e at SciVerse ScienceDirect

Technology in Society 33 (2011) 271–283

Contents lists availabl

Technology in Society

journal homepage: www.elsevier .com/locate/ techsoc

Factors affecting camera mobile phone adoption beforee-shopping in the Arab world

Kamel Rouibah a, Hasan Abbas a,*, Samia Rouibah a,b

aKuwait University, Shuwaikh, KuwaitbGulf University for Science & Technology, Kuwait

a r t i c l e i n f o

Article history:Received 21 June 2011Received in revised form 29 September 2011Accepted 7 October 2011

Keywords:ArabTechnologyArab technology adoptionKuwait technology adoptionMobile phoneCameraE-commerceSocial normsImageIslamic society and technology

* Corresponding author. Tel.: þ965 6640 0686.E-mail address: hasabba@gmail.com (H. Abbas).

0160-791X/$ – see front matter � 2011 Elsevier Ltddoi:10.1016/j.techsoc.2011.10.001

a b s t r a c t

With the seamless inclusion of a camera-mobile phone (CMP) as a regular part of people’severyday life, businesses have tried to explore their potential as an e-commerce tool. Evenwithout the Internet, CMP is a new technology that is in widespread use in the State ofKuwait. This study reports on the findings of a study exploring the intention to use andacceptance use of CMP prior to retail purchases using the second technology acceptancemodel (TAM 2) in a collectivism culture where social group affects individual decisions.Based on a sample of 151 users in Kuwait, the study reveals that TAM 2 provides limitedresults. The intention to use CMP is only determined by subjective norms, ease of use andcamera usefulness. Surprisingly, the study results revealed that the contribution of socialnorms on intention to use is weak and less than that of usefulness. Among externalvariables (image, job relevancy, output quality, and result demonstrability), only jobrelevancy exerts an indirect effect on the intention to use via the "usefulness" componentof this model.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction and at the same time focused on different issues (e.g. see

The number of mobile phones in use throughout theworld is now more than 4.6 billion (CBS News, 2010) andthe International Telecommunication Union is expectingthat mobile application downloads will reach 48 billion in2015 (ITU, 2011). With the widespread use of these tech-nologies, studies focusing on mobile device adoption at theindividual and organizational level have notably increased.A number of past studies have focused on different servicesthat mobile devices offer including: short message services[15,59], games [6,49], the digital camera [57,60], m-internet[27], mobile commerce [6,20,46,62], mobile payment[6,58], and m-government [22].

In addition, the number of studies dedicated to mobiledevices and mobile commerce (m-commerce) increased

. All rights reserved.

literature review done by Ngai and Gunasekaran [46].Venkatesh, and Ramesh [72] studied the profiles of mobileusers and the patterns for their use of different mobileservices). Hung et al. [23] studied the effect of subjectivenorms on consumer intentions for using mobile phones. Luet al. [39] studied the effects of facilitating conditions onthe intentions of using wireless mobile internet servicedevices. Cheong and Park [9] developed a modified Tech-nology Acceptance Model (TAM) for mobile internetacceptance in Korea. Nysveen et al. [49] investigated theintentions to use mobile services (text messaging, contactslist, payment, and games). Meso et al. [44] studied factorsaffecting mobile phone use in Africa. Yi et al. [76] studiedfactors affecting PDA adoption. Lin and Wang [35] exam-ined determinants of customer loyalty in m-commerce.Abdul Karim et al. [1] examined mobile phone applicationsin Malaysia. Au and Kauffman (2007) examined andanalyzed the different relevant stakeholders that have animpact on mobile payment. Rouibah and Abbas [60]

K. Rouibah et al. / Technology in Society 33 (2011) 271–283272

proposed a model for camera mobile phone adoption inKuwait. Rouibah and Abbas [57] studied the effect ofpersonal innovativeness, attachment motivation and socialnorms on the acceptance of camera mobile phones, whileRouibah [58] investigated the antecedents of mobilepayment in Kuwait.

Among mobile phone services, the camera mobilephone (CMP) receives rare attention even though it iswidely used. News agencies rely on CMP because of itsavailability at event sites almost anywhere and at any time.The British Broadcasting Corporation (BBC) recentlydistributed CMP devices to its reporters and producers tocapture sudden events, such as the occasion of the deadlybus accident in Wales when a CMP holder arrived at thescene ahead of traditional cameras [74].

1.1. Research question and significance

Among mobile services, this paper focuses solely on theintention to use the camera feature of mobile phones(hereafter CMP) before shopping. Several models werestudied for appropriateness as a framework, including theTechnology Acceptance Model (TAM) and TAM 2. We firstnoted that TAM 2 has not been critically analyzed as muchas the original TAM. Furthermore, TAM 2 was more appli-cable to the cultural nuances that might play a role in theuse and adoption of this unique feature of the mobilephone. The research question is the following: Does theTAM2 model account for technology acceptance or is therea need for more specification?

This study aims to assess the applicability and vali-dation of a universal model (TAM 2) in a new culturalcontext. Furthermore this study explores the applicabilityof this model to the Arab setting for e-commercepurposes. This context has been little explored in terms oftechnology acceptance and use, yet mobile technologyadoption has significant socio-economic and politicalimplications. Furthermore, in this paper we hope to shedlight on three unique changes in Kuwait that are influ-enced by and influence this technology use and adoption.First, the opportunities of m-commerce, which are flour-ishing without relying on Internet technology (mobilephones can transmit pictures via SMS). Second, with theincrease used of ICT, family members in Arab societies arespending little time socializing and even less timeshopping together. With the increase of multi-services inmobile phones, it is possible that one member of thefamily shops alone and may request the opinion ofa friend or other important person for effective shoppingusing the functionalities of CMP. Third, this study aimsalso to shed light on the effect of culture on the intentionto use a technology in a conservative Arab and Islamicsociety. By using TAM 2 as a theoretical framework foranalysis, this paper will explore the cultural motivationalfactors for the intention to use CMP before shopping.

Green et al. [16] stated (p. 146) that “. until recentlythere has been little social science research on the con-sumption and use of mobile technologies and it is onlyrecently that industry has begun to broaden its views of themobile consumer to include deeper understanding of users’behavior”. Along the same context, Urbaczewski et al. [70]

and Pedersen et al. [53] both pointed out that the maindrivers for adoptingm-commerce services remains unclear.

The significance of this study, thus, is twofold:academic/theoretical and economic/business. Practitionersand scholarsgainvaluable insights on thedriving forcesofm-commerce adoption and acceptance. Secondly, the massdevelopments of the Internet and related technologies ledcompanies and consumers to various new opportunities fortransaction, providing firms with new ways to conductbusiness and exchange/communication information throughthe development and broadening use of the Internet(e-commerce and m-commerce) and mobile communica-tions systems.

It is important to point out that e-commerce behaviorin the Arab region has been a component of past studies.However, the focus of this study is directed toward thesocial factors that affect shoppers’ behaviors andtendencies while shopping. In order to select the besttheoretical considerations, this study will focus on twoprimary considerations. First, we will review sometheories and research pertaining to the characteristics ofshopping behaviors and tendencies in the Arab region.And second the TAM 2 model will focus mainly on socialfactors which are driven by unique characteristics of theArab culture.

1.2. Literature review: the social factors describing consumerbehavior in Arab countries

Through their research in the State of Qatar, Jamal et al.[26] found that there is a variety of motivational factors thatdrive shopping in Qatar’s malls and department stores. Theproposed research model they built found that the mostimportant factors that drive consumers to shop in Arabcountries are, social shopping, gratification seeking highquality seeking, value seeking, brand loyal, utilitarianshopping, and hedonic shopping. Their findings confirmedresults of a previous study [66] that found Arabian womengo out and do social shopping because of their specificcultural context: Their lengthy free time during morningsand when their husband are working.

Based upon [42] human motivation theory, the socialshopping of consumers encompasses a consumer’stendency to be altruistic, cohesive, and seeking acceptanceand affection in interpersonal relationships [5,43,66,73].Despite their professional daily workload, Arab indivi-duals continue to take care of their household and familymembers. McAdams [41] claimed that Arab shoppersappear to be highly sociable, seeking friendships, solidaritywith groups, and in some occasions seeking the opportu-nity for engaging and establishing relationships.

From the above mentioned studies, one can observe theimportance of social factors in the Arab culture. A socialfactor canbe any variable that is socially driven and is criticalamong the studied group of people in the society. In thispaper, we incorporated social factors as the two followingconstructs: social norms and image. Unlike previous studiesthat investigated different factors that drive individuals touse new technology, this study will put more focus on therole played by these two variables that affect individuals’relationships and their decision to use CMP.

K. Rouibah et al. / Technology in Society 33 (2011) 271–283 273

2. Research background and research model

2.1. Models of IT/ICT adoption

Previous studies investigated new technology adoptionfrom different perspectives including attitudinal, social,and control factors. Although each of these theoriescontributed with different perspectives, they aimed atexploring and explaining how users perceive, adopt, anduse new technologies.

Psychologists focus on the theory of reasoned action(TRA) advocating that social pressure in the form ofsubjective norm is an important factor influencing thebehavior [13]. Information systems scholars concentratedtheir research using the Technology Acceptance Model(TAM) to predict how to use the technologies [12]. TAMassumes that instrumental and cognitive complexity(usefulness and ease of use) are the prime purposesbehind using technology. Initiated by TAM, the researchersproposed numerous extensions and developments, inclu-ding TAMversion 2 [71]. Marketing scholars predominantlybuilt on the innovation diffusion theory (IDT) to understandhow innovation diffuses and incorporates into the market-place [56]. IDT lists five characteristics of an innovation thathave key influences on acceptance behavior. These arerelative advantage, complexity, compatibility, triability, andobservability. Furthermore, two studies [45,69] reviewedprevious studies on IDT and found that only three innova-tion characteristics (relative advantage, complexity, andcompatibility) consistently related to adoption behavior.The relative advantage is similar to the notion of PU, andcomplexity is similar to PEOU,while compatibility is similarto job relevancy in TAM 2. Thus, components of IDT areincluded in TAM 2. This was proven by results of a previousstudy [61]. This paper selects TAM 2 for testing the appli-cability of intention to use CMP before shopping. Besidesease of use and usefulness of technology, TAM 2 assumesthat subjective norm, image, job relevance, output quality,and result demonstrability are also determinants of inten-tion to use.

2.2. Past e-commerce and m-commerce studies

Many researchers studied and explored influentialfactors on customer acceptance of e-commerce technolo-gies, which vary from social to technical and further toeconomical. The following table (Table 1) summaries ourreview of the available literature of e-commerce (m-commerce) with the application of the TAM 2 theorymodel.

2.3. Past studies in technology acceptance model 2

TAM 2 studies and research in the Arab world are rarecompared to Western countries. In the search for newpublications and studies in the area of e-commerce or m-commerce that adopt TAM 2 as theoretical background inthe Arab world, we found very few. However, there aresomeminor studies that adopted TAM in other fields in thiscultural context. For example, Selim [64] used the TAMmodel to explore website technology in the field of

education and learning. He conducted his study over 450students in University of United Arab Emirates and foundthat the instructors and higher education institutionsshould focus on the course websites according to his TAMresults.

Another study by Al-Gahtani et al. [3] applied theunified theory of acceptance and use of technology(UTAUT) in an Arabian environment. An important note byAl-Gahtani et al. [3] regarding the UTAUT model is thattheir model has not been validated thoroughly in any non-Eastern cultures and societies. This is why they used SaudiArabia as a field study to apply their research model. Theirstudy used a survey sample that collected data froma voluntary 722 participants who are knowledge workersusing desktop computer applications. The research teamexamined the relative power of an extended version ofUTAUT in determining ‘intention to use’ and ‘usagebehavior’. What the study found was that their model wasable to explain 39.1% of intention to use variance, and 42.1%of usage variance. Additionally, the team also hypothesizedand tested the similarities and variations between the twodifferent cultures of North America and Saudi in terms ofcultural and social differences that affected the IT accep-tance in the organization among the two societies [3].

However, there are plenty of TAM 2 studies in areasother than Arab areas. Chan and Lu [7] investigate adoptionand use behavior within the context of the Internet Bankingin Hong Kong. Their survey involved a total of 499 univer-sity students they found that user’s ability to use computersplays significant/indirect role in influencing both intentionsto adopt and continue use of Internet Banking. Their studyalso found that Risk perceptions hindered the adoption ofInternet Banking.

Al-Gahtani et al. [3] used theunified theoryof acceptanceand use of technology (UTAUT) to measure the user accep-tance of IT in Saudi Arabia. The same theorymodel used alsoin another study administered byGupta et al. [17] to explorethe adoption of ICT to enhance government-to-employeeinteractions in a government organization in India.

Stern et al. [65] used the extended TAM model byapplying it in the context of online auctions. The studyintroduced three new variables: affinity with the computer,impulsiveness, and risk tolerance. The groupdiscovered thatthe TAM is stable andgoodenoughacross contexts andmoreuser variables help explain the acceptance of consumertechnology use.

Al-Somali et al. [4] adopted and extended TAM modelfor online banking in Saudi Arabia. They administereda survey with sample size of 400 customers. The aim oftheir study was to identify the factors that encouragecustomers to adopt online banking in Saudi Kingdom.Their study results were important and significant fore-commerce practitioners and particularly for e-banking inparticular. They found that increasing availability ofbroadband connections throughout the country would leadto greater adoption of online banking. Their finding alsoshowed that the current situation, which includes a lack ofsolid infrastructure, played an important part in limitingadoption rates by Saudi customers.

According to our literature review, we noted that theArab world lacks research in technology acceptance.

Table 1Summary of past literature review.

Authors Objectives Theorymodel

Technology Sample Findings

[10] Factors influencem-satisfaction andm-loyalty

DecisionTree (DT)

Digital musicservice

300 S. Korean high schooland university studentsbetween ages 15–24

The two factors “contentreliability and “transactionprocess” significantlysupported hypotheses ofthe m-satisfaction model

[47] Adopting mobile devicein m-commerce services

ExtendedTAM

Mobile 542 Dutch consumers Social factors exert importantinfluence on people’s decisionto adopt advanced mobileservices.

[19] Adopting new technology ExtendedTAM

Virtual communityand new technologies

30 virtual communitymembers

The influence of social tiesplay an important role indetermining the acceptanceand usage behavior of newadopters of new informationtechnologies, specificallya virtual community.

[21] Measure customer loyaltythru perceived enjoyment,social norms, and perceivedcohesion.

TRA andmodifiedTAM

Entertainment andonline game

356 online gamers Perceived cohesion, extrinsicmotivation, and social normhave a significant effect inshaping consumers’ behavior.

[40] 1. To what extent individualperceptions toward WIMT isattributed to social influences,2. To what extent individualperceptions of WIMT is attributed to internal motivations;3. Whether internalization ofsocial influences and personaltendency to try affect potentialusers intention to adopt WIMT;and4. Whether behavioral beliefssuch as perceived usefulnessand perceived ease of use haveprimary explanatory powerover user intention to adopWIMT.

TAM2 Wireless Internetservices via mobiletechnology (WIMT)

350 MBA American studentsin a regional university inTexas.

Social networks and imagehave significant influence onboth usefulness and ease ofuse. Personal innovativenessin information technologyhas significant effect onboth PU and PEOU, andneither social influences norPIIT exerts any importantdirectly impact on adoptionof WIMT is non-work related.

[55] Explain the differencesbetween individual studentsin the level of acceptance anduse of a virtual learningenvironment (VLE).

ExtendedTAM

Virtual LearningEnvironment (VLE)

45 Chinese in ExecutiveMBA program

Perceived usefulness hasa direct effect on VLE use,PEOU and subjective normhave only indirect effectsvia PU. Both personalinnovativeness and computeranxiety have direct effectson PEOU only.

[27] To assess the causaldeterminants of customers’acceptance of airlinebusiness-to-customer(B2C) eCommerce websites(AB2CEWS).

ExtendedTAM

B2C eCommerce inairline business

495 customers withexperience in usingB2C eCommerce directlyfrom airline companywebsites.

PEOU and PU and attitudetoward the use of AB2CEWSare predictors of customers’behavioral intention to reuseAB2CEWS, and subjectivenorms and eTrust are bothsignificant and meaningfulin the customers’ acceptanceof AB2CEWS.

[29] To examine whethermisperceptions of socialnorms of mobileadvertising play any rolein predicting consumers’responses to mobileadvertising

ExtendedTAM

Mobile advertising 343 college studentsfrom large southwesternpublic university of USA

Some types of misperceptions(pluralistic ignorance) wereuseful predictors of consumers’PEOU of mobile advertising, butfalse uniqueness and falseconsensus didn’t predict PEOU.Unexpectedly the study didn’tfound PEOU to be a predictorof attitude towards mobileadvertising.

K. Rouibah et al. / Technology in Society 33 (2011) 271–283274

Table 1 (continued )

Authors Objectives Theorymodel

Technology Sample Findings

[78] To explain three objectives:1. propose and verify a TAMspecific to t-commerce,2. identify the factors thatdetermine consumers’adoption of it, and3. compare the differencein factors influencing itsadoption by experiencedand inexperienced users.

ExtendedTAM

t-commerce is aninteractive serviceson television thatallow it to become ane-commerce mediummerging video, voice,and transactional datain what is called thetelevision commerce(t-commerce).

947 experienced users and115 inexperienced users ofthe technology.

PEOU was found to have directinfluence on PU, and it affectedon potential user’s attitudetoward t-commerce. Forexperienced users, perceivedenjoyment, PU, PEOU, and trusthad positive influence onattitude, and perceivedenjoyment (PE) had thegreatestimpact. PE, PU, trust, SN, andattitude positively affectedbehavioral intention (BI), butattitude has the greatestinfluence.For inexperienced users PE andPU positively affected attitude,and attitude and SN had animpact on BI.

[75] Measure whether usersembrace the m-commerceacceptance and usage.

ExtendedTAM

M-commerce in thisstudy means eitherdirect or indirect,with a monetaryvalue implementedvia a wirelesstelecommunicationnetwork.

310 from the B2C MC users,where users invoked oneof four general onlinetransactions: onlinebanking, shopping,investing, and onlineservices for Personal notBusiness uses.

1. User adoption of MC can bepredicted adequately fromuser’s intention which affectedsignificantly by perceived risk,cost, compatibility, and PU,2. PEOU doesn’t directlyinfluence behavioral intentionbut indirectly affects behavioralintention to use through PU,3. Compatibility has mostimportant effect on behavioralintention to use and the secondmost important effect on theactual use,4. cost is one of the importantpredictors of MC adoptionintent, and5. perceived risk has a significant direct impact on behav-ioralintention to use.

[30] Presents the results of anempirical study investigatingWeb-based negotiationsupport systems (NSS, froma user acceptance perspectiveand utilizing a technologyacceptance model.

ExtendedTAM

Web-based negotiationsupport systems(NSS)

174 individuals at 35companies, all of whom wereinvolved in work requiringnegotiations. These includedsenior managers inprocurement, supply chainmanagement, outsourcing,production, and sales.

Playfulness, causality, and SNhave been shown to havea positive effect on theintentionto user NSS, through their effecton PEOU or PU.

K. Rouibah et al. / Technology in Society 33 (2011) 271–283 275

Although the research of technology acceptance has flour-ished for a long time ago in the Western and Easternsocieties, the Arab World in general and the Gulf areascientists specifically, did not give much attention toacceptance theories. The next section explores technologyimportance and penetration in the Six Persian Gulf coun-tries. It particularly shows technology acceptance in Kuwaitamidst the context of the Persian Gulf countries and formore universal comparison of the results of this paper.

2.4. State of ICT in Kuwait

Since Kuwait is the focus of this study, the followingtable (Table 2) shows some statistics and penetration rates.Kuwait has achieved high IT/ICT penetration rates amongstthe Gulf Cooperation Countries (GCC) (see Table 2) in terms

of PC, Internet, and mobile phone penetration. As seen inTable 2, the GDP per person in the State of Kuwait isconsidered the 2nd largest ($31,860) after the State of Qatarin this region. In terms of utilizing ICT, Kuwait is ranked inthe third position among GCC countries after UAE and afterQatar. Kuwait’s economic showed remarkable growth andconsiderably strong with an annual real GDP growth rate of8.2% after UAE (8.5%) and Qatar (8.4%). This growth rate ispredicted to continue increase by 5.4% [24].

Kuwait holds an advanced and well developed ICTinfrastructure and the Kuwaiti market carries the latestmobile devices. For example, in the banking sector, all banksin Kuwait offer online and mobile banking services. Bankscontinuously contact their customers and offer them wire-less alerts about changes in their account informationthrough their registered digital cell phones. Bank customers

Table 2Basic ICT indicators in the GCC countries.

Indicator Year Kuwait Bahrain Oman Qatar Saudi Arabia UAE

Population (thousands) 2000 2190 672.0 2442.0 606.4 20660.7 32472005 2535.4 726.6 2566.9 812.8 23118.9 4533.12010 2737 1262.0 2782 1759 27448 7512

GDP per capita ($) 2000 17222 11861 8135. 29290 9120 217402005 31860.6 17773 9460 52239 13399 286112009 41365 17609 17280 61532 13901 33183

PC penetration rate 2000 11.41 14.13 3.30 14.85 6.29 12.312005 23.66 16.65 4.60 16.36 36.66 18.75

Mobile penetration rate 2000 21.73 30.61 6.63 19.93 6.65 43.982005 93.86 100.00 51.93 88.17 57.53 100.002009 110.9 120.00 138.0 151.6 175.0 204.0

Internet penetration rate 2000 6.85 5.95 3.68 4.95 2.23 23.52005 27.6 21.3 11.10 26.9 6.86 30.822011 42.4 53.5 48.4 66.5 43.6 69.0

Source: World Development Indicator, 2011.

K. Rouibah et al. / Technology in Society 33 (2011) 271–283276

can use their mobile phone or Internet for variety of trans-actions such as: checking account balances, transferringfunds between savings and other accounts, paying mobilephone and Internet fees charges, purchasing prepaid cardsfor online shopping, and selling and buying stock marketactions.

2.5. Research model

The model we developed in this study is depicted inFig. 1, which is based on TAM 2 of Venkatesh and Davis [71].It has been selected because it depicts both cognitiveinstrumental determinants (ease of use and usefulness; jobrelevance, output quality, results demonstrability) as wellas social influence factors (social norms and image) whichboth affect intention to use.

The study hypotheses are outlined in Fig. 1. The ex-pected type of association is stated for each of the 11hypotheses. These associations were hypothesized basedon the study of Venkatesh and Davis [71].

H8

H2

H1

H3

H4

H5

H5

H7

PEOU

PUImage

Subjectivenorm

Jobrelevance

Outputquality

Resultsdemonstrability

H3

H1

H2

H4

H5

H6

H7

H8

Fig. 1. Research

2.5.1. The effect of social variables: subjective norm (SN) andimage

Arab culture is highly social and family-oriented. It isscored high on Hofstede’s collectivist dimension [18]. Arabvalues put more emphasis on the group than on the indi-vidual. In Arab countries, people live in societies wherefamily and friendship remain important and influentialfactors in the functioning of institutions and groups.Accordingly, the social values and patterns of relationshipall focused on the primary group (family, tribes, andfriends) toward whom primary loyalty and responsibilitiesof the individual are essentially directed. Accordingly, thegroups have a strong effect on the social norms that dictatethe behavior of Arab people, who in turn maintain theirimage among the group to whom they identify themselves.

In line with this idea, Loch et al. [37] found that socialnorms explain 47% of about Internet usage in theArabworld.A past study has found that Arab people like to maintainhigh social interaction and personalization of relationshipsusing instantmessaging [59,61]. The subjective norm (SN) is

H9

H10

Intention to use

H10

H9

model.

K. Rouibah et al. / Technology in Society 33 (2011) 271–283 277

derived from theory of reasoned action (TRA). The subjec-tive norm is considered as a determinant of the behavioralintention (BI), which was included in the modified tech-nology acceptance model (TAM2) by Venkatesh and Davis[71]. It refers to the perception of a person of how he thinksabout what the important people to him think he should orshould not perform in accordance to a behavior underquestion. According to the direct effect of the subjectivenorm, users normallymaychange and adopt a behavior thatcomplies with the group which they identify themselveswith. The direct compliance effect of the subjective norm isthe case when an individual perceives that a social actorwantshim toperforma certain behavior, and the social actorhas the ability to reward the behavior or punish it in case ofits absence [71].

Previous studies found a positive link between subjec-tive norm and both the construct image (IM) and themediating variable perceived usefulness (PU) [63,71,76,80];with perceived enjoyment [59]; and with actual behavior[23,37,50,59,61,63]. Thus, we hypothesize:

� H1: Subjective norm is positively associated with theperceived usefulness.

� H2: Subjective norm is positively associated with theimage.

� H3: Subjective norm is positively associated with theindividual level of CMP usage.

Image has been derived from the diffusion of innova-tions theory. Moore and Benbasat [45] (p. 195) definesimage as "the degree to which use of an innovation isperceived to enhance one’s status in a social system".Venkatesh and Davis [71] theorizes that subjective norm(SN) affects positively image and also image will influencepositively the use. Thus we hypothesize:

� H3: Image is positively associated with the perceivedusefulness.

2.5.2. Job relevance (JR)The construct job relevance (JR) is deducted from the

action theory, work motivation theory, and behavioraldecision theoryandbeenaddedbyVenkateshandDavis [71]into the extended technology acceptance model (TAM2).Venkatesh and Davis [71] (p. 191) defines job relevance “asan individual’s perception regarding thedegree towhich thetarget system is applicable to his or her job. In other words,job relevance is a function of the importance within one’sjob of the set of tasks the system is capable of supporting.”The studies found that job relevance positively influencesperceived usefulness [28,71]. Thus we hypothesize that:

� H5: Job relevance (JR) is positively associated withperceived usefulness.

2.5.3. Output quality (OQ)The output quality (OQ) is defined by Venkatesh and

Davis [71] as “people will take into consideration how wellthe system performs those tasks” (p. 191). The previous

studies [51,71,79] state that output quality is positivelyassociated with perceived usefulness (PU). Thus, wehypothesize that:

� H6: Output quality (OQ) is positively associated withperceived usefulness.

2.5.4. Results demonstrability (RD)Moore and Benbasat [45] define results demonstrability

(RD) as the “tangibility of the results of using innovation”[45] (p. 203). The studies show that result demonstrabilitypositively influences perceived usefulness [31,71]. Thus wehypothesize that:

� H7: results demonstrability (RD) has a positive effect onperceived usefulness.

2.5.5. Perceived usefulness (PU) perceived ease of use (PEOU)These two constructs are central to the original accep-

tance model (TAM). Many past studies have proven thatthese two constructs are determinants of intention to usefor different technologies [32]. PU refers to the extent towhich a person believes that using a technology wouldhave a value for his tasks. PEOU, on the other hand, refers tothe extent to which a person believes that by usinga technology they will be free from any mental effort [57].In accordance with other studies of technology acceptancemodel [14,71], we hypothesize the two following:

� H8: perceived ease of use (PEOU) positively effectsperceived usefulness.

� H9: Perceived ease of use positively effects the intentionto use CMP.

� H10: Perceived usefulness positively effects the inten-tion to use CMP.

As discussed previously, past studies [37] have foundthat technology adoption is usefulness driven in individu-alistic societies, whereas it is socially driven in collectivismcultures [59], thus we expect that the social norms hasmore effect on intention to use CMP than usefulness:

� H11: The effect of social norms on intention to use CMPwill be stronger than that of perceived usefulness.

3. Research methodology

3.1. Sample and procedure for data collection

This study used the instrument of Venketesh and Davis[71]. After instrument translation and verification, 200questionnaires distributed amongst students in the Collegeof Business Administration at Kuwait University. However,only 151 returned and filled in the surveys completely.The survey consisted of 25 questions, each representingcomponents of the research model and 4 additionalcomponents representing demographic data (gender, age,GPA, and type of used service of mobile phone). The

Table 3Respondent’s demographics.

Respondents demographics Number Percentage

GenderMale 23 15.2Female 128 84.8AgeLess than 17 8 5.418–25 134 90.5Above 25 6 4.1GPABelow 1 12 8<1-2� 21 13.9>2-3� 67 44.4>3 51 33.8Used servicesCamera mobile phone (Yes) 47 31.3Camera mobile phone (No) 103 68.7Bluetooth (Yes) 101 57.3Bluetooth (No) 49 32.7SMS (Yes) 123 82SMS (No) 27 18MMS (Yes) 31 20.7MMS (No) 119 79.3

K. Rouibah et al. / Technology in Society 33 (2011) 271–283278

questionnaire circulated to students soliciting their beliefsand attitudes regarding the use of CMP. Students contrib-uting in this survey enrolled in the course Introduction toManagement Information Systems and were introduced tothe subject of m-commerce. Students were given thequestionnaire upon completion of a chapter dedicated to e-commerce and m-commerce. They were first introduced tothe services of m-commerce followed by a discussion of thepotential application of CMP to support m-commerce. Thestudy chose students precisely because of two reasons.First, it is a convenient and appropriate sample to testacceptance and adoption of new technology (see Ref. [32]);and second, students are the heaviest CMP users amongvarious sectors in the local mobile market.

3.2. Measures

Subjective norm refers to a normative belief that peopleattribute relevance to what others expect them to do, aswell as their motivation to comply with those beliefs. It wasmeasured by two items of Venkatesh and Davis [71]: “Myfriends who influence my behavior encourage me to useCMP before shopping (SN1). Members of my familywho areimportant tome encourageme to use CMP before shopping(SN2).” Two additional items were added to the model. Oneis related to the effect of the lecturer since the study isconducted at a university setting and a fourth item takenfrom Ref. [44]. These items were: “My lecturer who isimportant to me encourages me to use CMP before shop-ping (SN3), and the society (tradition) influences me to useCMP before shopping (SN4).”

Perceived usefulness refers the prospective adopter’ssubjective probability that applying the new technologywill bebeneficial to the individual who will realize a perceived utilityfrom it, andperceived ease of use refers to the degree towhichthe prospective adopter expects the new technology to be freeof effort regarding its transfer and utilization. PU is measuredby four items of Davis’s original scales [12]: CMP is useful fortaking pictures before shopping (PU1); CMP improves myperformance before shopping (PU2); CMP enhances my effec-tiveness before shopping (PU3); and CMP increases myproductivity before shopping (PU4) and an additional itemCMPenablesme to consultmypartners before shopping (PU5).

Perceived ease of use (PEOU) refers to degree to whichthe prospective adopter expects the technology adoption to befree of effort during use. Davis measures PEOU by four itemsof Davis’s original scales [12]: Interacting with a CMP beforeshopping does not require a lot of my mental effort (PEOU1); Ifind CMP before shopping easy to use (PEOU2); and I find CMPbefore shopping flexible to interact with (PEOU3), and I find iteasy to get the CMP do what I want it to do (PEOU4).

Results demonstrability refers to the tangibility of theresults of using an information technology [45]. Venkateshand Davis [71] measure result demonstrability by fouritems: I would have no difficulty telling others about theresults of using a CMP before shopping (RD1); I believe I couldcommunicate to others the consequences of using a CMPbefore shopping (RD2); The results of using a CMP beforeshopping are apparent to me (RD3); I would have difficultyexplaining why using CMP before shopping may or may not bebeneficial (RD4).

The literature defines output quality as howwell the newtechnology performs tasks undertaken by the user [71]. Twoitems to measure the output quality are [71]: The quality ofthe output I get from CMP before shopping is high (OQ1); and Ihave no problem with the quality of the output I get from CMPbefore shopping (OQ2).

Image refers to the extent to which the use of an inno-vation is perceived as an enhancement of one’s status in thesocial system [45]. Venkatesh and Davis [71] measure imageby three items: People around me who use CMP have beforeshopping more prestige than those who do not (IM1); Peoplewho use CMP before shopping have a high profile (IM2); UsingCMP before shopping is considered a status symbol among myfriends (IM 3).

Relevance for e-commerce refers to an individual’sperception regarding the degree to which the target tech-nology is applicable to his or her task or job [71]. It is similarto the compatibility of Moore and Benbasat [45]. Venkateshand Davis [71] measure relevance by two items of : Beforeshopping, the use of a CMP is relevant for me (JR1); and Beforeshopping, the use of a CMP is important for me (JR2).

Intention to usewas measured by two customized itemsof Davis [12]: I intend to use CMP before shopping in thenext sixmonths (INT 1); The next sixmonths I intend to useCMP frequently before shopping (INT2).

4. Results & discussion

4.1. Demographic data and behavior of respondents with CMP

Table 3 presents descriptive statistics for respondents’gender, age, student’s GPA and type of services offered bymobile phone. In our sample of 151 respondents, there areslightly more female (84.5%) students than male (15.2%),and most of them (90.5%) belong to the range of agesbetween 18 and 25 years. 78.2% of respondents have theirGPA above 2. Among mobile phone services, 82% use SMS,which makes it at the top and most popular service,

Table 5Regression results explaining TAM 2.

This study [71]

TAMR2 b

1. SN–BI 33.4% 0.33** (t ¼ 4.68) 0.44**2. PU–BI 0.38** (t ¼ 5.42) 0.55**3. PEOU-BI 0.25** (t ¼ 3.48) 0.17**

Predicting PU R2

4. SN-PU 0.44.9% 0.45** (t ¼ 5.65) �0.47***5-PEOU-PU 0.11ns (t ¼ 1.43) 0.30***6-OQ-PU 0.05ns (t ¼ 0.70) 0.40***7-RD-PU 0.09ns (t ¼ 1.17) 0.28***8-Image-PU 10ns (t ¼ 1.64) 0.21***9-JR-PU 0.31** (t ¼ 4.10) 0.40***

Predicting Image10. SN-Image 0.36** (t ¼ 4.6) 0.49***

Significance level: *<0.05; **<0.01; ***<0.05.

K. Rouibah et al. / Technology in Society 33 (2011) 271–283 279

followed by Bluetooth (57.3%). Furthermore, one out ofthree respondents reported using CMP (31.3%).

4.2. Factor analysis

We coded and entered the 151 questionnaires into thestatistical software SPSS and conducted factor analysis toconfirm the relevance of the existing variables. The analysisidentified a total of eight factors with Eigen values greaterthan 1.0. These factors explained 74.25 of the total variance.

The measurement scales showed high reliability, withCronbach alpha coefficients for the eight variablesexceeding 0.70 (see Table 4), meaning the instrument ishighly reliable and valid [48]. Table 4 shows that all theeight variables had mean values higher than 2.5, except forsubjective norm and image. This result indicates that onaverage most respondents agreed to the items set in thequestionnaire. However, subjective norm had a mean valueof 2.22 signifying neutral or neither agree or disagree.

4.3. Results of regression analyses

After the factor analysis, we carried out a regressionanalysis to determine the relationship between the sixconstructs. All three constructs (image, perceived use, andbehavioral intention) we successively passed the threerounds of regression analysis.

Table 5 indicates that social norms have a strong impacton image (a¼ 0.36, t¼ 4.6) leading to the acceptance of H2.Among the hypothesized determinants of PU, only socialnorms (b ¼ 0.45, t ¼ 5.65) and job relevance (b ¼ 0.31,t ¼ 4.10) are significant, meaning that only two hypotheses,HI and H5 are supported. Four hypotheses (H4, H6, H7, and

Table 4Scale Items, Cronbach Alpha, Mean and Standard Deviation.

Variables Numberof items

Factoranalysis

Cronbach’sAlpha

Mean SD

Perceived EaseOf Use (PEOU)

4 PEOU1: 0.78 0.871 4.04 1.03PEOU2: 0.82PEOU3: 0.81PEOU4: 0.80

ResultDemonstrability(RD)

4 RD1: 0.70 0.833 3.65 1.13RD2: 0.68RD3: 0.71RD4: 0.67

Output Quality(OQ)

2 OQ1: 0.47 0.701 3.33 1.22OQ2: 0.73

Intention to Use(INT

2 INT1: 0.83 0.901 3.03 1.43INT2: 0.82

PerceivedUsefulness (PU)

5 PU1: 0.53 0.858 2.96 1.39PU2: 0.64PU3: 0.65PU4: 0.72PU5: 0.67

Job Relevancy (JR) 2 JR1: 0.82 0.899 2.93 1.27JR2: 0.82

Subjective Norm(SN)

4 SN1: 0.73 0.739 2.22 1.32SN2: 0.80SN3: 0.44SN4:0.69

Image (IM) 3 IM1: 0.82 0.745 2.07 1.15IM2: 0.83IM3: 0.71

H8) are rejected. The path from PEOU to PU is not signifi-cant (b ¼ 0.45, t ¼ 5.65) meaning that this result contrastswith most previous studies related to TAM findings.

Table 5 also indicates that the three hypothesized pathsbetween SN (b ¼ 0.33, t ¼ 4.68), PEOU (b ¼ 0.25, t ¼ 3.48),and PU (b ¼ 0.38, t ¼ 5.42) have a positive, direct effect onbehavioral intention to adopt CMP before shopping. Thusour model supports the hypotheses H3, H9 and H10.

Contrary to our expectation, ourmodel does not supportH11 since the effect on PU is stronger than that of socialnorms. Results also reveal that TAM 2 explains 33.4% of thevariation in the behavioral intention to use CMP beforeshopping Fig. 2.

4.4. Discussion

Based on the obtained results, it can be seen that theapplication of TAM 2 regarding the intention to use CMPbefore shopping in Kuwait provides different results thanthose of Venkatesh and Davis [71]; who themselves cautionagainst applying theory models originated in westernculture to others. More specifically, our results contrastwith that of Venkatesh and Davis [71] who found that thesix variables (subjective norms, image, job relevance,output quality, result demonstrability and ease of use) havean influence on system usefulness. Among the six deter-minants of PU, only subjective norms and job relevancewere found to be significant in our study. The same resultwas reached by Schepers and Wetzels (2006) who foundthat subjective norm is the second most significant deter-minant of PU. These combined results are contrary to Chanet al. [7] who found no such significance. In addition,among these two variables, subjective norm positivelyexerts the most important influence on PU, while Ven-katesh and Davis [71] found that it exerts a strong butnegative effect.

The absence of any relationship between PEOU and PUcontrasts with most prior TAM studies, as discussed in ourliterature review on TAM models [32,77] as well aswith TAM 2 [71]. For example, after analyzing 95 TAMstudies, [77] found PEOU had a higher impact on PU for

Intention to Use

R 2 =33.4%

PEOU

PU R 2 =44.9% Image

R 2 =12.3%

Subjective norm

Job relevance

Output quality

ResultsDemonstrability

0.36**

0.10 ns

0.40**

0.31**

0.05 ns 0.11 ns

0.10 ns 0.25**

0.33**

0.38**

Coefficients: *** significant at <0.001; ** significant at <0.01; *significant at <0.05

Fig. 2. Final research model with standardized coefficients.

K. Rouibah et al. / Technology in Society 33 (2011) 271–283280

communication and general purpose systems as opposed tooffice and specialized systems, which contrast to our find-ings. The same is said in a study by Chan et al. [7] whofound the second highest impact overall between theirconstructs belongs to PEOU over PU. Another study whichfound the significant impact of PEOU over PU is the workof Wu and Wang [75]. One possible reason for suchdifference is related to the target studied technology: CMPfor e-shopping purposes in our study compare to commu-nication and general systems. This indicates not onlycultural constraints in this theory but also technology/system constraints.

With regard to intention to use, and similar to Ven-katesh and Davis [71], our findings reveal that usefulness,ease of use, and social norms all have a positive influenceon intention to use. Moreover, the findings of our studyrevealed that PU exerts the strongest effect on intention touse, which corroborate the results of Venkatesh and Davis[71]. Also the study by Schpers andWetzels [63] had similarfindings. They found that among the four constructs(subjective norm, PU, PEOU, and attitude toward use) PUwas the most significant effect over intention to use. Thisresult validates the findings of Wu and Wang [75]. Takinginto account that a CMP is a communication tool to easeshopping, the results of our study contrast with previousstudies since Yousafzai et al. [77] found that PU is weaklycorrelated for communication systems, and stronglycorrelated to intentions for specialized systems. Along thesame lines, Kim et al. [27] concluded in their e-tourismresearch, the same result and found that PU has the leastsignificant impact among the other three constructs, whichare subjective norm, attitude toward use, and eTrust.

Finally, our model explained 33.4% of variance in inten-tion to use and 44.9% in usefulness of CMP against 44% in

intention to use and 60% in usefulness for the study ofVenkatesh and Davis [71]. Results of our study compare tothoseVenkatesh andDavis [71] and should be interpreted inlight of the following: (i) the type of respondents (studentsvs. employees), type of technology (camera mobile phonevs. automated systems), and setting (university vs. profitorganization).

5. Conclusion

This study proposed a revised TAM model trying toexplain more of the intention to use construct and find themost significant factors. The following subsections high-light the contributions and the limitations of our study tomanagerial and research perspectives.

5.1. Contributions

This research applies TAM 2 to new technology not yettested before (camera mobile phone before shopping) ina collectivist culture that valued group relations. It is a rarestudy in this field. Furthermore, our study sheds light onthe technology and its applications in the Arab world toother scholars and Western researchers and introduceshow new technologies are perceived. The main resultsindicate that TAM 2 partially explains the behavioralintention to use the CMP, since the explained variance ofintention to use is low compared to previous studies. Suchresults signify a need for caution when applying theoriesoriginated inWestern culture to other cultures. Intention touse CMP is only determined by subjective norms, ease ofuse and camera usefulness. Also, quite opposite to ourexpectation, the study results revealed that the contribu-tion of social norm on intention to use is weak and less than

K. Rouibah et al. / Technology in Society 33 (2011) 271–283 281

that of usefulness. In addition, three variables (socialnorms, usefulness, and ease of use) have a significant effecton users’ behavioral intention to use CMP before shopping.

5.2. Limitations and future studies

This study has two limitations. It uses young students asrespondents, which is debated in the management infor-mation system field because of the possible unique natureof this demographic. Second, the sample size is small,which means that the results may not be largely general-izable. Third, many studies have been published latelycriticizing the performance of TAM model [33,63] andpromoting instead other theories such as ExpectationConfirmation Model (ECM), Cognitive Model (COG), andTechnology Continuance Theory (TCT).

Moreover, the sample is homogenous, meaning that allsubjects are from one similar group. They are in there earlytwenties and all are College of Business Administrationstudents. Even though students constitute a significantpercentage of mobile users in Kuwait, the study mayexpose more meaningful and useful results if different agecategories with variety of backgrounds included. In addi-tion, there are many other constructs that can be added toour model that may explain more clearly the intention ofuse such as ethical and religious values (e.g. privacy).Furthermore, future studies should concentrate on cross-cultural analyses to test whether the intention to use theMobile Camera may differ from country to country andamong various cultures, which will be more applicable tomulti-national organizations. Finally, we suggest futurestudies emphasize other models and compare the results totest the strengths and the validity of these models indifferent contexts.

Despite these limitations, this study provides manage-rial and theoretical insights. From a managementperspective, this study suggests companies grasp the use ofCMP before shopping and advertising based on the threeidentified variables that influence its intention to use:usefulness, ease of use, and effect of important persons(effect of family members and friends). Furthermore, it isinteresting to see what practices or actions influence anindividual’s innovativeness with such technology andmeasure the effects over purchases. Additionally, SN playsa significant role in defining the three dependentconstructs: Image, PU, and intention to use (b ¼ 0.36;b ¼ 0.40 and b ¼ 0.38 respectively). This means there issignificant strength in ourmodel. This is an important pointas this model could provide a solid framework for devel-oping managerial analysis for mobile and communicationorganizations, particularly in this cultural context.

From a theoretical perspective, this study has found thatthe intention to use CMP explains 33.9% of the total varia-tions. Accordingly, 66.1% remain unexplained. Such resultssuggest the need for additional research incorporatingadditional variables to consider how to more completelyexplain this behavior in future models. For future research,adding the constructs such as perceived enjoyment/hedonic [25,61], personal innovativeness [2,57], perceivedprivacy [26], and habit [34] will be an enrichment to ourmodel. Finally, the results of this research encourages

testing the model with adults from both genders. More-over, there is a need for further research to test andmeasure the actual use of CMP compared to our currentresearch that focused only on the intention to use.

Acknowledgements

This research was funded by Kuwait University,Research Grant IQ 06-05.

References

[1] Abdul Karim NS, Darus SH, Hussin R. Mobile phone applications inacademic library services: a students’ feedback survey. Campus-Wide Information Systems 2006;23(1):35–51.

[2] Agarwal R, Prasad J. A conceptual and operational definition ofpersonal innovativeness in the domain of information technology.Information Systems Research 1998;9(2):204–16.

[3] Al-Gahtani Said S, Hubona Geoffrey S, Wang Jijie. Informationtechnology (IT) in Saudi Arabia: culture and the acceptance and useof IT. Information & Management 2007;44(1):681–91.

[4] Al-Somali Sabah A, Gholami Roya, Clegg Ben. An investigation intothe acceptance of online banking in Saudi Arabia. Tecnovation 2009;29(1):130–41.

[5] Arnold MJ, Reynolds KE. Hedonic shopping motivations. Journal ofRetailing 2003;79(2):1–20.

[6] Bouwman Harry, Bejar Angel, Nikou Shahrokh. Mobile services putin context: a Q-sort analysis. Telematics and Informatics 2012;29(1):66–81. February, 2012.

[7] Chan Siu-cheung, Lu Ming-te. Understanding internet bankingadoption and use behavior: a Hong Kong perspective. Journal ofGlobal Information Management 2004;12(3):21–43. July–Sept.

[9] Cheong JH, Park M. Mobile internet acceptance in Korea. InternetResearch 2005;15(2):125–40.

[10] Choi Jeewon, Seol Hyeonjoo, Lee Sungjoo, Cho Hyunmyung,Park Yongtae. Customer satisfaction factors of mobile commerce inKorea. Bradford 2008;18(3):313–35.

[12] Davis FD. Perceived usefulness, perceived ease of use, and useracceptance of information technology. MIS Quarterly 1989;13:319–39.

[13] Fishbein M, Ajzen I. Belief, attitude, intention and behavior: anintroduction to theory and research. Massachusetts: Addison-Wesley, Reading; 1975.

[14] Sanchez-Franco Manuel J. WebCT – the quasimoderating effect ofperceived affective quality on an extending technology acceptancemodel. Computers & Education 2010;54(1):37–46. January, 2010.

[15] Gerpott Torsten J. Impacts of mobile Internet use intensity on thedemand for SMS and voice services of mobile network operators: anempirical multi-method study of German mobile Internetcustomers. Telecommunications Policy 2010;34(8):430–43.September, 2010.

[16] Green N, Harper N, H. R. R, Murtagh G, Cooper G. Configuring themobile user: Sociological and industry views, Special issue onmobile communication and the Reformulation of the social order.Personal and Ubiquitous Computing 2001;5:146–56.

[17] Gupta Babita, Dasgupta Subhasish, Gupta Atul. Adoption of ICT ina government organization in a developing country: an empiricalstudy. Journal of Strategic Information Systems 2007;17:140–54.

[18] Hofstede G. Culture’s consequences: international differences inwork-related values. Newbury Park, CA: Sage; 1980.

[19] Hosain Liquat, de Silva Anjali. Exploring user acceptance of tech-nology using social networks. Journal of High TechnologyManagement Research 2009;20:1–18.

[20] Hsiao Chun Hua, Yang Chyan. The intellectual development of thetechnology acceptance model: a co-citation analysis. InternationalJournal of Information Management 2011;31(2):128–36. April,2011.

[21] Hsu Chin-Lung, Lu Hsi-Peng. Consumer behavior in online gamecommunities: a motivational factor perspective. Computers inHuman Behavior 2007;23:1642–59.

[22] Hu Paul Jen-Hwa, Chen Hsinchun, Hu Han-fen, Larson Cathy,Butierez Cynthia. Law enforcement officers’ acceptance of advancede-government technology: a survey study of COPLINK mobile.Electronic Commerce Research and Application 2011;10(1):6–16.Jan, Feb 2011.

K. Rouibah et al. / Technology in Society 33 (2011) 271–283282

[23] Hung S, Ku C, Chang C. Critical factors of WAP services adoption: anempirical study. Electronic Commerce Research and Application2003;2:42–62.

[24] ICT Qatar. Launch of new telecommunications licenses for fixed andmobile services. Web, www.ict.gov.qa/files/marketoverview.pdf;2007.

[25] Jamal Ahmad, Davies Fiona, Chudry Farooq, Al-Marri Mohamed.Profiling consumers: a study of Qatari consumers’ shopping moti-vations. Journal of Retailing and Consumer Services 2006;13:67–80.

[26] James T, Pirim T, Boswell K, Reithel B, Barkhi R. Determining theintention to use biometric devices: an application and extension ofthe technology acceptance model. Journal of Organizational and EndUser Computing 2006;18(3):1–24.

[27] Kim Hong-bumm, Kim Taegoo, Shin Sung Won. Modeling roles ofsubjective norms and eTrust in customers’ acceptance of airline B2CeCommercewebsites. TourismManagement 2009;2009(30):266–77.

[28] Kim Sang Hyun. Moderating effects of job relevance and experienceon mobile wireless technology acceptance: adoption of a smart-phone by individuals. Information & Management 2008;45(6):387–93. September, 2008.

[29] Koury Sandra Soroa, Yang, Kenneth CC. Factors affecting consumers’responses to mobile advertising from a social norm theoreticalperspective. Telematics and Informatics 2010;27(1):103–13.

[30] Lee Kun Chang, Kang Inwon, Kim Jin Sung. Exploring the userinterface of negotiation support systems from the user acceptanceperspective. Computers in Human Behavior 2007;23:220–39.

[31] Lee Yu-Cheng, Li Mei-Lan, Yen Tieh-Min, Huang Ting-Ho. Analysis ofadopting an integrated decision making trial and evaluation labo-ratory on a technology acceptance model. Expert Systems andApplications 2010;37(2):1745–54. March, 2010.

[32] Legris P, Ingham J, Collerette P. Why do people use informationtechnology? A critical review of the technology acceptance model.Information & Management 2003;40:191–204.

[33] Liao Chechen, Palvia Prashant, Chen Jain-Liang. Information tech-nology adoption behavior life cycle: toward a technology continu-ance theory (TCT). International Journal of Information Management2007;29:309–20.

[34] Limayem M, Hirt SG. Force of habit and information systems usage:theory and initial validation. Journal of the AIS 2003;(4).

[35] Lin H, Wang Y. An examination of the determinants of customerloyalty in mobile commerce contexts. Information & Management2006;43:271–82.

[37] Loch KD, Straub DW, Kamel S. Diffusing the internet in the Arabworld: the role of social norms and technological culturation. IEEETransactions on Engineering Management 2003;50(1):45–63.

[39] Lu J, Yu C, Liu C. Facilitating conditions, wireless trust and adoptionintention. Journal of Computer Information Systems; 2004:46–63.Fall.

[40] Lu June, Yao, James E, Yu Chun-Sheng. Personal innovativeness,social influences and adoption of wireless internet services viamobile technology. Journal of Strategic Information Systems 2005;14:245–68.

[41] McAdams DP. Personal needs and personal relationships. In:Steven WD, editor. Handbook of personal relationships. New York:Wiley; 1988. p. 7–22.

[42] McGuire W. Psychological motives and communication gratifica-tion. In: Blummer JF, Katz, editors. The uses of mass communica-tions: current perspectives on gratification research. Beverly Hills:Sage Publications; 1974. p. 106–67.

[43] McGuire W. Some internal psychological factors influencingconsumer choice. Journal of Consumer Research 1976;2(4):302–19.

[44] Meso P, Musa P, Mbarika V. Toward a model of consumer use ofmobile information and communication technology in LDCs: thecase of Sub-Saharan Africa. Information Systems Journal 2005;15(2):119–46.

[45] Moore GC, Benbasat I. Development of an instrument to measurethe perception of adopting an information technology innovation.Information Systems Research 1991;2(3):192–222.

[46] Ngai EWT, Gunasekaran A. A review for mobile commerce researchand applications. Decision Support Systems 2007;43:3–15.

[47] Lopez-Nicolas Carolina, Molina-Castillo Francisco J, Bouwman Harry.An assessment of advanced mobile services acceptance: contribu-tions from TAM and diffusion theory models. Information &Management 2008;45:359–64.

[48] Nunnally JC. Psychometric theory. 2nd ed. New York: MacGraw-Hill; 1978.

[49] Nysveen H, Pedersen Per E, Thorbjørnsen H. Intention to use mobileservices. Antecedents and cross-service comparisons’. Journal of theAcademy of Marketing Science 2005;33(3):330–46.

[50] Nysveen H, Pedersen PE, Thorbornsen H. Explaining intention to usemobile chat services: moderating effects of gender. Journal ofConsumer Marketing 2005;22(5):247–56.

[51] Pai Fan-Yun, Huang Kai-I. Applying the technology acceptancemodel to the introduction of healthcare information systems.Technological Forecasting and Social Change 2011;78(5):650–60.May, 2011.

[53] Pedersen PE, Methlie LB, Thorbjornsen H. Understanding mobilecommerce end-user adoption: a triangulation perspective andsuggestions for an exploratory service evaluation framework. In:35th annual Hawaii international conference on system sciences,Big Island, Hawaii. Los Alamitos: IEEE Computer Society Press;2002.

[55] van Raaij Erik M, Schepers Jeroen J L. The acceptance and use ofa virtual learning environment in China. Computers & Education2008;2008(50):838–52.

[56] Rogers EM. Diffusion of innovations. NY: Free Press; 1983.[57] Rouibah K, Abbas H. Effect of personal innovativeness, attachment

motivation and social norms on the acceptance of cameramobile phones: an empirical study in an Arab country. InternationalJournal of Handheld Computing Research (IJHCR) 2010;2(1):72–93.January–March.

[58] Rouibah K. Does mobile payment technology Mnet attract potentialconsumers: a Kuwaiti study. In: Mark Toleman, Aleen Cater-Steel,and David Roberts , editor. Proceedings of 18th AustralasianConference on Information System (18th ACIS), Queensland, Aus-tralia, 4, 5 December 2007. pp. 199–211.

[59] Rouibah K. Social usage of instant messaging by individuals outsidethe workplace in Kuwait: a structural equation model. IT & People2008;21(1):34–68.

[60] Rouibah K, Abbas H. Modified technology acceptance model forcamera mobile phone adoption: development and validation.Proceedings of the 17th Australian Conference on InformationSystem, Adelaide, Australia December 5–7, 2006.

[61] Rouibah K, Hamdy H. Factors affecting information communicationtechnologies usage and satisfaction: perspective from instantmessaging in Kuwait. Journal of Global Information Management2009;17(2) [April/June].

[62] Rouibah K, Ould-Ali S. In: Rouibah K, Khalil O, Hassanien Aboul Ella,editors. Mobile-Commerce intention to use via SMS: the case ofKuwait, in Emerging markets and e-commerce in developingeconomies;, ISBN 978-1-60566-100-1; 2008. 450 pages.

[63] Schepers Jeroen, Wetzels Martin. A meta-analysis of the technologyacceptance model: investigating subjective norm and moderationeffects. Information & Management 2007;44(1):90–103. January.

[64] Selim Hassan. An empirical investigation of student acceptance ofcourse websites. Computers and Education 2002;40:343–630.

[65] Stern Barbara B, Royne Marla B, Stafford Thomas F,Bienstock Carol C. Consumer acceptance of online auctions: anextension and revision of the TAM. Psychology & Marketing2008;25(7):619–36. July.

[66] Tauber EM. Why do people shop? Journal of Marketing 1972;36(4):46–9.

[69] Tornatzky LG, Klein KJ. Innovation characteristics and innovationadoption implementation: a meta-analysis of findings. IEEE Trans-actions on Engineering Management 1982;34(3):28–45.

[70] Urbaczewski A, Wells J, Suprateek S, Koivisto M. Exploring culturaldifferences as a means for understanding the global mobileinternet: a theoretical basis and program of research. In: The 35Hawaii International Conference on system sciences, Big Island,Hawaii. Los Alamitos: IEEE Computer Society Press; 2002.

[71] Venkatesh V, Davis FD. A theoretical extension of the technologyacceptance model: four longitudinal field. Management Science2000;46:186–204.

[72] Venkatesh V, Ramesh S. Understanding usability in mobilecommerce. Communications of the ACM 2003;46(12):53–6.

[73] Westbrook RA, Black WC. A motivation-based shopper typology.Journal of Retailing 1985;61(1):78–103.

[74] Wireless moment, http://www.wirelessmoment.com/2004/02/forbes_reports_.html; 2004.

[75] Wu Hen-Her, Want Shu-Ching. What drives mobile commerce? Anempirical evaluation of the revised technology acceptance model.Information & Management 2005;42:719–29.

[76] Yi MY, Jackson JD, Park JS, Probst JC. Understanding informa-tion technology acceptance by individual professionals: toward anintegrative view. Information & Management 2006;43:350–63.

[77] Yousafzai SY, Foxall GR, Pallister JG. Technology acceptance: a meta-analysis of the TAM: part 2. Journal of Modelling in Management2007;2(3):281–304.

K. Rouibah et al. / Technology in Society 33 (2011) 271–283 283

[78] Yu Jieun, Ha Imsook, Choi Munkee, Rho Jaejeun. Extending the TAMfor a t-commerce. Information & Management 2005;42:965–76.

[79] Chang MK, Cheung W, Lai SV. Literature derived reference modelsfor the adoption of online shopping. Information & Management2005;42(4):543–59.

[80] Rouibah Kamel. Intention to use camera mobile phone before e-shopping in the Arab World: Test of the applicability of TAM 2.Proceeding of the IADIS International Conference InformationSystems, Edited by Pedro Isaias and Philip Power, ISBN: 978-972-79-9. Barcelona 25–27; February 2009. p. 3–9.