Decision Support Systems 53 (2012) 34–43

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Decision Support Systems

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Predicting consumer decisions to adopt mobile commerce: Cross country empiricalexamination between China and Malaysia

Alain Yee-Loong Chong a,1, Felix T.S. Chan a,⁎, Keng-Boon Ooi b,2

a Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hum, Hong Kongb Faculty of Business and Finance, University Tunku Abdul Rahman, Malaysia

⁎ Corresponding author. Tel.: +852 2766 6605.E-mail addresses: alain.chong@polyu.edu.hk (A.Y.-L.

mffchan@inet.polyu.edu.hk (F.T.S. Chan), Ooikengboon@1 Tel.: +852 6207 8699.2 Tel.: +60 124525446.

0167-9236/$ – see front matter © 2012 Elsevier B.V. Alldoi:10.1016/j.dss.2011.12.001

a b s t r a c t

a r t i c l e i n f o

Article history:Received 30 November 2010Received in revised form 25 October 2011Accepted 11 December 2011Available online 17 December 2011

Keywords:M-commerceTechnology acceptance model (TAM)Consumer behaviourHierarchical regression analysis

Advancements in wireless communications have increased the number of people using mobile devices, and haveaccelerated the growth ofmobile commerce (m-commerce). This study aims to investigate the factors that predictconsumer intention to adoptm-commerce inMalaysia and China. Thework extends the traditional technology ac-ceptance model (TAM) and diffusion of innovation (DOI) model, and includes additional variables such as trust,cost, social influence, variety of services, and control variables such as age, educational level, and gender of con-sumers. By comparing consumers from both Malaysia and China, this research is able to form a prediction modelbased on two different cultural settings. Data was collected from 172 Malaysian consumers and 222 Chinese con-sumers, and hierarchical regression analysiswas employed to test the researchmodel. The results showed that age,trust, cost, social influence, and variety of services are able to predict Malaysian consumer decisions to adoptm-commerce. Trust, cost, and social influence can be used to predict Chinese consumer decisions to adoptm-commerce. This research confirms the need to extend the traditional TAM and DOI models when study-ing technology such as m-commerce. The results from this study will be useful for telecommunication andm-commerce companies in formulating marketing strategies.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

Electronic commerce (e-commerce) has revolutionized business sinceit first gain prevalence in the 1990s. The introduction of e-commerce tobusinesses brought profound changes to the competitiveness and struc-ture of industry. An example of this would be the travel andmusic indus-tries, which have seen significant changes to their business models as aresult of e-commerce. However, with the growth ofwireless communica-tion technologies, mobile commerce (m-commerce) is now seen as thenew business model and platform that will have a similar, if not bigger,impact on the business communities and industries than e-commerce.M-commerce is an extension of e-commerce, whereby the transactionsof businesses are conducted in a mobile environment using mobile de-vices.M-commerce offers the advantages of ubiquitous access to informa-tion, anytime, anywhere. The opportunities presented by m-commerceare reflected by the growth of mobile phones. According to the Interna-tional Telecommunication Union [48], mobile subscribers will surpass 5billion users worldwide in 2010. The International TelecommunicationUnion [48] also estimates that at the end of 2010 therewill be 940 million

Chong),gmail.com (K.-B. Ooi).

rights reserved.

3G subscriptions. Gartner [46] also estimates that by 2011, 85% of hand-sets shipped globally will include browsers.

The growth of personal computers and the Internet created oppor-tunities for e-commerce in the late 1990s and early 2000s. The currentpotential of m-commerce far exceeds that of e-commerce, given the in-creasing number of people who own mobile phones. IDC [47] believesthat by 2013, the number of mobile Internet users worldwide will sur-pass 1 billion users.

Personalization is one of themain advantages offered by e-commerceand m-commerce. Mobile phones such as iPhone and Blackberry havebecome important business and personal devices for consumers. Con-sumers use their mobile phones to listen to music, watch videos, playgames, conduct business transactions, and connect to social networkingsites. The interactions between consumers and their mobile phoneshave presented opportunities for organizations to use m-commerce topersonalize services to customers. Realizing these opportunities, organi-zations have been investing in m-commerce infrastructure, services anddevices. M-commerce applications such as mobile banking usage havedoubled from 2008 to 2009 [45]. The potential of m-commerce is alsogaining attention in many developing countries. Developing countriespresent amarket which has huge potential for many telecommunicationand m-commerce service providers. China and India for example, havehuge populations, and many people in these countries own a mobilephone. China has 957 millionmobile phone subscribers, and the numberis projected to grow to approximately 1.3 billion mobile subscribers by2014 [44]. However, it should be noted that although the number of

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mobile subscribers are increasing, actual m-commerce activities inmanydeveloping countries remain low. This means that although consumersare using mobile phones to surf the Internet, listen to music, the wide-spread usage of m-commerce, where consumers conduct transactionsfor goods or services using their mobile phone, remain low [36]. Thekey question faced by telecommunication providers is how to transferthe potential ofm-commerce intofinancial profits. The adoption of inter-net technologies such as e-commerce and online banking has been stud-ied by researchers [5,9,21]. Studies on consumer decisions to adopt m-commerce however, remain sparce [36]. Among those who have studiedm-commerce adoption, these studies have examined mainly existingmodels such as TechnologyAcceptanceModel (TAM) andDiffusion of In-novation (DOI) [36]. Although these models are well established, theyhave often investigated the decisions to adopt technologies based onthe perceived ease of use, perceived benefits, and technology character-istics (e.g. compatibility, trialability). M-commerce has different charac-teristics from technologies such as e-commerce. For example, usersmight have more concern on privacy and security issues using m-commerce given that data is transferred wirelessly, thus making inter-ception of datamuch easier. Unlike e-commerce,m-commerce's servicesmight be less. This present a dilemma for many service providers, as notmany software companies might want to develop applications for m-commerce given the lack of users, and users do not want to use m-commerce as it lacks services. Given such an environment and the char-acteristics of m-commerce, it is important to develop a research modelthat extends beyond what traditional TAM and DOI can offer. Further-more, Ngai and Gunasekaran [27] recommended that studies on users’experience should be conducted as the technology infrastructure ma-tures. Developing countries such as China and Malaysia offer interestingperspectives on the study of m-commerce adoptions. The technologicalinfrastructures such as 3G, WiMax are relatively advanced in Malaysia,while young consumers in these countries are technology savvy. Howev-er, in terms of m-commerce usage, these countries are still an emergingmarket compared to countries such as the United States and Japan [36].Ngai and Gunasekaran [27], as well as Lee et al. [20], also state that al-though there are many published works on the technology side of m-commerce, there exists “very limited research on the strategies and ap-plications of m-commerce” [20,27]. Therefore the purpose of this studyis to fill the existing research gap by developing a model that predicts aconsumers’ decision to adopt m-commerce. This study will conduct across country analysis between consumers in China and Malaysia.

2. Literature review

2.1. M-commerce

Variousmanagement information system researchers have provideddifferent definitions ofm-commerce. M-commerce is often considered asubset of e-commerce [27]. It is similar to e-commerce except that thetransactions by m-commerce are through a mobile device via wirelessconnections [35]. Yang [41] defined m-commerce as transactions con-ducted through a variety of mobile equipment over a wireless telecom-munication network in a wireless environment. Others who haveoffered similar definitions of m-commerce include Wu and Wang [39],Wong and Hsu [37].

Feng et al. [8] however, stated thatm-commerce is more than an ex-tension of e-commerce. They stated that given m-commerce's differentinteraction styles, usage patterns and value chain, m-commerce pro-vides a complete new business model with its characteristics such asmobility and reachability [36]. Moshin et al. [25] and Tiwari and Buse[34] viewed m-commerce as m-business. The differences between m-commerce and m-business are that m-business does not limit itself tothe transaction of monetary values via mobile devices. Tiwari and Buse[34] argued that it is not appropriate to constraint m-commerce totransactions of monetary value, as it ignores the commercial nature ofmarketing measures and after-sales services offered by m-commerce.

Furthermore, m-commerce activities such as sending free games ormusic to usersmight not involvemonetary transactions. Using the state-ment “wireless telecommunication networks” also misleads users thatthe transactions have to be completed exclusively throughwireless tele-communication networks [34]. For this research, the termm-commercewill be used instead of m-business as it is a more common term in thefield. However, the authors agree that the definition of m-commerceshould not be limited to monetary values and wireless telecommunica-tion networks, and therefore the definition from Tiwari and Buse [34] isadopted, which states that m-commerce is “any transaction, involvingthe transfer of ownership or rights to use goods and services, which isinitiated and/or completed by using mobiles access to computer-mediated networks with the help of mobile devices”.

2.2. M-commerce in China and Malaysia

Although m-commerce is a well adopted technology in developedcountries such as Japan, Korea and theUnited States, it is still at a growingstage in many developing countries [36], [24]. M-commerce has gainedattention because of its potential to surpass the success of e-commerce.As of 2008, the number of mobile users in China has far exceeded thenumber of Internet users. There are 630 million mobile phone userswhen compared to 298 million internet users [40]. Although the poten-tial of m-commerce is high in China, Wauters [51] stated that mobile In-ternet users in China do not monetize as well as users in the UnitedStates. Mobile advertising for example, is still relatively low when com-pared to the number of mobile users. Ming et al. (2008) also found thatm-commerce is still not well accepted by Chinese consumers. However,driven by the potential of m-commerce, large companies are still puttingm-commerce at the top of their business agenda [42]. China Mobile forexample, invested 5.8 billion dollars to purchase 20% of Shanghai PudongDevelopment Bank to expand its mobile payment business. A study con-ducted by Digital Media Across Asia [43] showed that besides sendingSMS and making calls, most mobile users use their mobile phones to lis-ten tomusic, play downloaded games, and read news. This however, doesnot fully make use of m-commerce's potential. Users rarely use their mo-bile phones to purchase products or conduct mobile banking [43].

Similar to China, m-commerce is also at a growing stage in Malaysia.Most of the m-commerce activities in Malaysia are information basedrather than transaction based [47]. Users will often use their mobile de-vice to access travel directions, flight schedules, and dining guides. M-commerce is alsomainly accessed via 3G. However, although the numberofmobile phone users inMalaysia is high, 3G adoption is relatively low inMalaysia [3]. According to Malaysian Communication and MultimediaCommission (MCMC), Malaysia ranks second in Southeast Asia amongInternet penetration, behind Singapore, which is a developed nation.The internet penetration rate in Malaysia is 57.80%. However, in termsof the mobile phone penetration rate, there are 100.8 mobile phonesper 100 Malaysians [50]. Surveys from MCMC [50] showed that 84.4%of Malaysians access the internet via mobile phones through GPRS, fol-lowed by 16.5% who use 3G, and 12.1% who use WAP. Only 1.5% of theusers use EDGE. This shows that Malaysian users in general, might notbe making use of the potential of the mobile internet, which can hinderthe growth of m-commerce. GPRS is relatively slow when users needto usemore bandwidth form-commerce. Nevertheless, the total wirelesssubscribers in Malaysia are expected to reach 44.2 million in 2013 [43].

Past studies on m-commerce/mobile technologies have been con-ducted either specifically in Malaysia [36] or China [29]. In these stud-ies, adoption models were built to predict the consumer adoption ofmobile technologies. However, it is interesting to note that thesestudies have shown that the model proposed is limited to their partic-ular country, and that the models proposed should be tested in othercountries, by conducting a cross country comparison. Other studiesthat have attempted to conduct cross country comparisons includeHarris [11] and Dai and Palvia [6]. However, these studies have beenconducted either by comparing two developed countries such as

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Hong Kong and the United Kingdom [11], or a developing and devel-oped countries such as China and the United States [6]. We believethat this research adds to the growing body of literature in thestudy of m-commerce. A comparative study between China and Ma-laysia is timely because these two countries compete directly witheach other for foreign direct investments. Digital Media Across Asia[43] stated that one threat faced by Malaysian mobile companies isthe possibility of being displaced by China as a regional hub, withvendors opting to locate/relocate to China. In terms of size, thesecountries contrast each other. China has a population of 1.3 billionwhile Malaysia has a population of 28 million. Although Malaysia isan Asian country, the population's culture is influenced by both west-ern and eastern cultures. Most Malaysians speak English as a secondlanguage, and the population consists of different ethnic groupssuch as Malays, Chinese and Indians [26,23]. China however, consistsof mainly Chinese who are strongly influenced by Confucian value.Therefore it would be interesting to test if a common adoptionmodel for consumer decisions can be developed for these two coun-tries, or should m-commerce providers and application developersapply different strategies to users from different countries.

2.3. Factors affecting adoption of m-commerce

Most studies on m-commerce adoption were derived frommodelssuch as TAM and DOI. TAM is one of the most common technologyadoption models and has been applied in various studies such as e-commerce, online banking, 3G and m-commerce [5,36,2]. TAM positsthat the perceived usefulness and perceived ease of use can predictattitudes towards using a technology [7,19]. Davis [7] defined per-ceived usefulness as the degree to which a person believes that hisor her job performance would be enhanced by using a particular tech-nology. Perceived ease of use is the degree to which a person believesthat using the technology requires little effort. The dependent vari-able used in TAM is the actual usage of the technology [19]. This istypically a self reporting measure of time or frequency of using thetechnology [19].

DOI according to Rogers [30], explains “the pattern of adoption,method, and assists in predicting the successfulness of new inventionof technology” [3]. According to Rogers [30], technology diffusion willgo through the knowledge, persuasion, decision, implementation andconfirmation stages. Such multiple-stage analysis of technology diffu-sion provides insights of IT diffusion problems and how they can besolved [38]. Rogers [30] states that the diffusion of a technology canbe influenced by innovation attributes such as relative advantage, com-plexity, compatibility, trialability and observability. Relative advantageis similar to perceived usefulness in TAM, as to whether the current in-novation offersmore benefits when compared to the previousmethods.Likewise, complexity is similar to perceived ease of use in TAM. Com-patibility refers to the degree in which the innovation is consistentwith the user's past experience and values. Trialability is whetherusers are given the chance to trial use the innovation. Observability isthe degree to which an innovation's results are visible to others.

TAM and DOI are two models that are suitable in predicting users'initial adoption of a new IT (Hong et al. [15]). However, given that m-commerce has different characteristics from other technologies thatwere studied in the past, it is important to extend the TAM and DOImodels to study the prediction of m-commerce adoption [36]. Thisstudy has excluded observability and compatability variables in DOIas they are not appropriate in the study of individual consumer adop-tion of mobile technologies, as suggested in Chong et al. [3].

However, this study extends the traditional TAMandDOImodel by in-cluding additional variables such as trust, cost, social influence, and varie-ty of services. Issues such as security and privacy play an important role ininfluencing consumer's trust in adopting m-commerce. Although usersare assured by various encryptions, and anti-virus tools when conductingtheir transactions online via computers, security tools for m-commerce

remain a relatively new concept to users. Consumers also have to facethe uncertainty of conducting transactions onm-commerce without hav-ing face-to-face contactwith a shop, or having seen the physical products.When conducting transactions in m-commerce, many users will becharged, e.g. 3G connections. Pre-paid phone users might find the trans-action cost involved in using m-commerce high. As such, it would be in-teresting to investigate if costs play such a role in affecting consumerdecisions to adoptm-commerce.M-commerce is also susceptible to socialinfluence. Consumers might purchase and use certain mobile devices be-cause of the trend or influence from the media and friends. As m-commerce is still new in China and Malaysia, it would also be interestingto see if the variety of services, as well as quality of the services offered bym-commerce, is able to attract user's adoption of m-commerce. Lastly, asthe decisions to adopt m-commerce lie with consumers, it would be im-portant to include demographic profiles of users as control variables todetermine if they have any influence on adoption decisions.

3. Hypotheses development and research model

3.1. Trust

Trust is an important element which affects consumer decisions toadopt technologies such as e-commerce [14]. Trust is defined aswheth-er users arewilling to become vulnerable to them-commerce providersafter considering their characteristics (e.g. security, brand name). Trusthas been studied in existing m-commerce by Wei et al. [36]. However,in this research, the aim is to examine whether users are willing totake the risks of trusting m-commerce, even though it involves uncer-tainties such as the security and privacy risks, and the lack of face-to-face interactions with the m-commerce providers. As m-commercetransactions involve transferring data in a wireless environment, usersare exposed to higher security and privacy risks [3]. Trust on m-commerce can be influenced by the culture of the consumers. Thatcher[33] found that trust plays an important role in influencing Chinese or-ganizations’ B2B adoption. Given that it is not possible to completelyeliminate the risks of security and privacy inm-commerce, the questionto be asked here iswhether Chinese orMalaysian consumers arewillingto take the risk and trust m-commerce providers. Hsee andWeber [16]stated that Chinese are in general, unwilling to take risk. This is sup-ported by Dai and Palvia [6] who stated that Chinese consumers in gen-eral have a high uncertainty avoidance culture.Wei et al. [36] found thattrust on security and privacy have is an important determinant of Ma-laysian consumer decisions to adopt m-commerce. Dai and Palvia [6]compared whether security issues play a role in American and Chinesem-commerce adoption decisions. Unfortunately, the study removed thesecurity variable due to amethodological problem, and thuswas unableto confirm security's role in m-commerce adoption prediction. There-fore this study hypothesizes that:

H1a. Trust has a significant and positive relationship with Malaysianconsumer decisions to adopt m-commerce

H1b. Trust has a significant and positive relationship with Chineseconsumer decisions to adopt m-commerce

3.2. Cost

Wei et al. [36] stated that cost is one factor that can slow the devel-opment of m-commerce. However, one may argue that given the in-creasing purchasing power of Malaysian and Chinese consumers, theprice of mobile phones, 3G services, andm-commerce should not affecttheir adoption decisions. Although the price of m-commerce might beaffordable to consumers in general, the key question here is whetherusers view that m-commerce is worth its value. It should also benoted that most of the users of mobile phones include younger stu-dents, such as university and high school students [36]. Therefore cost

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might play an important role in the adoption of m-commerce amongthis group of younger users. Cost was found to be a significant influenceonm-commerce adoption amongMalaysian users byWei et al. [36]. Lee[49] stated that a Malaysian telecommunication company under theYTL Corporation, recognized that cost might be a factor among youngconsumers, and offered 400000 free mobile internet services to stu-dents in order to drive the adoption of 4G.

Dai and Palvia [6] in their comparative study between Chinese andAmerican m-commerce consumers found that cost affects Chineseconsumers’ adoption decision, but does not affect American con-sumers. Dai and Palvia [6] attribute this to the spending power ofAmerican consumers compared to Chinese consumers. However,both Wei et al. [36] and Dai and Palvia [6] have conducted their stud-ies on samples with users of relatively young age, e.g. majority ofusers are age 25 and under. Therefore there is a need to examine ifcost factors affect consumers in general instead of only the youngage group. Therefore this study hypothesizes that:

H2a. Cost has a significant and negative relationship with Malaysianconsumer decisions to adopt m-commerce

H2b. Cost has a significant and negative relationship with Chineseconsumer decisions to adopt m-commerce

3.3. Social influence

Social influence is the “degree to which an individual user perceivesthe importance that others believe he or she should use an innovation”[3]. Social influence has studied past technology adoption such asWAP[39], mobile Internet services [22] and 3G [3]. These studies have addedthe social influence to extend the TAM model. Influence from peers,family, and even the media such as television might influence users touse m-commerce. Many users have purchased mobile devices with 3Gcapabilities because it is portrayed as a trend by the media [3]. M-commerce also involves applications such as social networking sitesand online games, where a web community can be created [17]. Asthe users for this research are fromMalaysia and China, it would be in-teresting to test if social influence will affect users in general, or if usersfrom different cultures view social influence differently. Therefore thefollowing hypothesis is proposed:

H3a. Social influence has a significant and negative relationship withMalaysian consumer decisions to adopt m-commerce

H3b. Social influence has a significant and negative relationship withChinese consumer decisions to adopt m-commerce

3.4. Variety of services

Although m-commerce has a great potential in its applications,the current services offered to users might be limited compared toe-commerce. M-commerce offers various entertainment such as allow-ing users to use social networking tools, listen tomusic or watch videos,as well as playing mobile games [36,3]. However, in terms of providinghigher value added services such as conducting mobile banking activi-ties, purchasing on the mobile phones and mobile advertisements re-mains limited [36].

This challenge in m-commerce lies with the facts that users mightnot be willing to pay to use m-commerce due to the lack of services.Application developers on the other hand, might not be willing to in-vest in developing mobile applications if there are not enough users.So the key question to m-commerce providers and application devel-opers therefore is that if more services are being offered, would usersbe more willing to use them, or are they still hindered by other fac-tors. Chong et al. [3] in their study of 3G adoption in Malaysia foundthat having a greater variety of services will increase users’ willing-ness to adopt 3G. However, the study on the variety of services

remains too limited to draw a solid conclusion. Hence, the followinghypothesis is proposed:

H4a. Variety of services has a significant and positive relationshipwith Malaysian consumer decisions to adopt m-commerce

H4b. Variety of services has a significant and positive relationshipwith Chinese consumer decisions to adopt m-commerce

3.5. Perceived usefulness

Perceived usefulness is one of the most widely studied variables intechnology adoption [18]. Besides conducting their web activities usingm-commerce, users also have the option of using e-commerce. Activitiessuch as internet banking, reserving air tickets, playing online gamesetc. can all be conducted via both e-commerce and m-commerce. M-commerce however, offers an advantage for users in terms of not beingconstrainedbyphysical locations, and itwould be easier to carry amobilephone when compared to carrying a notebook for these activities.However, would users consider m-commerce as more useful than e-commerce, and therefore be willing to invest and adopt m-commerce?Jeyaraj et al. [18] in their meta-analysis of the predictors of IT adoption,found that perceived usefulness is one of the fivemost important predic-tors of consumer decisions to adopt an IT technology. Studies from Hsuand Lu [17], Lu et al. [22], Chong et al. [3], and Wei et al. [36] haveshown that perceived usefulness plays an important role in determiningconsumer adoption decisions on m-commerce or mobile internet activi-ties. It would therefore be worthwhile to worthwhile to investigate ifperceived usefulness has an influence on Malaysian and Chinese con-sumer adoption decision:

H5a. Perceived usefulness has a significant and positive relationshipwith Malaysian consumer decisions to adopt m-commerce

H5b. Perceived usefulness has a significant and positive relationshipwith Chinese consumer decisions to adopt m-commerce

3.6. Perceived ease of use

Similar to perceived usefulness, perceived ease of use is one of theoriginal variables found in the TAM model. The perceived ease of useof m-commerce will be different for users with different educationallevels, or age groups. Although one may argue that m-commerce appli-cations should therefore have a simple interface, sometimes this mightbe done at the expense of features and functionalities. M-commerce'sadvantage also involves personalizing the services to the users. The per-ceived ease of use has been studied in past technologies such as mobilegaming [17], 3G [3], World Wide Web [19], and Online Banking [2]. Be-sides the application features which might affect the perceived ease ofuse, physical features of mobile devices such as its small display screen,or difficulty in keying data, can also serve as a constraint to the decisionto adoptm-commerce [39]. Asm-commerce is relatively new to users inboth China and Malaysia, it would be therefore important to determineif they perceivem-commerce as easy or difficult to use, andwhether thisperception will lead to their decision to adopt or not adopt m-commerce. In this connection, the following hypotheses are proposed:

H6a. Perceived ease of use has a significant and positive relationshipwith Malaysian consumer decisions to adopt m-commerce

H6b. Perceived ease of use has a significant and positive relationshipwith Chinese consumer decisions to adopt m-commerce

3.7. Trialability

Trialability refers to whether users given free trials are more likelyto accept a new technology. Offering free trials to users also means

Table 1Demographic profile.

Demographic profile Malaysian Chinese

Number Percentage Number Percentage

GenderMale 119 69.2 88 39.6Female 53 30.8 134 60.4

Age15–18 3 1.7 10 4.519–23 107 62.2 90 40.524–28 44 25.6 69 31.129–35 9 5.2 28 12.6Above 35 9 5.2 25 11.3

Highest educational levelPrimary 3 1.7 21 9.5Secondary 8 4.7 60 27.0Diploma/polytechnic 18 10.5 50 22.5Bachelor degree 135 78.5 75 33.8Masters degree and above 8 4.7 16 7.2

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that m-commerce providers will have to bear the initial costs. This istherefore a risk that a m-commerce provider needs to take into con-sideration. In Malaysia for example, the YTL Corporation, offers400,000 free mobile internet services for students to use their 4G ser-vice. Chong et al. [3] in their study of 3G adoption in Malaysia howev-er, found that Malaysian consumers are not influenced by the freetrials given to them. Tan and Teo [31] however, found that trialabilityis a signficiant determinant of internet commerce adoption. Likewise,Brown et al. [1] also found that trialability is able to influence theadoption of cell phone banking. Given the inconsistencies in paststudies and that m-commerce has different characteristics fromother technologies, this study hypothesizes that:

H7a. Trialability has a significant and positive relationship with Ma-laysian consumer decisions to adopt m-commerce

H7b. Trialability has a significant and positive relationship with Chi-nese consumer decisions to adopt m-commerce

3.8. Control variables

One advantage ofm-commerce is its ability to personalize services tousers. Studies such as those from Chong et al. [3], Wei et al. [36] and DaiandPalvia [6] have often conducted their surveys on younger users, whoare university students. Teo [32] found that demographic variables suchas age, educational levels, and gender have a significant influence on In-ternet usage activities in Singapore. This studywill therefore include theage and educational level of the users as control variables.

3.9. Research model

Based on the literature review, a research model is developed asshown in Fig. 1. The model shows that the adoption factors (e.g. cost, so-cial influence, variety of services, perceived usefulness, perceived ease ofuse, and trialability have a significant influence on consumer decisions toadoptm-commerce. Themodel also adds the control variables of age, ed-ucation level, and race.

4. Methodology

4.1. Sample and procedure

A survey questionnaire was developed to test the hypotheses inthis study. The survey questionnaire was pre-tested with 20 Chinese

Consumer intentionto adopt m-commerce

Cost

Social Influence

Variety ofServices

PerceivedUsefulness

Perceived Easeof Use

Trialability

Control Variables

Gender, Age,Educational Level

Trust

Fig. 1. Conceptual model.

and Malaysian m-commerce users, and 2 university professors,whose main research areas are in the field of e-business. The surveywas also translated by a language instructor to ensure that the word-ing used in the surveys (e.g. Chinese and English survey) were consis-tent. In order to increase the response rate, gifts such as key chainsand t-shirts were offered as incentives to the respondents. Data wascollected at various mobile phone shops in Malaysia and China. Atotal of 222 surveys were collected from the Chinese consumers,and 172 surveys were collected from Malaysian consumers. Table 1summarizes the demographic profile of the respondents.

4.2. Variable measurement

Consistent with previous technology adoption studies, the inde-pendent and dependent variables used in this study are derivedfrom the existing literature [36]. 28 items were used to measure the7 independent variables in this study, and 3 questions were used tomeasure the dependent variables. Besides the demographic profiles,all items were measured on a 5 point Likert Scale ranging from 1(strong disagree) to 5 (strongly agree). The sample questionnaire isattached in the Appendix A.

4.3. Scale reliability and factor analysis

The reliability of the questionnaire was tested using Cronbach'salpha. The reliability coefficients (α) for both data sets (e.g. Malaysiaand China) are shown in Table 2.

The reliability coefficient (α) of all independent variables areabove 0.70, thus confirming that the items used to measure the con-structs are reliable [28,10].

In order the test the validity of the constructs, factor analysis is ap-plied. The model consists of variables from TAM, as well as additional

Table 2Reliability analysis.

Variables Malaysia China

Cronbach's alpha Cronbach's alpha

Trust 0.959 0.943Cost 0.899 0.896Social influence 0.941 0.854Variety of services 0.882 0.870Perceived usefulness 0.841 0.827Perceived ease of use 0.868 0.841Trialability 0.900 0.878

Table 3Factor analysis.

Constructs No. of items Malaysia China

Factor loadings Eigenvalues Percentage of variance Factor loadings Eigenvalues Percentage of variance

Trust 6 0.780–0.896 10.581 37.791 0.810–0.893 9.753 34.833Cost 5 0.638–0.890 2.632 9.402 0.775–0.838 3.388 12.101Social Influence 3 0.793–0.868 1.348 4.814 0.832–0.839 1.360 4.858Variety of Services 3 0.862–0.892 2.394 8.551 0.630–0.853 1.085 3.874Perceived usefulness 4 0.711–0.781 2.840 10.143 0.629–0.772 2.445 8.733Perceived ease of use 4 0.550–0.806 1.645 5.874 0.663–0.800 1.588 5.673Trialability 3 0.673–0.838 1.110 3.964 0.788–0.838 1.516 5.416

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new variables that are catered towards m-commerce adoption. Fur-thermore, some of the items adapted were not studied in Malaysiaand China. Therefore, exploratory factor analysis is applied insteadof confirmatory factor analysis [4]. Principal component analysis ischosen to evaluate if the items are linked to their underlying factors.Table 3 shows the results of the factor analysis.

Factor loading of 0.50 was used as a cut-off point as suggested byHair et al.[10]. Factors which have eigenvalues of less than one are con-sidered insignificant and are therefore disregarded [4]. Based on Table 3,all items have a factor loading of more than 0.50, thus confirming thatthese constructs are unidimensional and factorially distinct, and theitems used to operationalize the constructs are all loaded onto a singlefactor [32].

4.4. Intercorrelation matrix

An intercorrelation matrix for the demographic profiles of theconsumers and the m-commerce activities are presented in the nextsections.

4.4.1. Intercorrelation matrix for Malaysian consumersBased on Table 4, it is shown that age and educational level are

negatively correlated (r=−.294). This implies that the people whoare older in the samples have lower educational levels. The resultsshow that people with higher educational levels tend to use more

Table 4Intercorrelation matrix for Malaysian consumers.

1 2

Gender (female, male) 1 −0.0Age −0.08 1Educational level 0.08 −0.2Checking or posting advertisements −0.10 0.23⁎

Location based services—e.g. directions, maps 0.17⁎ −0.1Mobile banking 0.19⁎ 0.02Entertainment purpose (e.g. games, music, ringtones downloads −0.16⁎ 0.07Purchasing products/services (e.g. movie, airline tickets, books). 0.01 −0.0

Notes: *Pb0.05, **Pb0.01.

Table 5Intercorrelation matrix for Chinese consumers.

1 2

Gender (female, male) 1 −0.3Age −0.35⁎⁎ 1Educational level −0.34⁎⁎ 0.44Checking or posting advertisements −0.01 0.10Location based services—e.g. directions, maps 0.13 0.22Mobile banking 0.15⁎ −0.0Entertainment purpose (e.g. Games, music, ringtones downloads 0.08 0.13Purchasing products/services (e.g. movie, airline tickets, books). 0.07 0.30

Notes: *Pb0.05, **Pb0.01.

m-commerce activities such as purchasing products and services(r=0.19) and mobile banking (r=0.22). An interesting result showsthat thosewith lower educational levels tend to usem-commerce for en-tertainment activities such as downloading ring tones, games and music(r=−0.20). In terms of gender, males tend to usem-commerce for mo-bile banking (r=0.19) and location based services (r=0.17), whereasfemales on the other hand, aremore likely to usem-commerce for enter-tainment purposes (r=−0.16). In terms of age, older users are morelikely to use m-commerce to check or post advertisements.

4.4.2. Intercorrelation matrix for Chinese consumersBased on Table 5, Chinese consumers who are older have higher ed-

ucational levels (r=0.44). This is the opposite toMalaysian consumers.The result shows that consumers who are older are more likely to pur-chase products or services through m-commerce (r=0.30), and arealsomore likely to use location based services (r=0.22). In terms of ed-ucational level, thosewhohave higher level of education aremore likelyto use location based services (r=0.15) and check or post advertise-ments (r=0.15) using m-commerce.

4.5. Hypotheses testing

Hierarchical regression analysiswasused to test thehypotheses in thisresearch. The method is suitable as it allows examination of the effectsof the control variables and the independent variables separately.

3 4 5 6 7 8

8 0.08 −0.10 0.17⁎ 0.19⁎ −0.16⁎ 0.01−0.29⁎⁎ 0.23⁎⁎ −0.10 0.02 0.07 −0.02

9⁎⁎ 1 −0.13 0.11 0.22⁎⁎ −0.20⁎⁎ 0.19⁎⁎ −0.13 1 0.04 0.05 −0.04 0.020 0.11 0.04 1 0.20⁎⁎ 0.05 0.19⁎

0.22⁎⁎ 0.05 .20⁎⁎ 1 0.11 0.33⁎⁎

−0.20⁎⁎ −0.04 0.05 0.11 1 0.132 0.19⁎ 0.02 0.19⁎ 0.33⁎⁎ 0.13 1

3 4 5 6 7 8

5⁎⁎ −0.34⁎⁎ −0.01 0.13 0.15⁎ 0.08 0.070.44⁎⁎ 0.10 0.22⁎⁎ −0.07 0.13 0.30⁎⁎

⁎⁎ 1 0.15⁎ 0.15⁎ −0.11 0.11 0.21⁎⁎

0.15⁎ 1 −0.16⁎ −0.02 0.20⁎⁎ −0.20⁎⁎⁎⁎ 0.15⁎ −0.16⁎ 1 0.13 0.01 0.38⁎⁎

7 −0.11 −0.02 0.13 1 0.35⁎⁎ 0.090.11 0.20⁎⁎ 0.01 0.35⁎⁎ 1 0.05

⁎⁎ 0.21⁎⁎ −0.20⁎⁎ 0.38⁎⁎ 0.09 0.05 1

40 A.Y.-L. Chong et al. / Decision Support Systems 53 (2012) 34–43

Multicollinearity of data was tested using VIF and Tolerance. All the inde-pendent variables have VIF values of less than 10, and Tolerance values ofmore than 0.10, thus ensuring that the data did not suffer frommulticol-linearity. The dependent variable also has skewness and kurtosis valuesbetween −1.0 and +1.0, thus ensuring that the data used in the studyis normally distributed.

In step 1 of the Hierarchical regression analysis, demographic vari-ables were entered to examine their effects on the consumers’ inten-tion to adopt m-commerce. In step 2, the adoption factors are enteredand allow the examination of any increase in explained variance overthat contributed by the demographic variables [32]. The results areshown in Table 6.

Based on Table 6, demographic profiles only contributed 6% towardsthe Malaysian consumers intention to adopt m-commerce, and 3% to-wards Chinese consumers intention to adoptm-commerce (as indicatedby the adjusted R2 value). In contrast, the adoption factors in this studyare able to predict 53% of Malaysian consumer m-commerce adoptiondecisions, and 26% of Chinese consumer m-commerce adoption deci-sions. For the Malaysian consumers, older consumers are more likelyto adoptm-commerce (b=0.23) compared to young consumers. The re-sult shows the demographic profiles do not affect Chinese consumer de-cisions to adopt m-commerce.

Trust, social influence, cost and variety of services are able to sig-nificantly predict the adoption decision of Malaysian consumers.Cost has a negative standardized coefficient, which suggests thathigher costs will reduce the Malaysian consumer intention to adoptm-commerce. Therefore H1a, H2a, H3a and H4a are supported. Per-ceived usefulness, perceived ease of use, and trialability have no sig-nificant relationship with Malaysian consumers decision to adoptm-commerce, thus H5a, H6a, and H7a are rejected.

Trust, social influence, and cost have a significant relationshipwith Chinese consumer decisions to adopt m-commerce. Similar tothe Malaysian consumers, cost has a negative relationship with theirintention to adopt m-commerce. Therefore H1b, H2b, and H3b aresupported, while H4b, H5b, H6b, and H7b are rejected.

5. Discussion

This study proposes an m-commerce adoption model that wastested in both Malaysia and China. Wei et al. [36] in their study onm-commerce adoption stated that future studies should consider in-vestigating if demographic profiles of users play an important rolein predicting consumers’ intention to adopt m-commerce. The resultsshow that in Malaysia, older consumers are more likely to adopt m-commerce compared to younger consumers. This is a surprising find-ing as it is thought that younger users are more open to new technol-ogies. Given that the results also suggest that cost is a factor affectingthe adoption of m-commerce, one explanation could be that olderMalaysian users have more spending power, thus are more likely to

Table 6Hierarchical regression analysis.

Research variable Malaysia China

Step 1 Step 2 Step 1 Step 2

Gender 0.25** 0.02 −0.30 −.05Age 0.13 0.23** −0.23* −0.12Educational level −0.05 −0.06 0.17* 0.04Trust 0.28** 0.53**Cost −0.14* −0.14*Social influence 0.20** 0.45**Variety of services 0.51** −0.01Perceived usefulness 0.11 0.02Perceived ease of use −0.06 0.06Trialability −0.10 0.10Adjusted R2 0.06 0.53 0.03 0.26

Notes: *Pb0.05, **Pb0.01.

adopt m-commerce compared to younger Malaysian consumers.Young Malaysian consumers might also be more likely to use m-commerce for entertainment purposes, thus not necessarily makingfull use of m-commerce activities such as mobile banking and pur-chasing products/service from mobile phones. It is surprising thatthe use of demographic profiles is unable to predict Chinese con-sumers’ decision to adopt m-commerce.

In both Malaysia and China, perceived usefulness, perceived ease ofuse, and trialability have no significant relationships with consumer in-tention to adopt m-commerce. These three variables are derived fromthe TAM and DOI models. As stated by Jeyaraj et al. [18], these variablesare good predictors of IT usage in past studies. However, although m-commerce is a relatively new technology, Malaysian and Chinese con-sumers are no strangers to mobile phones. As discussed earlier, thereare 100.8 mobile phones per 100 Malaysians, and there are currentlyabout 630 million mobile phone users in China [50]. Therefore con-sumers from these two countries are familiar with their mobile phones,and are quite comfortable interactingwith the device. As such, whetherthe technology is easy to use will not influence their adoption decision.These users also have a good understanding of what m-commerce canoffer, and thus will not be attracted to use m-commerce based on theperceived usefulness or being given free trials. This result contradictswith the findings from Wei et al. [36], which call for m-commerce pro-viders to focus on ease of use, and promote the advantages offered bym-commerce.

Instead, this research found that social factors such as trust and so-cial influence play a significant role in m-commerce adoption by bothMalaysian and Chinese consumers. The result shows that when itcomes to trust, both Malaysian and Chinese users are generally con-cerned about the security and privacy offered by m-commerce, anddo not trust transactions that lack physical contact. This is consistentwith suggestions by Hofstede [12,13], whereby Malaysians and Chi-nese are less likely to take risks when compared to their westerncounterparts. M-commerce is a relatively new technology in thesetwo countries, and therefore more efforts are needed to increase con-sumers’ trust, which will increase m-commerce adoption.

Social influence is also a significant predictor of Malaysian andChinese consumer intention to adopt m-commerce. This finding is in-teresting as it suggests that consumers are far more likely to be influ-enced by trends, mass media, and peers when it comes to using m-commerce. This result is consistent with the finding from Wei et al.[36] where both Malaysian and Chinese consumers also based theiradoption decisions on the price of m-commerce.

One variablewhich is different betweenMalaysian and Chinese con-sumers is the variety of services. Malaysian consumers are more likelyto use m-commerce if there are more services, while for the Chineseconsumers, this is not their main concern. This finding is interesting asit suggests that Malaysian users are not satisfied with the services andapplications available to them, and increasing services and applicationswill attract them to adoptm-commerce. In China, consumers seem to becontent with the current number of m-commerce services available tothem.

6. Conclusions and implications

This research investigated the factors that can predict the adop-tion of m-commerce among Malaysian and Chinese consumers. Theresults suggest that the variables in TAM and DOI models are notable to predict consumer m-commerce adoption decisions. Instead,factors such as trust, cost, social influence, and variety of serviceshave a significant relationship with consumer decisions to adopt m-commerce. This study offers several implications.

Firstly, this is one of the few studies to offer a comparative study inm-commerce adoption in two fast growing developing countries. In-stead of comparing developed and developing countries as inmost exist-ing studies, this study focuses on developing countries where themobile

41A.Y.-L. Chong et al. / Decision Support Systems 53 (2012) 34–43

devices and infrastructure are quite advanced, but the m-commerceadoption remains low. Unlike developed countries such as Japan, Singa-pore and Korea, many developing countries’ m-commerce industriesare in a similar situation to Malaysia and China. The result allows enabletelecommunication and m-commerce companies to develop strategiesthat are appropriate to consumers from developing countries.

Secondly, the research extends the traditional TAM and DOI model,and includes new variables such as trust, social influence, variety of ser-vices, and cost to predict consumer decisions to adopt m-commerce.The results show that traditional variables found in TAM and DOI arenot sufficient to predict m-commerce adoption. Instead, m-commercedevelopers should focus on cultural elements such as trust and social in-fluence when targeting Malaysian and Chinese consumers. The compa-nies can focus on developing their marketing strategies via socialnetwork channels, as well as using word of mouth and informal semi-nars [36]. Various strategies are also needed to improve consumertrust inm-commerce.M-commerce providers should improve the secu-rity and privacy features of their systems. M-commerce providersshould also change the perceptions of their consumers by emphasizingthe safety features offered by their applications. This could involvemar-keting campaigns to educate consumers, or in creating awarenessamong them. M-commerce applications involving financial transac-tions, such as mobile banking and mobile purchasing of products,should pass the message to consumers that web security facilitiessuch as encryption, and firewalls are able to eliminate third party intru-sions [2].

Past researchers have stated that service and application providersshould focus on ensuring that m-commerce applications are userfriendly in order to attract users [3]. However, the results haveshow that with increasing user-friendliness and understanding inm-commerce, users are becoming more competent in usage, and areless worried about the perceived usefulness of m-commerce. There-fore to increase m-commerce adoptions, new strategies should be de-veloped instead of following past technology adoption studies whichhave placed an important emphasis on ease of use and usefulness ofthe IT system. This is consistent with findings from Cheng et al. [2] re-garding online banking adoption in Hong Kong.

Thirdly, the study shows that Malaysian consumers are more con-cerned with the variety of services offered by m-commerce. Thereforemore efforts should be made by m-commerce providers to offer agreater variety of applications and services.

Fourthly, the result shows both Malaysian and Chinese consumersare price conscious when it comes to m-commerce. Service providersshould therefore develop creative promotional and pricing strategies(e.g. cost reduction) to attract price-conscious customers. The resultis interesting as the cost of the 3G mobile device, as well as the 3Gservice is relatively affordable to most Malaysian and Chinese con-sumers. However, Malaysian consumers felt that the variety of serviceand cost are both important considerations in their adoption deci-sions. Therefore it is likely that for Malaysian consumers, they arenot willing to pay for m-commerce, given the current lack of varietyof services.

Fifthly, the results also show that demographic profiles of usersin general, are not good predictors of m-commerce adoptions.Although older Malaysian users are found to be more likely to adoptm-commerce, age is found to be insignificant in Chinese consumers.Therefore contrary to suggestions by Wei et al. [36], service providersshould not just focus on young consumers. As m-commerce con-sumers do not vary in terms of educational level and gender, applica-tion providers should focus on the general population instead ofspecific target groups.

Lastly, this research has contributed to the existing literature byincluding different demographic variables together with the adoptionfactors to predict m-commerce adoption in China and Malaysia. Theresult shows very similar findings in terms of consumers from bothChina and Malaysia. It has also answered calls from past researchers

such as Wei et al. [36], Teo [32] and Chong et al. [3] who have sug-gested cross-country comparison studies.

7. Limitations and future studies

There are several limitations in this study. Firstly, the study is re-stricted to comparingMalaysian and Chinese consumers. Future studiescan extend this scope to include consumers from other countries. Acomparison between various developing and developed countries willimprove the generality of the model. Secondly, as m-commerce is rela-tively new inMalaysia andChina, this study has the intention ofmeasur-ing m-commerce adoption. Future studies can consider measuring thediffusion of m-commerce across time, and investigate if the adoptionfactors change at various stages of consumer diffusion of m-commerce.Lastly, there is a possibility that additional adoption factors have notbeen included in this model, such as self efficacy and perceived enjoy-ment for example, and these can be included in future researchmodels.

Acknowledgment

The work described in this paper was substantially supported by agrant from the Hong Kong Polytechnic University PostdoctoralFellowship Scheme (Project No. G-YX4D). The authors would like tothank Hong Kong Polytechnic University Research Committee forthe financial support.

Appendix A. Questionnaire

Please indicate how often you use the following applications onmobile phones (via Internet) (1 Never, 2=Seldom, 3=Sometimes,4=Often, 5=Always)

1. Checking or posting advertisements2. Entertainment purpose (e.g. Games, music, ringtones downloads)3. Location based services—e.g. directions, maps4. Mobile banking5. Purchasing products/services (e.g. movie, airline tickets, books).

Please indicate your agreement or disagreement with the followingstatements (1=strong disagree, 2=disagree, 3=neutral, 4=agree,5=strongly agree)

Trust

1. Payments made through m-commerce will be processed securely.2. Transactions via m-commerce are secured.3. I am confident with the security measurements offered by m-

commerce websites.4. Privacy on m-commerce is well protected.5. I am not worry about providing credit card information for m-

commerce transactions6. M-commerce is as secure as any e-commerce websites.

Cost

1. Phone with m-commerce capabilities is expensive2. 3G subscription fee is too expensive for me3. M-commerce transactions are costly4. I will not use m-commerce because of its costs5. I prefer to spend money on other entertainments instead of

m-commerce

Social influence

1. Friends and family members have influence on my decision to usem-commerce

2. Mass media (e.g. TV, Radio, newspapers) will influence mydecision to use m-commerce

3. It is the current trend to use m-commerce

42 A.Y.-L. Chong et al. / Decision Support Systems 53 (2012) 34–43

Variety of services

1. The current available m-commerce services/applications are attrac-tive to me.

2. There are many m-commerce services/applications that meet myneeds.

3. Currentm-commerce services/applications are up tomy expectations.

Perceived usefulness

1. M-commerce allows me to improve my work productivity.2. M-banking is much more convenient than e-banking3. I find that m-commerce is more convenient than e-commerce4. Entertainments offered by m-commerce is better and more conve-

nient than pc based Internet.

Perceived ease of use

1. It is easy to use m-commerce2. I know how to use GPRS and 3G3. Using m-commerce requires minimum effort4. I am able to use mobile banking, connect to location based services,

purchase products/services easily.

Trialability

1. I would like to know more about m-commerce before using it2. Have free access to m-commerce applications for a month might

convince me to use it3. Trial access to 3G might convince me to subscribe to 3G and use

m-commerce

Consumer intention to adopt M-commerce

1. I will use m-commerce in the near future.2. I will purchase m-commerce enabled phones in the near future3. I am currently using m-commerce frequently

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43A.Y.-L. Chong et al. / Decision Support Systems 53 (2012) 34–43

Dr. Alain Yee-Loong Chong is currently a postdoctoral

fellow in the Department of Industrial and SystemsEngineering, The Hong Kong Polytechnic University. Hereceived his BSc and MSc from Coventry University, UK,and PhD from the Multimedia University, Malaysia. Hisresearch interests include supply chain management,e-business adoption and service science. He is a Memberof the Institute of Electrical and Electronics Engineers(IEEE).

C

Dr. Felix T. S. Chan received his BSc Degree in MechanicalEngineering from Brighton Polytechnic (now University),UK, and obtained his MSc and PhD in Manufacturing Engi-neering from the Imperial College of Science and Technol-ogy, University of London, UK. Dr Chan is now an AssociateProfessor in the Department of Industrial and SystemsEngineering, The Hong Kong Polytechnic University. Hiscurrent research interests are Logistics and Supply ChainManagement, Distribution Coordination, Systems Model-ling and Simulation, Supplier Selection. To date, he haspublished nine book chapters, over 200 refereed interna-tional journal papers and 200 peer reviewed internationalconference papers. He is a Chartered Member of the

hartered Institute of Logistics and Transport in Hong Kong.

Dr. Keng-Boon Ooi is an Associate Professor in the Faculty

of Business and Finance, University Tunku Abdul Rahman(UTAR), Malaysia. He obtained his Engineering Doctoratedegree in Engineering Business Management from Univer-sity of Technology Malaysia. His research includes areas inTotal Quality Management, Supply Chain Management, ITManagement, Human Resource Management, andKnowledge Management. Professionally, He is a Fellowof the Royal Statistical Society, UK