Int. Journal of Business Science and Applied Management, Volume 6, Issue 2, 2011
A structural model of customer satisfaction and trust in vendors
involved in mobile commerce
Norazah Mohd Suki
Labuan School of International Business & Finance, Universiti Malaysia Sabah
Jln Sg. Pagar, 87000 Labuan F.T, Sabah, Malaysia
Telephone: +6 012 - 689 1044
The purpose of this paper is to provide an explanation of factors influencing customer satisfaction and trust in
vendors involved in mobile commerce (m-commerce). The study sample consists of 200 respondents. Data were
analyzed by employing structural equation modelling (SEM) supported by AMOS 5.0 with maximum likelihood
estimation in order to test the proposed hypotheses. The proposed model was empirically tested and results
confirmed that users’ satisfaction with vendors in m-commerce was not significantly influenced by two
antecedents of the vendor’s website quality: interactivity and customisation, and also two antecedents of mobile
technology quality: usefulness and ease-of-use. Meanwhile, users’ trust towards the vendor in m-commerce is
affected by users’ satisfaction with the vendor. Interestingly, vendor quality dimensions such as responsiveness
and brand image influence customer satisfaction with vendors in m-commerce. Based on the findings, vendors
in m-commerce should focus on the factors which generate more satisfaction and trust among customers. For
vendors in general, the results can help them to better develop customer trust in m-commerce. Vendors of m-
commerce can provide a more satisfying experience for customers.
Keywords: mobile, m-commerce, trust, Satisfaction, structural equation modelling
Int. Journal of Business Science and Applied Management /
Mobile commerce, or e-commerce using mobile devices and now known as m-commerce, has become a
major topic of interest for the IS research community and a key priority for many business organizations
(Ropers, 2001). Scholars and industry representatives are turning their attention towards the promise of
electronic wireless media, envisaging that the next - or the real phase of e-commerce growth will be in the area
of mobile commerce (Bertrand, Caplan, Chab, Fernandez-Moran, & Letelier, 2001; Kalakota & Robinson, 2001;
Keen & Mackintosh, 2001; Varshney & Vetter, 2001; Varshney, Vetter, & Kalakota, 2000). Olla, Patel and
Atkinson (2003) stated that m-business is mobile Internet applications on ubiquitous mobile networks allowing
real-time, anywhere, anytime connectivity to services. This means that customers, partners and employees
should be able to access information resources and services of a company wherever and whenever they want
(Steendern, 2002).
There is growing demand in the business environment for mobility. eDigitalResearch and Portaltech (2011)
found that over one quarter (28%) of users use their Smartphone to shop, browse and research products via their
phones. The mobile commerce site of Dover Saddlery Inc., a multichannel retailer of equestrian products, has
had a dramatic increase in mobile visitors to their site. This has resulted in 3.3% of total visitors in November
2010 coming from mobile devices, 3.4% in December 2010 and 3.6% in January 2011 (Siwicki, 2011). Every
company entering the mobile marketplace has the same goal: leveraging this channel to create customer value
(Kalakota & Robinson, 2001). Prior research has identified trust as a research issue in both e- and m-commerce
(Hsu & Lu, 2005; Hsu, Lu, & Hsu, 2007; Lai, 2004; Siau & Shen, 2003). Other studies have examined a variety
of topics, including the impact of satisfaction on loyalty in m-commerce (Lin & Wang, 2006), factors affecting
satisfaction in m-commerce (Choi, Seol, Lee, Cho, & Park, 2008) and the effect of culture on satisfaction (Cyr,
Kindra, & Dash, 2008). Relevant to the studies, Li and Yeh (2009) found that the level of satisfaction is a key
determinant of gaining customer trust in m-commerce. Hence, this study aims to provide an explanation of the
factors that build customer trust towards vendors using m-commerce.
This paper is structured as follows: Section 2 presents the model employed in this study, focusing on the
rationale of the constructs used and deriving testable hypotheses. Section 3 describes the research methodology.
The next section presents the results and discussions sections. The paper rounds off with conclusions and an
agenda for future research in this area.
Perceived Quality of Mobile Services
In the marketing field Parasuraman, Zeithaml, and Berry (1985) argued that service quality is based on a
comparison between what the customer feels should be offered and what is provided. Wang and Liao (2007)
included the construct of service quality as one of the dimensions affecting customer satisfaction in m-
commerce. Lin and Wang (2006) extended SERVQUAL satisfaction with trust and perceived value, and
examined customer loyalty in m-commerce. Feng, Hoegler, and Stucky (2006) suggested that m-commerce is
more than e-commerce due to its different interaction style, usage pattern and value chain. Feng et al. (2006)
stated that m-commerce is a new and innovative business opportunity with its own unique characteristics and
functions, such as mobility and broad reach ability. Tiwari and Buse (2007) stated that m-commerce is an
integral subset of m-business since the services provided by m-business cover both commercial and non-
commercial areas. For usage-dependent contractual goods, a long-term relationship with customers is important
for service providers (Gerpott, Rams, & Schindler, 2001). For this reason customer-oriented marketing
strategies are essential for mobile service carriers to retain their customers (Kim, Park, & Jeong, 2004).
Trust is the confidence in the other’s goodwill and can also be viewed as being a consensual ideology
(Ring & Van de Ven, 1992). The benefits of a relationship based on trust are that it economises on information
and commercial transaction costs and creates the condition where exchanges between technologically and
legally separate entities can take the form of problem solving rather than bargaining (March & Simon, 1958).
Lee (2005) stressed the importance of responsiveness in leading to trust in m-commerce. Expectations are
described as the beliefs developed by the consumer relative to the characteristics of a product or service before
the purchase (Evrard, 1993). According to Siau and Shen (2003) trust in m-commerce (m-trust) can be divided
into two categories: trust in mobile technology and trust in mobile vendors. Lee and Benbasat (2003) and Chae
and Kim (2003) agreed that limited system resources (e.g. smaller screens and lower multimedia processing
capabilities) can hinder the development of trust in m-commerce.
Norazah Mohd Suki
Since there is no consensus on the nature of quality dimensions, it is necessary to identify the quality
dimensions considered important by customers in m-commerce, these include web site quality, mobile
technology quality and vendor quality.
Web Site Quality
Satisfaction in the open market depends on transaction experiences (Ha & Liu, 2010). Customers’
perception of satisfaction during the use of mobile technology is influenced by mobile business applications that
involve interactivity and customisation (Liang and Wei, 2004). Interactivity and customisation interact to
influence customers’ perceptions of satisfaction during the use of mobile technology (Liang & Wei, 2004).
Venkatesh et al. (2003) further suggested that customisation’s impact can be extended to enhance the mobile
interface design and to improve mobile usability, thus raising the level of satisfaction. Accordingly, this study
hypothesizes that:
. Interactivity significantly affects satisfaction with the vendor in m-commerce.
. Customisation significantly affects satisfaction with the vendor in m-commerce.
Mobile Technology Quality
Usefulness and ease-of-use are the two vital elements in the Technology Acceptance Model (TAM) that
influence individuals’ attitudes towards using the system (Davis, 1989). These elements were shown to be
closely related to the acceptance of computer technologies (Davis, 1989; Venkatesh & Davis, 2000) and are of
great importance for new users (Gefen & Straub, 2000). Perceived ease of use has been considered as an
important determinant in adoption of past information technologies such as intranet (Chang, 2004), 3G (Liao,
Tsou, & Huang, 2007), online banking (Guriting & Ndubisi, 2006; Jahangir & Begum, 2008), wireless Internet
(Lu, Yu, Liu, & Yao, 2003), Internet commerce (Cho, Kwon, & Lee, 2007) and m-commerce (Lin & Wang,
2005; Kurnia, Smith, & Lee, 2006; Luarn & Lin, 2005; Mallat, Rossi, Tuunainen, & Oorni, 2006; Wang &
Barnes, 2007). Based on a review of empirical evidence, usefulness and ease-of-use may positively affect
satisfaction (Ribbink, Van Riel, & Liljander, 2004). Therefore, this study posits:
. Usefulness significantly affects satisfaction with the vendor in m-commerce.
. Ease-of-use significantly affects satisfaction with the vendor in m-commerce.
Vendor Quality
Responsiveness and brand image are possible sources of perceived vendor quality. The first represent an e-
retailer’s commitment to providing rapid feedback (Dholakia, Miao, Dholakia & Fortin, 2000; Ku, 1992) or
generally refer to being responsive to the service subscribers (Heeter, 1989). Its applications can be found in
different areas of e-commerce such as web-based services (Kuo, 2003), Internet retailing (Barnes & Vidgen,
2001) and electronic banking (Zhu et al., 2002). The latter acts as a diagnostic tool for uncovering areas of
service quality strengths and shortfalls (Kettinger & Lee, 1997, 1999; Pitt, Watson, & Kavan, 1995; Van Dyke,
Kappelman & Prybutok, 1997). A high level of responsiveness, representing a trust cue, can convey the
trustworthiness of the vendor in m-commerce to customers (Corritore, Kracher, & Wiedenbeck, 2003).
Therefore, the study hypothesizes that:
. Responsiveness significantly affects satisfaction with the vendor in m-commerce.
Brand image is the other possible source of vendor quality, this involves more than just a name given to a
product. Similar classifications of brand image distinguish product-related and non-product-related attributes, as
proposed by Keller (1998) and Aaker (1997). While the former refers to the components of the core product or
function sought by customers, the latter are external to the function or process of the product or the service
provided (Keller, 1998). These two attributes can be formed from customers’ own experiences with the brand or
through the image portrayed via marketing channels (O’Cass & Grace, 2004). Geyskens, Steenkamp, Scheer,
and Kumar (1996) suggested ease of relationships with service operators can improve the level of satisfaction.
Accordingly, it seems that a strong image will lead to better customer satisfaction. Hence, the study
hypothesizes that:
. Brand image significantly affects satisfaction with the vendor in m-commerce.
Geyskens, Steenkamp and Kumar (1999) stated that satisfaction can be raised by economic conditions (e.g.
monetary benefits) or psychological factors (e.g. promise fulfilment or ease of relationship with retailers).
Int. Journal of Business Science and Applied Management /
with the Vendor
Consequently, the consumer’s post-trust level is affected directly by the level of satisfaction (Singh &
Sirdeshmukh, 2000). Past research has suggested that customer satisfaction is the antecedent of trust (Garbarino
& Johnson, 1999; Pavlou, 2003). When performance is worse than expected, a low level of satisfaction occurs
because of negative disconfirmation (Yi, 1990). When customers make transactions with the vendor, they may
have different reactions towards the transaction, thus affecting overall satisfaction (Spreng, MacKenzie &
Olshavsky, 1996). Chae, Kim, Kim & Ryu (2002) and Li & Yeh (2009) found factors affecting quality of
service in m-commerce have an impact on behavioural intention to use 3G services through improved levels of
satisfaction. Satisfaction was the fundamental performance variable affecting customer perceptions with regard
to m-commerce (Siau, Sheng, Nah & Davis, 2004; Yeh & Li, 2009). In addition, customer acceptance leads
customers to use m-commerce, subsequently customer satisfaction is built (Lee, Lee & Park 2007). The most
powerful trust emerges from positive customer experiences, whilst knowledge-based trust has the strongest
impact on customer satisfaction (Yoo, Lee & Julian Hoffmann, 2008). Hence, the study posits:
. Satisfaction significantly affects trust in the vendor in m-commerce.
Figure 1: Theoretical Framework
Two hundred questionnaires were distributed to students conveniently sampled at a higher learning
institution in the Federal Territory of Labuan, Malaysia. It took a week to complete the data collection.
Convenience sampling does have limitations, however, as the sample is not representative of the total
population. Consequently there is a constant difference between the results from the sample and the theoretical
results from the entire population. The scale items for web site quality (i.e. interactivity and customisation) were
adapted from Lee (2005) and Ribbink et al. (2004). The scale items for mobile technology quality (i.e.
usefulness and ease-of-use) were taken from Davis (1989). Items for vendor quality (i.e. responsiveness and
brand image) were adapted from Parasuraman et al. (1985) and Hsieh and Li (2008). The constructs for
satisfaction and trust were adapted from Lin & Wang (2006), Hsu et al. (2007) and Heijden et al. (2003). Hence,
the items selected represent the concepts in the empirical model under investigation and ensure the content
validity of the scales. Appendix 1 shows the detailed constructs in the proposed model. The questionnaires were
designed using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Data were
collected and analysed using structural equation modelling (SEM) supported by AMOS 5.0 with maximum
likelihood estimation in order to test the proposed hypotheses. SEM is a second-generation multivariate
Mobile Technology Quality
Vendor Quality
Vendor’s Web Site Quality
Brand Image
Customer Trust
in the Vendor
Norazah Mohd Suki
technique that combines multiple regressions with confirmatory factor analysis to estimate simultaneously a
series of interrelated dependence relationships. SEM is a widespread technique in several fields including
marketing, psychology, social sciences and information systems (Hull et al., 1991; Methlie & Nysveen, 1999;
Seibert et al., 2001).
Data Analysis
Table 1 summarizes the socio-demographic profile of the sample. There were 200 students who
participated in the survey with 82 of them males and 118 females. Eighty eight percent of the students, or 176 of
them, are between the age of 19-23 and the remaining 12 percent of the students, 24 of them, are aged between
21-24 years. The survey revealed that 196 respondents are degree pursuers and 4 of them hold Masters Degrees.
Seventy eight percent are cell phone users, 16 percent are PDA phone users and 6 percent are Smartphone users.
These technologies are being used more and more as an essential lifestyle accessory. This allows companies to
increase the number of touchpoints with their customers, and to drive increased sales in order to generate a
dramatic increase via Smartphone apps. The survey shows that 29 percent have experienced 1-3 types of m-
commerce experiences, 33 percent have experienced 4-6 types, and 38 percent have experienced more than 7
types of m-commerce.
Table 1: Socio-demographic Profile of Respondents
Highest Education Level
Wireless Handheld Equipment Type
Cell Phone
PDA Phone
Smart Phone
Number of M-Commerce Experience
1-3 years
4-6 years
> 7 years
Structural Equation Modelling
The results of SEM include two components: the measurement model and the structural model. The
measurement model, giving relationships between latent variables and observed variables, aims to provide
reliability and validity based on these variables. The structural model studies path strength and the direction of
the relationships among the latent variables.
The Measurement Model
A confirmatory factor analysis (CFA) using AMOS 5.0 was conducted to test the measurement model. It is
necessary to test that the measurement model has a satisfactory level of validity and reliability before testing for
a significant relationship in the structural model (Fornell & Larcker, 1981; Ifinedo, 2006). The psychometric
properties of the measurement model in terms of reliability, convergent validity and discriminant validity were
evaluated (see Table 2).
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Table 2: Reliability and Item Loadings
Average Variance
Ease of Use
Brand Image
Construct Reliability
Composite reliability (CR) was used to measure the reliability of a construct in the measurement model.
CR offers a more retrospective approach of overall reliability and estimates consistency of the construct itself
including the stability and equivalence of the construct (Hair, Black, Babin, Anderson, & Tatham, 2010). The
formula to calculate CR is (Σstandardized loading)
/ (Σstandardized loading)
+ Σεj) (where ε = error variance
and Σ is summation). A value of 0.70 or greater is deemed to be indicative of good scale reliability (Hair et al.,
2010). Table 2 portrays the result of the calculated composite’s reliability to support construct reliability. The
reading of composite reliability of all latent variables is above 0.70 and suggests that all latent variables have
good reliability.
Convergent Validity
Convergent validity shows the extent to which indicators of a specific construct converge or have a high
proportion of variance in common (Hair et al., 2010). This validity was measured using standardized factor
loadings. The factor loadings of latent to observed variables should be above 0.50 (Byrne, 2001, 2006; Hair et
al., 2010). The result of the confirmatory factor analysis in Table 2 shows that the standard regression weight or
standard factor loadings of all observed variables are adequate ranging from 0.660 to 0.914. This finding
indicates that the constructs conform to construct convergent validity.
Discriminant Validity
Discriminant validity shows the extent to which a construct is truly distinct from other constructs (Hair et
al., 2010). A commonly used statistical measure of discriminant validity is a comparison of the Average
Variance Extracted (AVE) value with correlation squared (Fornell & Larcker, 1981). To satisfy the
requirements of discriminant validity, the AVE of two constructs must be more than the square of the
correlation between the given two constructs. The formula to calculate discriminant validity is Variance
Extracted (VE) = (Σstandardized loadings
/ Σstandardized loadings
+ Σεj) (where ε = error variance and Σ is
summation). Table 3 demonstrates the average AVE, the latent variable correlations and the square root of the
AVE. The square root of the AVE is shown on the diagonal of the table. No correlations were equal to or greater
than the square root of the AVE indicating there was discriminant validity. Each AVE value is found to be more
Norazah Mohd Suki
than the correlation square, thus discriminant validity is supported, or in other words multicollinearlity is absent
(Byrne, 2001).
Table 3: Correlation between the Factors
(1) Interactivity
(2) Customization
(3) Responsiveness
(4) Brand image
(5) Satisfaction
(6) Trust
(7) Ease of use
(8) Usefulness
Std. Deviation
** Correlation is significant at the 0.01 level (2-tailed).
The Structural Model
The test of the structural model was performed using SEM in order to examine the hypothesized conceptual
framework by performing a simultaneous test. Table 4 depicts that the goodness-of-fit for the model was met:
Chi-square/df = 2.121, CFI = 0.945, GFI = 0.868, AGFI = 0.807, NFI = 0.902, and RMSEA = 0.075. The
overall values provided evidence of a good model fit. All of the model-fit indices exceed the respective common
acceptance levels suggested by previous research, following the suggested cut-off value, demonstrating that the
model exhibited a good fit with the data collected. Thus it was possible to proceed to examine the path
Properties of the causal paths for the structural model (standardized path coefficients ), standard error,
and hypotheses result) are signified in Table 5. The number of distinct sample moments for the model is 253.
The number of distinct parameters to be estimated is 80 and degrees of freedom is 173. The level of significance
(α) was set at 0.05. The square multiple correlation for the structural equations index connotes that the
predictors interactivity, customisation, usefulness, ease of use, responsiveness, and brand image have together
explained 79.9% of the variance in satisfaction. In other words, there are other additional variables that are
important in explaining satisfaction and trust that have not been considered in this study.
Table 4: Goodness-of-fit Indices for Structural Model
Fit Indices
Absolute Fit Measures
df (Degrees of Freedom)
Chi-square/df (χ
< 3
GFI (Goodness of Fit Index)
> 0.9
RMSEA (Root Mean Square Error of Approximation)
< 0.10
Incremental Fit Measures
AGFI (Adjusted Goodness of Fit Index)
> 0.80
NFI (Normed Fit Index)
> 0.90
CFI (Comparative Fit Index)
> 0.90
IFI (Incremental Fit Index)
> 0.90
RFI (Relative Fit Index)
> 0.90
Parsimony Fit Measures
PCFI (Parsimony Comparative of Fit Index)
> 0.50
PNFI (Parsimony Normed Fit Index)
> 0.50
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Figure 2: The Result of Proposed Research Model (Standardized Path Coefficients)
Hypotheses 1 and 2 postulate the associations between satisfaction with the vendor or the
service/application in m-commerce and two antecedents of vendor’s website quality: interactivity and
customisation. Hypotheses 3 and 4 propose the associations between satisfaction with the vendor or the
service/application in m-commerce and two antecedents of mobile technology quality: usefulness, and ease-of-
use. As evident in Table 5, satisfaction of vendors or the service/application in m-commerce were not
significantly influenced by the two antecedents of vendor’s website quality: interactivity (β
= -0.071) and
customisation (β
= 0.165). The two antecedents of mobile technology quality: usefulness
= 0.147) and ease-
of-use (β
= 0.133) also had insignificant results as p>0.05. All in all, Hypothesis 1 to 4 targeting the vendor or
the service/application, which then impacts on the vendor. Hence, the proposed hypotheses are not supported,
Table 5: Summary of Hypotheses Testing Results
Estimate (β)
Not Supported
Not Supported
Not Supported
Ease of Use
Not Supported
Brand Image
Note: β = standardised beta coefficients; S.E. = standard error; C.R. = critical ratio; *p< 0.05
Norazah Mohd Suki
Hypothesis 5 and 6 explicate the associations between users’ satisfaction with the vendor in m-commerce
with two antecedents of vendor quality: responsiveness and brand image. This study asserts that users’
satisfaction with the vendor in m-commerce includes two dimensions of vendor quality: responsiveness and
brand image. Table 5 depicts that users’ satisfaction with the vendors using m-commerce is significantly
influenced by vendor quality dimensions such as responsiveness and brand image (β
= 0.329, β
= 0.376,
p<0.05), respectively. Evidently, this study asserts that users’ satisfaction with the vendor in m-commerce (β
0.966) significantly influences trust in the vendor in m-commerce, supporting H
Accordingly, 93.3% of the
variance in trust in the vendor of m-commerce has been explained, indicating that the explanatory power of the
model may be considered satisfactory and that the model fits the data and is appropriate to test the hypothesis.
This research examines the factors that influence customer satisfaction and trust in the vendor or the
service/application of m-commerce. The study confirms that satisfaction with the vendor or the
service/application in m-commerce was not significantly influenced by two antecedents of vendor’s website
quality: interactivity and customisation, and also two antecedents of mobile technology quality: usefulness and
ease-of-use. Vendors on the mobile Internet can provide a more satisfying experience for customers by
emphasizing other m-quality factors than these four factors as it does not contributed to the formation of
satisfaction in the vendor or the service/application of m-commerce. The findings also reveal that users’ trust in
the vendor or the service/application of m-commerce is affected by their satisfaction with the vendor or the
service/application of m-commerce. Align with past studies, satisfaction is an important determinant of
customer trust (Chae, Kim, Kim & Ryu, 2002; Li & Yeh, 2009; Yeh & Li, 2009). Surprisingly, vendor quality
dimensions such as responsiveness and brand image does influence customer relationships with the vendor in m-
commerce. These were affected by vendor benevolence (brand image) and service honesty (responsiveness).
Reminiscent of previous findings (Lee, 2005; Corritore et al., 2003; Yeh & Li, 2009) the results indicated that
responsiveness did directly lead to satisfaction. This may be because m-commerce customers were more
concerned with vendor service honesty (responsiveness) (Ratnasingham & Kumar, 2000). As in the studies by
Berry (2000), Liang &Wei (2004) and Parasuraman et al. (1985), web site and vendor quality influenced
customer satisfaction. Customers should be satisfied with the vendor services in m-commerce in order to gain
trust and remain loyal to them. Moreover, this study has validated the determinants of satisfaction and trust,
leading the way for a detailed exploration of how to improve users’ satisfaction and trust in the vendor in m-
Despite the useful findings of this study, this empirical study has several limitations that need to be
acknowledged. Several factors were examined in this study. Future studies should attempt to draw profiles based
on characteristics other than these factors. It must also be mentioned that the data were collected from a 200
student sample of convenience at a higher learning institution in the Federal Territory of Labuan, Malaysia. It is
recognized that this convenience sample, given its demographic limitations, would place restrictions on the
generalization of the results of this study to other geographic areas or to the general population. Hence, future
research should expand or increase the involvement of respondents by using probability sampling techniques
such as stratified random sampling. The larger the geographic area included in this type of research, the more
representative the result will be.
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I1 I can use this mobile Internet site anywhere and anytime I need to.
I2 This mobile Internet site enables me to order products or services anywhere and anytime.
I3 This mobile Internet site offers timely and location-specific packets of information
(e.g. restaurant coupons for lunch) to me.
C1 I feel that personal needs have been met when using 3G services or making 3G transactions.
C2 3G service provides me with information and products according to my preferences.
C3 I feel that 3G services providers have the same norms and values as I have.
U1 3G service enables me to have the access to useful service.
U2 3G service enables me to use 3G service effectively.
E1 Learning to use 3G service is easy for me.
E2 It is easy for me to become skilful at using 3G service.
E3 Overall, I believe that 3G service is easy to operate.
R1 It is easy to get in contact with 3G service providers.
R2 3G service providers are interesting in feedback.
R3 3G service providers quickly reply to requests.
Brand Image
BI1 I feel that company A branded product fulfils its practical function.
BI2 I feel that company A branded product possesses a positive symbolic meaning.
BI3 I feel that company A branded product is associated with pleasant experiences.
S1 The web site provided in 3G service is successful.
S2 The web site provided in 3G service has met my expectations.
T1 Based on my experience with 3G service, I know it cares about customers.
T2 Based on my experience with 3G service, I know it is predictable.
T3 Based on my experience with 3G service, I know it knows its market.
Norazah Mohd Suki
Aaker, J.L. (1997), “Dimensions of brand personality”, Journal of Marketing Research, Vol. 34 No. 3, pp. 347-
Barnes, S.J. and Vidgen, R. (2001), “An evaluation of cyber-bookshops: the WebQual method”, International
Journal of Electronic Commerce, Vol. 6 No. 1, pp. 11-30.
Berry, L.L. (2000), “Cultivating service brand equity”, Journal of the Academy of Marketing Science, Vol. 28
No. 1, pp. 128-137.
Bertrand, V., Caplan, A., Chab, F., Fernandez-Moran, E., & Letelier, C. (2001). “MCommerce: Who Will Reap
the Profits? “ Kellogg Tech Venture 2001 Anthology, March 2001. Available at:
Byrne, B. M. (2001), “Structural equation modeling with AMOS: Basic concept application and programming”,
London: Lawrence Erlbaum Associates.
Chae, M.H. and Kim, J.W. (2003), “What’s so different about the mobile Internet”, Communications of the
ACM, Vol. 46 No. 12, pp. 240-247.
Chae, M., Kim, J., Kim, H. and Ryu, H. (2002), “Information quality for mobile internet services: a theoretical
model with empirical validation”, Electronic Markets, Vol. 12 No. 1, pp. 38-46.
Chang, P.V. (2004), “The validity of an extended technology acceptance model (TAM) for predicting
intranet/portal usage”, Master thesis, University of North Carolina, Chapel Hill, NC.
Cho, D.Y., Kwon, H.J. and Lee, H.Y. (2007), “Analysis of trust in internet and mobile commerce adoption”,
Proceedings of the 40th Hawaii International Conference on System Science, USA.
Choi, J., Seol, H., Lee, S., Cho, H. and Park, Y. (2008), “Customer satisfaction factors of mobile commerce in
Korea”, Internet Research, Vol. 18 No. 3, pp. 313-335.
Corritore, C.L., Kracher, B. and Wiedenbeck, S. (2003), “On-line trust: concepts, evolving themes, a model”,
International Journal of Human-Computer Studies, Vol. 58 No. 6, pp. 737-758.
Cyr, D., Kindra, G.S. and Dash, S. (2008), “Web site design, trust, satisfaction and e-loyalty: the Indian
experience”, Online Information Review, Vol. 32 No. 6, pp. 773-790.
Davis, F.D. (1989), “Perceived usefulness, perceived ease-of-use, and user acceptance of information
technologies”, MIS Quarterly, Vol. 13 No. 3, pp. 319-340.
Dholakia, R.R., Miao, Z., Dholakia, N. and Fortin, D.R. (2000), “Interactivity and revisits to websites: a
theoretical framework”, RITIM working paper, (online), available at: http:// (accessed
4 September 2009).
eDigitalResearch and Portaltech (2011). Half of Smartphone owners now shop online via a mobile device.
Retrieved August 16, 2011, from
Evrard, Y. (1993), “La satisfaction des consommateurs: e´tat des recherches”, Revue Francaise du Marketing,
Nos 144/145, pp. 53-65.
Feng, H., Hoegler, T. and Stucky, W. (2006), “Exploring the critical success factors for mobile commerce”,
Proceedings of the International Conference on Mobile Business (ICMB’06), Copenhagen, Denmark.
Fornell, C., & Larcker, D. (1981), “Evaluating structural equation models with unobservable variables and
measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
Garbarino, E. and Johnson, M.S. (1999), “The different roles of satisfaction, trust, and commitment in consumer
relationships”, Journal of Marketing, Vol. 63 No. 2, pp. 70-87.
Gefen, D. and Straub, D.W. (2000), “The relative importance of perceived ease of use in IS adoption: a study of
e-commerce adoption”, Journal of the Association for Information Systems, Vol. 1 No. 8, pp. 1-28.
Gerpott, T.J., Rams, W. and Schindler, A. (2001), “Customer retention, loyalty, and satisfaction in the German
mobile cellular telecommunications market”, Telecommunications Policy, Vol. 25, pp. 249-269.
Geyskens, I., Steenkamp, J. and Kumar, N. (1999), “A meta analysis of satisfaction in marketing channel
relationships”, Journal of Marketing Research, Vol. 36 No. 2, pp. 223-238.
Geyskens, I., Steenkamp, J., Scheer, L.K. and Kumar, N. (1996), “The effects of trust and interdependence on
relationship commitment: a trans-Atlantic study”, International Journal of Research in Marketing, Vol. 13
No. 4, pp. 303-317.
Guriting, P. and Ndubisi, A.O. (2006), “Borneo online banking: evaluating customer perceptions and behavioral
intention”, Management Research News, Vol. 29 Nos 1/2, pp. 6-15.
Int. Journal of Business Science and Applied Management /
Ha, S. H. and Liu, L. T. (2010), “Critical Success Factors of Open Markets on the Internet in Terms of Buyers”,
IFIP Advances in Information and Communication Technology, 341, 282-291.
Hair, J. F., Black, B., Babin, B., Anderson, R. E. and Tatham, R. L. (2010). Multivariate Data Analysis: A
Global Perspective. Pearson Education Inc., NJ.
Heeter, C. (1989), “Implication of new interactivity technologies for conceptualizing communication”, in
Salvaggio, J. and Bryant, J. (Eds), Media Use in the Information Age, Erlbaum, Hillsdale, NJ, pp. 217-235.
Hsu, C.L. and Lu, H.P. (2005), “Consumer behavior in online game communities: a motivational factor
perspective”, Computers in Human Behavior, Vol. 23 No. 3, pp. 1642-1659.
Hsu, C.L., Lu, H.P. and Hsu, H.H. (2007), “Adoption of the mobile internet: an empirical study of multimedia
message service (MMS)”, Omega, Vol. 35 No. 6, pp. 715-726.
Ifinedo, P. (2006), “Acceptance and continuance intention of web-based learining technologies (WLT) use
among university students in a Baltic country”, The Electronic Journal on Information Systems in
Developing Countries, Vol. 23 No. 6, pp. 1-20.
Jahangir, N. and Begum, N. (2008), “The role of perceived usefulness, perceived ease of use, security and
privacy, and customer attitude to engender customer adaptation in the context of electronic banking ”,
African Journal of Business Management, Vol. 2 No. 1, pp. 32-40.
Kalakota, R., and Robinson, M. (2001). “M-Business: The Race to Mobility”. New York: McGraw-Hill.
Keen, P., and Mackintosh, R. (2001). “The Freedom Economy: Gaining the M-commerce Edge in the Era of the
Wireless Internet”. Berkeley, CA: Osborne/McGraw-Hill.
Keller, K.L. (1998), Strategic Brand Management, Prentice-Hall, Upper Saddle River, NJ.
Kettinger, W.J. and Lee, C.C. (1997), “Pragmatic perspectives on the measurement of information systems
service quality”, MIS Quarterly, Vol. 21 No. 2, pp. 223-240.
Kim, M., Park, M. and Jeong, D. (2004), “The effects of customer satisfaction and switching barrier on
customer loyalty in Korean mobile telecommunication services”, Telecommunications Policy, Vol. 28, pp.
Ku, L.(1992), “Impacts of interactivity from computer-mediated communication in an organization setting: a
study of electronic mail”, doctoral dissertation, Michigan State University, East Lansing, MI. Kumar, N.
(1996), “The power of trust in manufacturer-retailer relationships”, Harvard Business Review, Vol. 74 No.
6, pp. 92-106.
Kuo, Y.F. (2003), “A study on service quality of virtual community web sites”, Total Quality Management, Vol.
14 No. 4, pp. 461-473.
Kurnia, S., Smith, S.P. and Lee, H. (2006), “Consumers’ perception of mobile internet in Australia”, e-Business
Review, Vol. 5 No. 1, pp. 19-32.
Lai, T.L. (2004), “Service quality and perceived value’s impact on satisfaction, intention and usage of short
message service (SMS)”, Information Systems Frontiers, Vol. 6 No. 4, pp. 353-368.
Lee, T.M. (2005), “The impact of perceptions of interactivity on customer trust and transaction intentions in
mobile commerce”, Journal of Electronic Commerce Research, Vol. 6 No. 3, pp. 165-180.
Lee, Y.E. and Benbasat, I. (2003), “Interface design for mobile commerce”, Communications of the ACM, Vol.
46 No. 12, pp. 49-52.
Lee, S., Lee, S. and Park, Y. (2007), “A prediction model for success of services in e-commerce using decision
tree: e-customer’s attitude towards online service”, Expert Systems with Applications, Vol. 33 No. 3, pp.
Li, Y.M. and Yeh, Y.S. (2009), “Service quality’s impact on mobile satisfaction and intention touse 3G
service”, Proceedings of the 42nd Hawaii International Conference on System Sciences, Waikoloa, IEEE
Computer Society Press, Los Alamitos, CA, pp. 1-10.
Liang, T.P. and Wei, C.P. (2004), “Introduction to the special issue: mobile commerce applications”,
International Journal of Electronic Commerce, Vol. 8 No. 3, pp. 7-17.
Liao, C.H., Tsou, C.W. and Huang, M.F. (2007), “Factors influencing the usage of 3G mobile services in
Taiwan”, Online Information Review, Vol. 31 No. 6, pp. 759-774.
Lin, H.H. and Wang, Y.S. (2006), “An examination of the determinants of customer loyalty in mobile
commerce contexts”, Information and Management, Vol. 43 No. 3, pp. 271-282.
Lin, H.H. and Wang, Y.S. (2005), “ Predicting consumer intention to use mobile commerce in Taiwan”,
Proceedings of the International Conferences on Mobile Business (ICMB’05), Sydney, Australia.
Norazah Mohd Suki
Lu, J., Yu, C.S., Liu, C. and Yao, J.E. (2003), “Technology acceptance model for wireless internet”, Internet
Research: Electronics Networking Applications and Policy, Vol. 13 No. 3, pp. 206-222.
Luarn, P. and Lin, H.H. (2005), “Towards an understanding of the behavioral intention to use mobile banking”,
Computers in Human Behavior, Vol. 21 No. 6, pp. 873-891.
Mallat, N., Rossi, M., Tuunainen, V.K. and Oorni, A. (2006), “The impact of use situation and mobility on the
acceptance of mobile ticketing services”, Proceedings of the 39th Hawaii International Conference on
System Science, USA
March, J.C., and Simon, H.A. (1958). Organizations, New York: Wiley
O’Cass, A. and Grace, D. (2004), “Exploring consumer experiences with a service brand”, Journal of Product
and Brand Management, Vol. 13 No. 4, pp. 257-268.
Olla, P., Patel, N., and Atkinson, C. (2003), "A case study of MM02's MADIC: a framework for creating mobile
internet systems", Internet Research: Electronic Networking and Policy, Vol. 13 No.4, pp. 311-21.
Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1985), “A conceptual model of service quality and its
implications for future research”, Journal of Marketing, Vol. 49 No. 4, pp. 41-50.
Pavlou, P.A. (2003), “Consumer acceptance of electronic commerce: integrating trust and risk with the
technology acceptance model”, International Journal of Electronic Commerce, Vol. 7 No. 3, pp. 101-134.
Pitt, L.F., Watson, R.T. and Kavan, C.B. (1995), “Service quality: a measure of information systems
effectiveness”, MIS Quarterly, Vol. 19 No. 2, pp. 173-187.
Ribbink, D., Van Riel, A.C.R. and Liljander, V. (2004), “Comfort your online customer: quality, trust and
loyalty on the internet”, Managing Service Quality, Vol. 14 No. 6, pp. 446-456.
Ring, P., and Van de Ven, A. (1994), "Developmental processes of cooperative inter-organisational
relationships", Academy Management Review, Vol. 19 pp. 90-119.
Ropers, S. (2001). “New Business Models for the Mobile Revolution”. eAI Journal, February, pp. 53-57.
Siau, K. and Shen, Z. (2003), “Building customer trust in mobile commerce”, Communications of the ACM,
Vol. 46 No. 4, pp. 91-94.
Siau, K., Sheng, H., Nah, F. and Davis, S. (2004), “A qualitative investigation on consumer trust in mobile
commerce”, International Journal of Electronic Business, Vol. 2 No. 3, pp. 283-300.
Simon, S. (2008). Definitions of correlation. Children’s Mercy. Retrieved February 19, 2011, from
Singh, J. and Sirdeshmukh, D. (2000), “Agency and trust mechanisms in consumer satisfaction and loyalty
judgements”, Journal of the Academy of Marketing Science, Vol. 28 No. 1, pp. 150-167.
Siwicki, B. (2011). Dover Saddlery trots into mobile commerce. Retrieved August 16, 2011, from
Spreng, R.A., MacKenzie, S.B. and Olshavsky, R.W. (1996), “A reexamination of the determinants of consumer
satisfaction”, Journal of Marketing, Vol. 60 No. 3, pp. 15-32.
Steendern, M. (2002), "Business applications of WAP", The Electronic Library, Vol. 20 No.3, pp. 215-223.
Tiwari, R. and Buse, S. (2007), The Mobile Commerce Prospects: A Strategic Analysis of Opportunities in
Banking Sector, Hamburg University Press, Hamburg.
Van Dyke, T.P., Kappelman, L.A. and Prybutok, V.R. (1997), “Measuring information systems service quality:
concerns on the use of the SERVQUAL questionnaire”, MIS Quarterly, Vol. 21 No. 2, pp. 195-208.
Varshney, U., and Vetter, R. (2001). “A Framework for the Emerging Mobile Commerce Applications”,
Proceedings of the 34th Hawaii International Conference on System Sciences (HICSS-34), January 3-6,
2001, Maui, Hawaii. Los Alamitos, CA: IEEE Computer Society Press.
Varshney, U., Vetter, R.J., and Kalakota, R. (2000). Mobile Commerce: A New Frontier, Computer, Vol. 33,
No. 10 (October), pp. 32-38.
Venkatesh, V. and Davis, F.D. (2000), “A theoretical extension of the technology acceptance model: four
longitudinal field studies”, Management Science, Vol. 46 No. 2, pp. 186-204.
Venkatesh, V., Ramesh, V. and Massey, A.P. (2003), “Understanding usability in mobile commerce”,
Communications of the ACM, Vol. 46 No. 12, pp. 53-56.
Vrechopoulos, A.P., O’Keefe, R.M., Doukidis, G.I. and Siomkos, G.J. (2004), “Virtual store layout: an
experimental comparison in the context of grocery retail”, Journal of Retailing, Vol. 80 No. 1, pp. 13-22
Wang, S. and Barnes, S. (2007), “Exploring the acceptance of mobile auctions in China”, Proceedings of the
Sixth International Conference on the Management of Mobile Business, Toronto, Canada.
Int. Journal of Business Science and Applied Management /
Wang, Y.S. and Liao, Y.W. (2007), “The conceptualization and measurement of m-commerce user satisfaction”,
Computers in Human Behavior, Vol. 23 No. 1, pp. 381-398.
Yeh, Y. S. and Li, Y. M. (2009), “Building trust in m-commerce: Contributions from quality and satisfaction”,
Online Information Review, Vol. 33 No. 6, pp. 1066-1086.
Yi, Y. (1990), “A critical review of consumer satisfaction”, in Zeithaml, V.A. (Ed.), Review of Marketing,
American Marketing Association, Chicago, IL, pp. 68-123.
Yoo, J. S., Lee, J. N and Hoffmann, J. (2008), “Trust in Online Shopping: The Korean Student Experience”,
Proceedings of the 41st Hawaii International Conference on System Sciences.
Zhu, F.X., Wymer, W. and Chen, I. (2002), “IT-based services and service quality in consumer banking”,
International Journal of Service Industry Management, Vol. 13 No. 1, pp. 69-90.