indirect effect of customer interface quality on customer e-loyalty and e-satisfaction through
trustworthiness. The reasons for this may be that the quality of interface itself naturally builds
trust over time (Egger, 2001) and drives customer e-loyalty and e-satisfaction. For instance,
the quality of website interactivity, has been found to enhance trust (Klein, 2001; Steuer,
2003), and increase the level of pleasure and excitement (Ballantine & Fortin, 2010). As
noted by Lynch, Kent, and Srinivasan (2001), trustworthiness is an important mechanism to
encourage and persuade e-consumers to shop and repurchase from the same website (Ponirin
et al., 2009). Hence, to conclude, trust plays an important role in shaping people’s perceptions
and behaviours (Setó-Pamies, 2012). According to Morrison and Firmstone (2000), trust
reduces the levels of uncertainty, manages and overcomes the different risks on Internet
(Setó-Pamies, 2012).
4.2 Implications
In terms of theory building, this study helps to develop a parsimonious model to examine
customer e-loyalty and e-satisfaction among Malaysian Internet users. With this examination
of customer interface quality and trustworthiness, it forms a knowledge base for future
researchers who wish to explore the influences on Internet user behaviour in Malaysia.
Specifically, these findings could enrich the body of knowledge of Malaysian online
consumer behaviour, online consumer choice, and online buying behaviour in terms of
preferences and frequency of visits to a particular website.
In addition to theoretical implication, the findings of the present study also provided
important practical insights to Malaysians e-retailers, e-marketers, web-designers, and e-
service providers. As reflected in the literature review of this study, marketing academicians
and professionals continue to seek the most up-to-date and significant antecedents of
customer e-loyalty and e-satisfaction. In detail, this substantial body of knowledge enables
Malaysian e-retailers to understand and address the key factors of e-commerce that help them
retain existing consumers and capture new e-consumers. In other words, these findings form
the basis for an e-business to improve their existing website in design and technical aspects.
Moreover, an ongoing issue for e-retailers and e-shoppers is the lack of direct interaction. E-
customers cannot physically touch the products or question a sales person face-to-face. In this
case, the attribute of trustworthiness plays an extremely influential role in the online shopping
environment. From an academic perspective, future researchers now would have more
knowledge on Malaysian consumer attitudes and intention behaviours towards the web
technology.
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4.3 Limitations
One of the most important limitation in our research is regarding the study sample.
Majority of respondents for this study were chosen from three selected areas in Malaysia. All
those selected for our study, have different perceptions on technology use, such as online
purchasing, from those who were not selected. As perceptions towards technology use change
over the time, the findings of the study were limited to the Internet buyers only and limited to
these specific locations. Another limitation with the sample is that the study sample lacked
diversity. The survey concentrated on the urban area only. Third limitation is projected by the
size of the sample taken. Although the sample size of 395 respondents is acceptable, there is a
need to maximize it to generate stronger generalizations. Also, this study did not control the
differences across products and services categories. The last constraint concerns the quality of
data because of the way of questionnaires answered by the respondents. Due to the limitations
of this study, there are several recommendations for further research to enhance the outcome
of future studies on customer e-loyalty and customer e-satisfaction.
5. Conclusion and Future Recommendation
To conclude, in the competitive Internet environment, Malaysian online retailers should
design an interactive customer interface to create an online and an offline competitive
advantage. Likewise, with online shopping gaining more attention and momentum, these
pertinent antecedents ultimately benefit those e-marketers and e-designers who develop new
online marketing strategies and tactics to persuade more Malaysians to shop online.
Additionally, academicians and practitioners who continue to enrich their knowledge of
customer e-loyalty and e-satisfaction predictors are in a stronger position to develop a more
comprehensive online marketing strategy model, of customer e-loyalty and e-satisfaction in
the Malaysian Internet shopping context.
References
Akter, S., D’Ambra, J., & Ray, P. (2011). Trustworthiness In mHealth Information Services:
An Assessment of Hierarchical Model with Mediating and Moderating Effects Using Partial
Least Squares (PLS). Journal of the American Society for Information Science and
Technology, 62(1), p.100-116.
Alam, M., & Khokhar, A.U.R. (2006). Impact of the Internet on Customer Loyalty in Swedish
Banks. (Master’s Thesis). Department of Business Administration and Social Science, Lulea
University of Technology.
Anderson, E.W., & Sullivan, M.W. (1993). The Antecedents and Consequences of Customer
Satisfaction for Firms. Marketing Science, 12(2), p. 125-143.
Ansari, A., & Mela, C.F. (2003) e-Customization, Journal of Marketing Research, 40(2), p.
131-145.
Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170)
2014 Vol: 1 Issue 2
131
www.globalbizresearch.org
Baron, R.M., & Kenny, D.A. (1986). The Moderator-Mediator Variable Distinction In Social
Psychological Research: Conceptual, Strategic and Statistical Considerations. Journal of
Personality and Social Psychology, 51(6), p. 1173-118.
Barroso, C., Carrión, G.C., & Roldán, J.L. (2010). Applying Maximum Likelihood and PLS
On Different Sample Sizes: Studies on SERVQUAL Model and Employee Behaviour Model.
In Handbook of Partial Least Squares: Concept, Methods, and Applications by Esposito, V.
et al., (Eds). P. 427-447.
Beaird, J. (2007). The Principles of Beautiful Web Design. SitePoint Pty. Ltd, Australia.
Bolton, R.N., & Drew, J.H. (1991) A Multistage Model of Customers’ Assessment of Service
Quality and Value. Journal of Consumer Research, 17(1), p. 375-384.
Bosnkaj, M., Galesic, M., & Tuten, T. (2007). Personality Determinants of Online Shopping:
Explaining Online Purchase Intentions Using a Hierarchical Approach. Journal of Business
Research, 60, p. 597-605.
Chang, H.H., & Chen, S.W. (2008). The Impact of Customer Interface Quality, Satisfaction,
and Switching Costs on e-Loyalty: Internet Experience As A Moderator. Computers in
Human Behaviour, 24(63), p. 2927-2944.
Cheng, J.M.S., Wang, E.S.T., Lin, J.Y.C., & Vivek, S.D. (2009). Why do customers utilize
the internet as a retailing platform? A view from customer perceived value. Asia Pacific
Journal of Marketing and Logistics, 21(1), p. 144-160.
Chin, W.W. (2010). How to write up and report PLS analyses. In Handbook of Partial Least
Squares: Concept, Methods, And Applications by Esposito, V., et al., (Eds). P. 655-690.
Chirico, P., & Presti, A.L. (2008). A customer loyalty model for services based on a
continuing relationship with the provider. Presented at Method, Models, and Information
Technologies For Decision Support Systems. University Del Salento, Lecce.
Choi, D.H., Kim, S.I., & Kim, S.H. (2000). Antecedents and behavioural consequences of
customer satisfaction on internet retail store. International Center for Electronic Commerce,
p. 9-18.
Chung, K.H., & Shin, J.I. (2008). The Relationship among e-Retailing Attributes, e-
Satisfaction, and e-Loyalty. Management Review: An International Journal, 3(1), p. 23-45.
Constantinides, E. (2004). Influencing the online consumer’s behaviour: the web experience.
Internet Research, 14(2), p. 111-126.
Cyr, D., Head, M., &Ivanov, A. (2009). Perceived interactivity leading to e-loyalty:
development of a model for cognitive-affective user responses. International Journal Human-
Computer Studies, 67, p. 850-869.
Deb, M.,& Chavali, K. (2009). A study on the significant e-trust and e-loyalty in online
banking, AIMS International Journal of Management, 3(2), p. 241-257.
Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170)
2014 Vol: 1 Issue 2
132
www.globalbizresearch.org
Dholakia, R., Zhao, M., Dholakia, N., & Fortin, D. (2000). Interactivity and revisits to
websites: A Theoretical Framework, RITIM Working Paper.
Dholakia, R.R., & Zhao, M. (2010). Retail web site interactivity: how does it influence
customer satisfaction and behavioural intentions?. International Journal of Retail &
Distribution Management, 37(10), p. 821-838.
Dholakia, U.P., & Bagozzi, R.P. (2001). Consumer behaviour in digital environments. In the
Book of Digital Marketing: Global Strategies from the World’s Leading Experts. John Wiley
& Sons, Inc.
Digital Media Across Asia. (2011). Malaysia internet penetration: Malaysian Internet users in
urban/rural area. Available from:
http://comm215.wetpaint.com/page/Malaysia+Internet+Penetration. [Accessed: 12th April
2011]
Donnelly, M. (2009). Building customer loyalty: a customer experience based approach in a
tourism Context. (Master’s Thesis). Waterford Institute of Technology, Ireland.
Egger, F.N. (2000). "Trust me, I'm an online vendor": towards a model of trust for e-
commerce system Design. In: G. Szwillus & T.Turner (Eds.), CHI2000 Extended Abstracts:
Conference on Human Factors in Computing Systems, The Hague (The Netherlands), April
1-6, 2000, 101-102.
Fiore, A.M., & Jin, H.Y. (2003). Influence of image interactivity on approach responses
towards an online retailer, Internet research: electronic networking applications and Policy,
13(1), p. 38-48.
Flavián, C., Guinaliu, M., & Gurrea, R. (2006a). The role played by perceived usability,
satisfaction and consumer trust on website loyalty. Information and Management, 43(1), p. 1-
14.
Gefen, D. (2000). e-Commerce: The Role of Familiarity and Trust. Omega, 28(6), p. 725-737.
Grönroos, C. (1990). Service Management and Marketing: Managing the Moments of Truth
in Service Competition. Lexington Books: Canada.
Hamid, N.A.A. (2008). The relative importance of trust and usable website design in building
e-loyalty Intention on internet banking. Communication of the IBIMA, 3, p. 101-113.
Helm, S., Eggert, A., & Garnefeld, I. (2010). Modeling the Impact of Corporate Reputation
on Customer Satisfaction and Loyalty Using Partial Least Squares. In Vinzi, V.E., Chin,
W.W., Henseler, J., Wang, H. (Eds), Handbook of Partial Least Squares: Concepts, Methods,
and Applications. Berlin: Springer.
Hennig-Thurau, T., Gwinner, K.P., & Gremler, D.D. (2002). Understanding relationship
marketing outcomes: an integration of relational benefits and relationship quality, Journal of
Service Research, 4(3), p. 230-247.
Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170)
2014 Vol: 1 Issue 2
133
www.globalbizresearch.org
Henseler, J., Ringle, C.M., & Sinkovics, R.R. (2009). The Use of Partial Least Squares Path
Modelling In International Marketing. Advances in International Marketing, 20, p. 277-319.
Hosmer, L.T. (1995). Trust: the connecting link between organizational theory and
philosophical ethics, Academy of Management Review, 20(2), p. 379-403.
Hou, M., & Xu, X.F. (2008). Study on the influence of service to the retailing store image-
based on the theory of halo effect, Journal of Business Economics, p. 404-408.
Jones, T.O., & Sasser, Jr, W.E. (1995). Why satisfied customers defect. Harvard Business
Review.
Kassim, N., & Ismail, S. (2009). Antecedents of customer loyalty in an e-commerce settings:
an empirical study. In the Proceedings of the Oxford Business & Economics Conference
Program.
Kassim, N., & Ismail, S. (2009). Investigating the complex drivers of loyalty in e-commerce
settings. Measuring Business Excellence, 13(1), p. 56-71.
Kate, S.T. (2009). Trustworthiness within social networking sites: a study on the intersection
of HCI and Sociology. (Master thesis).Faculty of Economic and Business at the University of
Amsterdam.
Kim, J. (2003). An integrative model of e-loyalty development process: the role of e-
satisfaction, e-trust, e-tail quality, and situational factors. (Master thesis). Yonsei University,
Seoul, Korea.
Kim. J.H., Kim, M., & Kandampully, J. (2009). Buying environment characteristics in the
context of e-service. European Journal of Marketing, 43(9/19), p. 1188-1204.
Kim, M.J., Chung, N., & Lee, C.K. (2010). The effect of perceived trust on electronic
commerce: shopping online for tourism products and services in South Korea. Tourism
Management, 30, p. 1-10.
Kim, W.J., Ma, X., & Kim, D.J. (2006). Determinants of Chinese hotel customers’ e-
satisfaction and purchase intentions. Tourism Management, 27, p. 890-900.
Ku, C.Y.F., & Liu, C. (2010). A study of consumer’s trust in privacy on electronic commerce.
In the Proceedings of the 12th International Conference on Comparative Management.
LaBarbera, P.A., & Mazursky, D. (1983). A longitudinal assessment of customer satisfaction
or dissatisfaction: the dynamics aspect of the cognitive process, Journal of Marketing
Research, 20, p. 393-404.
Liao, Y.W. (2012). Applying RFM Model to Evaluate the e-Loyalty: the Moderate Role of
Switching Cost. Business and Information, 3/5: p. 212-229.
Li, H., Ward, R., & Zhang, H. (2010). Risk, convenience, cost, and online payment choice: a
study of eBay transactions.
Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170)
2014 Vol: 1 Issue 2
134
www.globalbizresearch.org
Lii, Y.S. (2009). A model of customer e-loyalty in the online banking, Economics Bulletin,
29(2), p. 891-902.
Lim, H., & Dubinsky, A.J. (2004). Consumers’ perceptions of e-shopping characteristics: an
expectancy-value approach, Journal of Services Marketing, 18(7), p. 500-511.
Lim, Y.M., Yap, C.S., & Lau, T.C. (2010b). Response to internet advertising among
Malaysian young consumers. Cross-Cultural Communication, 6(2), p. 93-99.
Liu, D., & Payne, A. (2010). Word-of-Mouth and fundamental attribution error: external
influencing factors and a research agenda.
Montoya-Weiss, M., Voss, G.B., &Grewal, D. (2003). Determinants of online channel use
and overall satisfaction with a relational, Multichannel Service Provider. Journal of Academy
of Marketing Science, 31(4), p. 448-458.
Mutum, D., & Ghazali, E. (2010). Online Shoppers Vs Non-Shoppers: A Lifestyle Study of
Malaysian Internet Users. Available from:
file:///C:/Users/Academic/Downloads/online_shoppers_vs_non-shoppers.pdf. [Accessed from
26th April 2014].
Muylle, S., Moenaert, R., & Despontin, M. (2004). The conceptualization and empirical
validation of web user satisfaction, Information and Management, 41, p. 543-560.
Ng, J., & Matanda, M.J., (2008). Determinants of e-loyalty and customer patronage in blog-
retailing: a case Study of Retailers Using Blog Retailing Format in Singapore. In the
Proceedings of the Australia and New Zealand Marketing Academy Conference 2008 -
Marketing: Shifting the Focus from Mainstream to Offbeat, 1-3 December 2008, Australia
and New Zealand Marketing Academy (ANZMAC), Sydney NSW Australia.
Pennington, R., Wilcox, H.D., & Grover, V. (2003). The role of system trust in business-to-
business consumer transactions. Journal of Management Information Systems, 20(3), p. 197-
226.
Reibstein, D.J. (2002). What attracts customers to online stores, and what keeps them coming
back? Journal of the Academy of Marketing Science, 30, p. 465-471.
Ribbink, D., van Riel, A.C., Liljander, V., & Streukens, S. (2004). Comfort your online
customer: quality, trust, and loyalty on the Internet. Managing Service Quality, 14(5), p. 446-
456.
Ridings, C.M., Gefen, D., & Arinze, B. (2002). Some antecedents and effects of trust in
virtual communities,Journal of Strategic Information Systems, 11, p. 271-295.
Ribbink, D., van Riel, A.C., Liljander, V., & Streukens, S. (2004). Comfort your online
customer: quality, trust, and loyalty on the Internet. Managing Service Quality, 14(5), p. 446-
456.
Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170)
2014 Vol: 1 Issue 2
135
www.globalbizresearch.org
Salisbury, W.D., Pearson, R.A., Pearson, A.W., & Miller, D.W. (2001). Perceived security
and worldwide web purchase intention, Industrial Management and Data Systems, 101(4), p.
165-176.
Salman, A., & Hasim, M.S. (2011). Internet usage in a Malaysian sub-urban community: a
study of diffusion of ICT innovation. The Innovation Journal: The Public Sector Innovation
Journal, 16(2), p. 1-15.
Sam, M.F.M., &Tahir, M.N.H. (2010). Website quality and consumer online purchase
intention of air ticket, International Journal of Basic and Applied Sciences, 9(10), p. 20-25.
Seiders, K., Berry, L.L., & Gresham, L.G. (2000). Attention, retailers! How convenience is
your convenience strategy? MIT Sloan Management Review.
Sekaran, U. (2010). Research Methods for Business: A Skill-Building Approach. Fifth
Edition. New York: John Willey & Sons.
Setó-Pamies, D. (2012). Customer loyalty to service providers: examining the role of service
quality, customer satisfaction and trust. Total Quality Management & Business Excellence,
23(11), p. 1257-1271.
Srinivasan, S.S., Anderson, R., & Ponnavolu, K. (2002a). Customer loyalty in e‐commerce:
an exploration of its antecedents and consequences. Journal of Retailing, 78 (1), p. 41–50.
Srinivasan, S.S, Anderson, R., & Ponnavolu, K. (2002). Customer loyalty: an exploration of
its antecedents and consequences. Journal of Retailing, 78, p. 41-50.
Surjadjaja, H., Ghosh, S., & Antony, J. (2003). Determining and assessing the determinants of
e-service operations. Managing Service quality, 13(1), p. 39-53.
Szymanski, D.M., & Hise, R.T. (2000). e-Satisfaction: an initial examination. Journal of
Retailing, 76(3), p. 309-322.
Tarafdar, M., & Zhang J. (2008). Determinants of reach and loyalty–a study of website
performance and implications for website design. Journal of Computer Information Systems,
p. 16-24.
Tsao, H.D. (2005). The impact of customer interface and perceived security design on
customer loyalty in electric commerce. Master thesis of National Cheng Kung University.
Yang, Y., Hu, Y., & Chen, J. (2005). A web trust-inducing model for e-commerce and
empirical research. In the Proceedings of the 7th International Conference on Electronic
Commerce.
Yang, X., Zhang, X., & Wu, J. (2010). Impact of marketing efforts and customer satisfaction
on word-of-mouth: study based on mobile phone users in China. Available from
http://www.jcm.hk/uploads/soft/onlinereading/2008-1/6.pdf. [Accessed from: 21 April 2014]
Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170)
2014 Vol: 1 Issue 2
136
www.globalbizresearch.org
Ye, N., & Jia, J. (2010). Customer’s perceived service quality of internet retailing. Available
from http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01499526. [Accessed from: 26
November 2010]
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