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

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

Int. Journal of Business Science and Applied Management / Business-and-Management.org

1 INTRODUCTION

Mobile commerce, or e-commerce using mobile devices and now known as m-commerce, has become a

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

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.

2 LITERATURE REVIEW

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

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

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

Norazah Mohd Suki

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:

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:

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:

H

. Responsiveness significantly affects satisfaction with the vendor in m-commerce.

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:

H

. Brand image significantly affects satisfaction with the vendor in m-commerce.

Satisfaction

Geyskens, Steenkamp and Kumar (1999) stated that satisfaction can be raised by economic conditions (e.g.

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& Johnson, 1999; Pavlou, 2003). When performance is worse than expected, a low level of satisfaction occurs

institution in the Federal Territory of Labuan, Malaysia. It took a week to complete the data collection.

brand image) were adapted from Parasuraman et al. (1985) and Hsieh and Li (2008). The constructs for

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marketing, psychology, social sciences and information systems (Hull et al., 1991; Methlie & Nysveen, 1999;

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

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

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including the stability and equivalence of the construct (Hair, Black, Babin, Anderson, & Tatham, 2010). The

formula to calculate CR is (Σstandardized loading)

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

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

than the square root of the AVE indicating there was discriminant validity. Each AVE value is found to be more

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Table 3: Correlation between the Factors

** 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

coefficients.

important in explaining satisfaction and trust that have not been considered in this study.

Table 4: Goodness-of-fit Indices for Structural Model

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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 (β

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,

p>0.05.

Note: β = standardised beta coefficients; S.E. = standard error; C.R. = critical ratio; *p< 0.05

Norazah Mohd Suki

with two antecedents of vendor quality: responsiveness and brand image. This study asserts that users’

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

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