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The nature and components of perceived behaviouralcontrol as an element of theory of planned behaviourAli Hussein Saleh Zolait aa Department of Information Systems, University of Bahrain, Sakhir, Kingdom of Bahrain

Accepted author version posted online: 17 Oct 2011.Version of record first published: 04Nov 2011.

To cite this article: Ali Hussein Saleh Zolait (2011): The nature and components of perceived behavioural control as anelement of theory of planned behaviour, Behaviour & Information Technology, DOI:10.1080/0144929X.2011.630419

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The nature and components of perceived behavioural control as an element of theory of

planned behaviour

Ali Hussein Saleh Zolait*

Department of Information Systems, University of Bahrain, Sakhir, Kingdom of Bahrain

(Received 11 January 2010; final version received 16 September 2011)

Ajzen (1991. The theory of planned behaviour. Organizational Behaviour and Human Decision Processes, 50 (2), 179–211) suggested three constructs that determine the user’s intention: attitude, subject norms and perceivedbehavioural control (PBC). Recently, some psychologists have argued that PBC is poorly understood. This studyattempted to investigate the nature and components of PBC in research adapting theory of planned behaviour (TPB)to predict the intentions of bank customers with regard to adoption of Internet banking. The findings show somedifficulty in discriminating between the presumed internal and external determinants of PBC. This study identifiestwo determinants: (1) self-efficacy (SE) and (2) facilitating conditions. The latter is broken into three facilitationfactors: (1) resources, (2) technology and (3) government support (GS). Interestingly, SE, if considered as an internalfactor, exhibits a significance effect on PBC in the presence of the three external factors. This shows that the externalfactors have a significance effect on PBC when entered for regression analysis without SE. These are valuablefindings which show that both components of one’s belief in one’s level of control (internal factors: SE; externalfactors: resources, technology and GS) are important. However, which factors have the greatest effect on PBC mightbe related to the type of innovation or to other factors.

Keywords: Internet banking; theory of planned behaviour; control-belief components; self-efficacy; facilitatingconditions

1. Introduction

Perceived behavioural control (PBC) is the thirddeterminant of intention added to the theory ofreasoned action (TRA) model by Ajzen (1991). Theconcept of PBC was added to TRA to explainconditions where individuals do not have completecontrol over their behaviour. PBC, according to Ajzen,refers to people’s perception of the ease or difficulty ofperforming the behaviour in question. Similarly,Mathieson’s (1991) definition of PBC is ‘the indivi-dual’s perception of his/her control over performanceof the behaviour’. This definition includes the indivi-dual’s perception of the presence or absence ofrequisite resources and opportunities (Ajzen andMadden 1986) to engage in a particular behaviour.Consequently, PBC is viewed by Doll and Ajzen (1992)as ‘the perceived ease or difficulty of performing thebehaviour and is assumed to reflect past experience aswell as anticipated impediments and obstacles’.

Murphy (2009) defined PBC as one’s perception ofhow easy or difficult it will be to carry out the intendedbehaviour. The definition of the concept of PBC hasbeen controversial. Kraft et al. (2005, p. 480) reportedthat one ‘symptom of this controversy is the disparity

in the labels used for the PBC components’. Thedisparity in PBC definitions and operationalisationsmay reflect that much empirical research has beenconducted on the theory of planned behaviour (TPB).Along these lines, researchers (e.g. Liu et al. 2007,Amireault et al. 2008) have argued that PBC is amultidimensional construct. Kraft et al. (2005) statedthat PBC consists of two (interrelated) components: (1)self-efficacy (SE) and (2) controllability.

Previous studies in information systems (ISs) haveutilised the PBC construct and provided an explana-tion of the effect of PBC on behaviour–intention (BI).For example, Mathieson (1991) demonstrated thatlevel of behavioural control positively influencesintention to use an IS. A positive relationship betweenPBC and intention is also found in Taylor and Todd(1995a), which examined users in a computer resourcescentre. Meanwhile, Pavlou (2002) investigates thephenomenon in e-commerce behaviour. The impor-tance of PBC in this regard is based on the recognitionthat perception of control would facilitate informationacquisition, since the in-control consumer has thecognitive resources to manage information-acquisitionbehavioural activities and, thus, positively influencethe level of product purchases, since these consumers

*Email: azolait@uob.edu.bh; alizolait@gmail.com

Behaviour & Information Technology

2011, 1–21, iFirst article

ISSN 0144-929X print/ISSN 1362-3001 online

� 2011 Taylor & Francis

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would not fear opportunistic behaviour on the part ofWeb retailers. In sum, PBC is likely to reduce barriersto the adoption of business to customer (B2C) e-commerce (Pavlou 2002). In a proposed model of e-commerce service acceptance, an adaptation of theTPB, Bhattacherjee (2000) shows that behaviouralcontrol has a positive influence on the intention toaccept a service. From a different perspective, Taylorand Todd’s (1995a) study on understanding informa-tion-technology (IT) usage demonstrated that PBC canreflect perceptions of internal and external constraintson behaviour. In the context of Internet banking (IB),Tan and Teo (2000) demonstrated that the intention toadopt IB services can be predicted by PBC factors.Some studies of IB have utilised PBC constructs,especially those which employed and adapted the TPBand its decomposed models, such as Liao et al. (1999)and Shih and Fang (2004). The objective of this studyis, therefore, to improve our understanding of thisissue by answering the research question:

RQ: How do PBC and its proposed determinantsexplain the BI to adopt IB services?

This article is organised as follows: Section 1 (thecurrent section) introduces what is known about PBCand Section 2 gives a brief overview of the theoreticaland research background, while Section 3 describes thescientific approach of the present article. Then, aliterature review of the previous studies pertaining toIB and PBC is presented in Section 4. Next, theresearch methodology, research instrument and hy-potheses are presented in Section 5. After that, Section6 displays the analyses of the study results. The seventhand last section concludes the article and highlights thekey findings, limitations and implications of thisresearch.

2. Theoretical background

There is strong theoretical and empirical support forthe existence of an effect of PBC on BI. Applied toonline-transaction intentions, for instance, behaviourcontrol should have a positive effect, since consumerswould have less fears of opportunistic behaviour onthe part of a Web retailer. According to the principlesof TPB, PBC, together with BI, can be directly used topredict behaviour. Doll and Ajzen (1992) posited thatwhen a situation affords a person complete controlover their behaviour, intention alone should besufficient to predict behaviour, as specified in TRA.This implies that PBC is a very important antecedentin predicting BI when people have no control overtheir behaviour. Evidence can be drawn from Venka-tesh (2000), who pointed out that PBC was

demonstrated to have an effect on dependent variablessuch as intention and behaviour in a variety ofdomains.

Prior to discussing the determinants of PBC, Ajzen(1991) specified some conditions for accurate predic-tion to be possible. First, the measures of intention andPBC must correspond to or be compatible with thebehaviour that is predicted. Second, intentions andPBC must remain stable in the interval between theirassessment and the observation of the behaviour. Thethird condition concerns the accuracy of PBC realis-tically reflecting actual control (i.e. its predictivevalidity).

In broad terms, PBC as a construct includes twodimensions, those of SE and facilitating conditions,that reflect situational enablers or constraints toparticular behaviour (Venkatesh 2000). In the psychol-ogy literature, behaviour control is approached inseveral ways, but Ajzen’s (1991) idea of PBC built onbehavioural achievement jointly depends on motiva-tion (intention) and ability (behaviour control). Theconcept of PBC was expanded in Taylor and Todd’s(1995b) decomposed TPB, which combined the dimen-sions of SE, ‘resource’ facilitating conditions and‘technology’ facilitating conditions as the most rele-vant determinants of behaviour control. In thisconnection, the PBC construct may include suchcomponents as ‘technological facilitating conditions’,‘resource-related facilitating conditions’, ‘governmentsupport (GS)’ and ‘SE’ (Taylor and Todd 1995a,1995b). Pursuant to the attempt to understand humanbehaviour, according to Venkatesh (2000), PBC hasreceived greater interest from a psychological perspec-tive than has actual control. Control relates to anindividual’s perception of the availability of theknowledge, resources and opportunities required toperform a specific behaviour. Some authors, such asTrafimow et al. (2002), have debated the emerging roleof PBC in the social psychology literature, where theyintroduced a multidimensional conception of theconstruct, Furthermore, Trafimow et al. (2002) suggestthe use of the two dimensions of perceived control andperceived difficulty, instead of those in Ajzen’s (1991)PBC construct, as proximal determinants of BI andbehaviour.

In line with the needs implied by this previousresearch, IB (an emerging technology that allowscustomers to conduct banking transactions throughthe Internet) was selected as the technological contextfor the present article. Stafford (2001) notes that IB isone of the today’s hottest topics among bankers andthat its growth is driven by growing consumer demand,competitor pressure and pressure to improve profits.The research findings of the present study will beworthwhile not only in the expansion of PBC research

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to a new technological context, but also to a newgeographical context different from those previouslystudied in culture and IB-adoption levels: Yemen. Itshould also be remembered that marketing researchhas recently identified IB as a new marketing channelfor banks and financial services. Nielsen et al. (2003)have made the general statement that Internet-basedmarketing channels appear to offer better service to aquickly growing segment of customers and have lowermarginal costs than the traditional brick-and-mortarchannels.

This study has been structured in terms of threegoals. First, the researcher has examined the roles ofSE and the three facilitating condition variables(technology, resources and GS) in explaining variationin perceived control and intention towards the adop-tion of IB on the part of bank customers. This will beof value to scholars, IB providers and IB stakeholders,because it will assess the relevance of these factors bymeasuring their impacts on BI and PBC (Bentley andWhitten 2007). Second, the study employs factor-analysis and linear-regression techniques to examinethe relationship between the obtained components andBI and assess the components’ applicability as general-ised perceptions of both control and intention. Finally,this study also provides an insight into the positiveeffects of control-belief components [SE, facilitatingtechnology (FT), facilitating resources (FR) and GS]on both perceived-control and intention measures.

3. Research approach

This study relies on a ‘hypothetico-deductive method’in which a set of quantitative rules are used to examinethe roles of SE and the three facilitating conditionvariables (technology, resources and GS) in explainingthe effect of perceived control and BI on the adoptionof IB. This research has taken a positivist philosophicalstance and has adopted a behavioural-science para-digm. The research question was expressed in terms offormal hypotheses to be tested, and primary data werecollected via a self-administrated survey covering asample of bank users. In this study, a new approach isapplied to the construct of PBC, in that both internalfactors affecting control belief, such as SE, andexternal factors, such as technology, resources andavailability of governmental support, are combined.Thus, it is essential to choose appropriate dimensionsfor the study, to ensure that it aligns adequately withthe research objectives. As well, the approach used inthis study relies on a concept of decomposition of thecontrol-belief structure inspired by TPB. However,individual intentions towards service adoption cantake place with little reliance on FT, resources orgovernmental support. The researcher’s view is that the

impetus for the adoption of a particular technologycomes from inside individuals when they feel the needfor that technology [i.e. when they feel that it willimprove their lives (SE)]. The necessity of anyparticular technology will be felt once it has commu-nity approval as a useful tool. Adoption will thenoccur quickly, especially in IT-saturated societies.Examples include mobile phones and online socialnetworks (Laudon and Laudon 2010). This modelimplies a combination of internal and external beliefconstraints, as shown in Figure 1.

Figure 1 shows that there are four variables: (1) SE,(2) FT, (3) facilitated resources and (4) availability ofGS. These are regarded as the determinants of the PBCvariable. According to Hernandez and Mazzon (2007),SE is related to self-perceived ability to use a newtechnology, while facilitating conditions are thephysical and technological resources available fortechnology adoption. Findings by Hartshorne andAjjan (2009) reported that each of three factors—SE,FR and FT—explains a significant part of the variancein PBC. Wang and Kim (2007) have suggested thatgovernment plays a leading role in diffusing innova-tion. In Liu et al. (2007), only perceived difficulty andSE were found to be predictors of overall PBC.Similarly, Amireault et al. (2008) viewed PBC as thecombined influence of SE and controllability. Thisstudy aims to contribute to a further understanding ofthe PBC literature. Moreover, it is important to assessthe impact of PBC on the prediction of individualintentions towards the adoption of IT systems such asIB systems. Therefore, this study looks into thedeterminants of PBC and intention variables asspecified in TPB theory, with control for the effectsof attitude and participant-norm variables which wereheld constant in order to assess and clarify therelationships among the components of PBC in theTPB.

4. Literature review

Chen and Cheng (2009) argued that the consumer’sintention to adopt innovations is quite important andaccurately predicts usage behaviour. Meanwhile, theexisting literature related to the PBC construct hasidentified three antecedents that contribute to itsformation (Liu et al. 2007). Some researchers haveused different dimensions for PBC, such as perceiveddifficulty and perceived control (Liu et al. 2007,Amireault et al. 2008) or salient barriers and facil-itators (Sutton et al. 2003). The existing theories of ITusage suggest that users’ BI is the primary predictor oftheir IT-usage behaviour (Bhattacherjee and Sanford2009). This has been supported by Chen and Cheng(2009), who stated that the BI variable is very

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important in understanding consumer intentions.Understanding what motivates consumer intentions iscritical for product and service providers, becauseunderstanding intention is the key to survival in thefast-paced and hypercompetitive business environment(Chen and Cheng 2009).

4.1. Self-efficacy

SE is a construct derived from social cognitive theory.Grant et al. (2009), in their literature review of SE,provided three-pronged definitions of the construct.First, SE refers to an individual’s comprehensive beliefin his/her ability to perform a specific task. Second, SEis a dynamic, multileveled and multifaceted construct.Third, efficacy beliefs involve a mobilisation compo-nent. According to Grant et al. (2009), SE reflects anindividual’s perception of his/her ability based on thepast performance or experience, but it also forms acrucial influence on future intentions. From the ITperspective, computer SE is defined as ‘the judgment ofone’s ability to use a computer’ (Shih 2006, Ndubisi2007). According to Hartshorne and Ajjan (2009), SEwas found to influence the perception of behaviouralcontrol, which also had an influence on the BI ofstudents to use Web 2.0 tools. Applied to IB, SEdescribes consumers’ judgement of their own capabil-ity to use the Internet to get access to a bank’sinformation, use financial services and conduct trans-actions online (Ndubisi 2007). SE, in this context, canperhaps be conceived as the measure of one’sconfidence in mastering a new challenge (Hartzel2003). Furthermore, recent studies of IS have provided

empirical support for a relationship between SE andoutcome expectations. For instance, Shih (2006) andHartzel (2003) showed that computer SE is animportant determinant for an individual’s decisionsregarding software adoption and use. Compeau andHiggins (1995) found that SE played an important rolein determining patterns of computer usage, bothdirectly and through outcome expectations. Otherstudies of IB have provided empirical support for asignificant moderating effect of SE in the relationshipbetween perceived usefulness, perceived ease of use andadoption intention (Ndubisi 2007). Tan and Teo(2000), Lai and Li (2004/2005) and Brown et al.(2004) show that computer SE is an importantdeterminant of an individual’s decision to adopt IB.In this connection, SE, according to Davis et al. (1989),is one of the two mechanisms by which ease of useinfluences attitudes. Lopez and Manson (1997) arguethat the constructs of computer SE must be directedtowards improving the perceived usefulness of an IS.This is in line with the TRA, in that it is assumed thatindividuals will use computers more if they can see thatthere will be positive benefits (outcomes) associatedwith using them (Fishbein and Ajzen 1975).

4.2. Facilitating conditions

The facilitating condition constructs are viewed asexternal factors related to the environment in whichadoption will take place (Taylor and Todd 1995a,Celik 2008, Teo 2009). Therefore, understanding theanticipated influence of facilitating conditions is veryimportant in studying human behaviour in IS,

Figure 1. Relevance of PBC antecedents (conceptual framework). Source: Taylor and Todd (1995a).

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especially in the areas such as the adoption of IB(Hernandez and Mazzon 2007). The proposed frame-work of this study includes facilitating conditions asexternal factors. It is proposed in Taylor and Todd(1995a) that they will influence user BI towards theadoption of IB. The consideration of facilitatingconditions means that adoptive behaviour cannot occurif objective conditions in the environment prevent it(Thompson et al. 1991, Teo 2009), or if the facilitatingconditions make the behaviour difficult (Thompsonet al. 1994). Furthermore, facilitating conditions, ac-cording to Ratnasingam et al. (2005), contribute to anorderly manner of transacting electronically and adher-ing to certain procedures that contribute to best businesspractices for e-marketplace participation.

Facilitating conditions, according to the literaturereviewed in Hernandez and Mazzon (2007) and Taylorand Todd (1995a), involve availability of resourcessuch as time and money, availability of GS andtechnology-compatibility issues that may constrainusage. In this light, the adoption of new technologyrelated to the field of ISs requires service providers toensure an adequate and fertile environment thatfacilitates adoption. In this respect, some facilitatingconditions (technology, resources and GS) are definedin Thompson et al. (1991) as ‘objective factors, ‘‘outthere’’ in the environment, that several judges orobservers can agree make an act easy to do’ (p. 205).Taylor and Todd (1995a) argued that when all otherthings are equal, the innovation adoption in the ITcontext is less likely, as time and money are lessavailable and technical compatibility decreases (Taylorand Todd 1995a). While facilitating conditions in theTriandis model (1980) only affect the actual behaviour,the modified model inspired by Chang and Cheung(2001) postulates that facilitating conditions can have asignificant impact on the individual’s intention. Hence,investigating the possible influence or impact of thethree facilitating conditions, (technology, resourcesand government) on user BI towards adoption of IBcannot be neglected. Furthermore, Venkatesh et al.(2003) pointed out that these variables affect both BIand actual usage. In the IS literature, the above-mentioned variables were utilised in predicting theadoption of new technologies (see, for example,Cheung et al. 2000, Cho and Cheung 2003, Hunget al. 2003, Hernandez and Mazzon 2007). Tan andTeo (2000) employed two facilitating condition vari-ables: (1) availability of GS and (2) availability oftechnological support.

Shih and Fang (2004) in Taiwan found thatfacilitating condition variables did not influence PBC.However, Wang et al. (2003) investigated the determi-nants of user acceptance of IB based on theTechnology Acceptance Model (Davis 1986); their

findings recommended adding variables related tosocial factors, for example, subjective norm, to PBCdeterminants to make the prediction and study ofusage intentions more accurate. The present researcherbelieves that more success in the adoption or diffusionof new technology in the field of ISs shows that moreconditions have been facilitated. In the field ofadoption and diffusion of technology, the set ofconditions can be thought of either as drivers or aschallenges. They are sometimes referred to as inhibi-tors, factors that delay and perhaps prevent adoption,or motivators, factors that speed up adoption. Thisstudy suggests that there are three types of conditionswhich should be examined. These are technology,resources and GS. The evidence for their relativeimportance to IB adoption will be expounded upon inthe following sections.

4.2.1. Technological facilitating conditions (FT)

User perception of technological support for IBservices in developed countries such as Singapore, asdiscussed by Tan and Teo (2000), might not beimportant. However, this situation may be differentin developing countries where the technology infra-structure is not as advanced as it is in Singapore or inWestern countries like the United States and UnitedKingdom. In a similar case, Shih and Fang (2004)demonstrated the lack of influence of (latent) technolo-gical facilitating conditions on the PBC construct, arguingthat this was because most respondents were familiar withthe Internet and, therefore, had easy access to technolo-gical resources and infrastructure. Similarly, Hernandezand Mazzon’s (2007) findings indicated that technologicalsupport is not a significance predictor for either theintention to adopt or the intention to continue to use IB.Hartshorne and Ajjan (2009), in research investigatingWeb 2.0 acceptance, found that technological facilitationdid not have any significance effect on PBC.

In a study investigating barriers to IB adoptionamong corporate customers in Thailand, Rotchanaki-tumnuai and Speece (2003) pointed out that thetechnology readiness of corporate customers plays arole in technology adoption. Prior research (e.g.Dabholkar 1996) similarly indicated that customerattitudes and beliefs about technology are correlatedwith their intention to use it. Likewise, Thompsonet al. (1991) suggested that the provision of support forowners of personal computers (PCs) may be one typeof facilitating condition that can influence systemadoption. Along these lines, Hernandez and Mazzon(2007), based on the empirical evidence, argued thatyounger male PC owners with a college degree andhigher income are more likely than other groups toadopt IB.

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Prior research has also discussed technologicalfacilitating conditions from the perspective of ITinfrastructure. For example, facilitating conditions,according to Venkatesh et al. (2003), affect the degreeto which an individual believes that an organisationaland technical infrastructure exists to support the use ofa system. Ratnasingam et al. (2005) utilised theconcept of IT connectivity. They identified threedimensions of IT connectivity: (1) IT compatibility,(2) IS telecommunication infrastructure and (3) inter-nal integration. IT connectivity, according to them,means the technological mechanisms that enable banksto be ‘IT connected’ in order to undertake transactionexchanges. In the context of ISs, availability of trainingand the provision of support are considered to befacilitating conditions, according to Thompson et al.(1991), because when they are present some of thepotential barriers to use are reduced or eliminated.This variable was tested in a number of technology-acceptance studies, and empirical support was foundfor the proposed effect on perceived usefulness andperceived ease of use (Thompson et al. 1994, Taylorand Todd 1995a, 1995b, Jiang et al. 2000, Venkatesh2000, Celik 2008). In this study, technological facil-itating conditions include access to the Internet andadequate hardware, software and network connec-tions, as well as the support provided by banks tofacilitate IB.

In sum, this clearly depicts a scenario in which theneed for facilitating conditions in technology adoptionfor IB differs according to the pace of technologicaldevelopment in a particular country.

The minimum needed facilitating conditions in adeveloped country cannot be generalised to otherdeveloped or developing nations. Thus, in deciding thesignificance of facilitating conditions, researchers shouldbe aware of the literacy and Internet-usage rates of apopulation. As previously mentioned, there are variousdimensions to the facilitating factors that have beenexamined and the technological facilitating conditionsinclude Internet accessibility as well as hardware,software and network connections, among others.

4.2.2. Resource-related facilitating condition (FR)

The second component of the facilitating conditionsconsidered in this article is the resources required touse a specific system. Examples of such resources aretime, financial resources and information communica-tion technology (ICT) resources. IT is an importantpart of the requirements for utilising a specific systemand cannot be ignored. However, it has to be notedthat other resources, such as those mentioned above,are also vital. As stated by Lu et al. (2003), policies,regulations and the legal environment are all

conditions critical to technology acceptance. Further-more, Cheung et al. (2000) observed that peoplerequire not only the necessary resources but also thesupport to carry out their intended action. This clearlyindicates that there are multiple systems related to theuse of a specific system; without underestimating therole of IT, other resources do play a role in the use of aspecific IT- or IS-related system. According to Celik(2008), user perceptions of system control decline whenknowledge about the system, needed resources andopportunities to reuse the system are more available. Inthis regard, appropriate training programmes combinedwith appropriate resources, as highlighted by Venkatesh(2000), should pave the way for the acceptance and useof new systems. Additionally, Hartshorne and Ajjan(2009) highlighted the possibility that the lack ofresources needed to use the technology could hinderuse and the formation of intention to adopt. Therefore,it is clear that the adoption of innovations requires FRsuch as time and technology. Finally, it is notable thatthe facilitation of resources was found to influence theperception of behavioural control, which also had aninfluence on the BI of students to use Web 2.0 tools(Hartshorne and Ajjan 2009).

4.2.3. Availability of GS

Yemen is a developing country with a population of 27million people. It is currently working towards thequick implementation of a highly advanced telecom-munication and IT infrastructure to increase itscitizens’ social and economic welfare. Widely availableIT services include fixed telephone lines, data commu-nication services using digital subscribers line (DSL)technology, integrated services digital network (ISDN)with a speed of 64 kilobytes or 128 kilobytes, pagingservices, audio-text services and cellular telephony,provided by companies including TeleYemen, YemenMobile, Spacetel and Sabafon. The first Internetservice started in the 1990s when the Yemen InternetGateway connected Internet service providers with theinternational backbone.

It has been widely noted that government can playinterventionist and leadership roles in the diffusion ofinnovation (Wang and Kim 2007). In Yemen, it hasbeen recognised that government entities such as thePublic Telecommunications Corporation, TeleYemenand the Ministry of Communications and InformationTechnology are major driving forces in the diffusion ofIT. GS for IB diffusion specifically is still notrecognised, and efforts exerted in this field are stillnot documented.

Hernandez and Mazzon’s (2007) findings in aBrazilian study indicated that GS is not a significancepredictor of individual intention to use IB. On the

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other hand, GS to facilitate the diffusion of innovationcan take several forms. Lee-Partridge and Ho (2003)suggested that these may include ‘setting regulationsthat minimise security risks’ and ‘taking more specificactions to encourage a higher level of online trading tocomplement its emphasis on e-lifestyle’.

Furthermore, when a government gives support toe-commerce, it is also one form of support for thediffusion of IB (Tan and Teo 2000). From theresearcher’s point of view, the greater the level of GSperceived by potential users, the more likely theywould be to adopt IB. This is congruent with theempirical evidence put forward by Wang and Kim(2007), Lee-Partridge and Ho (2003) and Tan and Teo(2000), who express the view that intervention bygovernment does lead to the adoption of bothtechnology and IB. Although it is not within the scopeof this study to measure the level of support for IBadoption from the Yemeni government, this researchattempts to measure the perceptions of Yemeni userswith regard to the government’s supportive role in IBadoption. GS, according to Tan and Teo (2000), is animportant factor in the adoption of IB by Singapor-eans. Such a case may apply to countries like Yemenwhere the government’s role in encouraging innovationadoption has still not been researched.

In Yemen, IB is rapidly growing. However, theadoption of IB among Yemeni consumers is still in itsinfancy. Additionally, the majority of the users can becharacterised as innovators and early adopters of IB(Zolait 2010) rather than the general public. Thus, atthis stage, GS is crucial to form a solid foundation toprovide a sense of ‘security’ for the users (current andpotential). GS, according to Tan and Teo (2000), is animportant factor in the adoption of IB by consumerseven where the government’s role in encouraginginnovation adoption has still not been researched.For example, support from local government-con-trolled media, as a social influence, can play animportant role in intention formation and alsocontributes to exposure (Zolait and Ainin 2008).Yemeni consumers viewed IB services as easy to use,possessed of a relative advantage (over the traditionalbanking) and compatible with the Yemeni way ofdoing business, all factors that impacted adoptionpositively. Four factors were presented by Zolait et al.(2008) to Yemeni policymakers, both in governmentand in banks, to facilitate IB for Yemeni customersand increase the adoption rate of IB. These factorswere (1) experience, (2) knowledge, (3) awareness and(4) exposure, also known as the four dimensions ofuser informational-based readiness by Zolait et al.(2008), which significantly and positively correlated tothe IB acceptance by Yemeni consumers. The fourfactors represent a segmentation tool that can assist

policymakers and bank managers in categorising IBcustomers (Zolait et al. 2008).

5. Methodology

For the purposes of instrument development in thepresent study, the conceptual definition of perceivedbehaviour control is a person’s perception of the ease ordifficulty of performing IB, as well as their beliefsabout whether they have the necessary resources andopportunities to adopt IB (Ajzen 1991, Pavlou 2002).Operationally, this section discusses the developmentof the instrument that will be utilised to measure PBCand beliefs about PBC. Taylor and Todd (1995a)proposed that PBC is ‘the sum of the control beliefs(cbk) weighted by the perceived facilitation (pfk) of thecontrol belief in either inhibiting or facilitating thebehaviour’. A control belief, according to Mathieson(1991), is ‘an individual’s perception of the availabilityof skills, resources, and opportunities’, while perceivedfacilitation is ‘the individual’s assessment of theimportance of those resources to the achievement ofthe outcome’. The proposed PBC’s equation is

PBC ¼Xni

i¼1cbkpfkðMathieson 1991;

Taylor and Todd 1995aÞ ð1Þ

Mathieson (1991) pointed out that the weights ofevaluation of desirable outcome (ei), motivation tocomply (mc) and perceived facilitation (pfk) have twoapproaches by which they can be measured in thestudy. The first method is direct assessment, in whichthe individual is asked to specify them using a Likertscale, while the second approach is indirect assessment,in which the weights can be estimated as coefficients ina regression equation.

In this study, the researcher applied the PBCconstruct following two main approaches. First, theresearcher made efforts to develop and validate a directoperational definition for the PBC construct. Thisstage was carried out by asking respondents to give arating to five statements (items) which were adaptedfrom Taylor and Todd’s (1995a, 1995b) study on aseven-point Likert scale ranging from ‘strongly dis-agree’ to ‘strongly agree’. The second stage inoperationalising the PBC construct was achieved bylooking at indirect measures. Measurement of the PBCconstruct and its relevant control-belief constructs inthis study was based on the combinations of severalitems adapted from TPB itself; from empirical researchon IB adoption such as Shih and Fang (2004), Al-Sabbagh and Molla (2004) and Tan and Teo (2000)and IS literature such as Venkatesh (2000). The

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following sections will elaborate on this description ofwhat researcher did and how the PBC construct wasapplied. This article will explain the PBC-measurementitems first; then, we will move on to identify thoseitems which operationalise the other PBC-beliefcomponents of SE and facilitating conditions.

5.1. Direct measurement of PBC

This section contains the discussion of the directassessment of the perceived-control construct. Deter-minants of behavioural control have been studiedusing many categories and terms: affective or cognitive;SE or controllability; ability; ease or difficulty andconfidence. The classic dichotomy between feeling(affective construct) and knowing (cognitive con-struct), as in Trafimow et al. (2004), has been employedin much researches in order to better understand whypeople perform certain actions or behave in certainways. Trafimow et al. concluded that there is a lack ofsystematic research on which factors are most im-portant for determining behaviour. Their findingsrevealed that ‘the predictive validity of affect vs.cognition depended upon whether participants wereaffectively or cognitively controlled’ (p. 207). As aresult, the conceptualisation of TPB’s PBC constructhas received recent attention, and potential problemswith low item-internal consistency have been identifiedin the early research. Rhodes and Courneya (2003a)attempt to make a conceptual distinction between PBCitems based on their respective relationships withintention. Tavousi et al. (2009) applied principalcomponent analysis (PCA) and confirmatory factoranalysis, using an initial population of 433 adolescents,to provide support for distinguishing between PBCand SE in looking at substance-abuse avoidanceamong Iranian adolescents. More recent empiricalresearch, according to Rhodes and Courneya (2003a),has identified two distinct item clusters used tomeasure PBC, which they labelled ‘SE’ and ‘controll-ability’. Rhodes and Courneya operationalise the PBCconstruct using items such as ‘ability’, ‘ease’ or‘difficulty’, ‘confidence’, ‘confidence if it were up tome’, ‘confidence to overcome obstacles’, ‘confidence ifI wanted to’, ‘entirely up to me’, ‘personal control’ and‘beyond my control’. The inclusion of PBC in studyingbehaviour, according to Armitage and Conner (2001),provides information about potential constraints onaction as perceived by the actor and explains whyintentions do not always predict behaviour. On aseven-point Likert scale ranging from ‘completelyagree’ to ‘completely disagree’, Tavousi et al. (2009)measured PBC with two items—‘avoidance of sub-stance use in future is entirely up to me’ and ‘avoidanceof substance use in future is in my control’.

PBC, according to Liu et al. (2007), has beendirectly measured in three different ways: (1) perceiveddifficulty, (2) SE and (3) controllability. Five itemswere selected to operationalise the PBC constructs.These were adapted from Taylor and Todd (1995a).The third item in Taylor and Todd (1995a) lends itselfto different possible answers to its subparts (i.e. it is adouble-barrelled question). Therefore, according toSekaran (2003), the researcher needs to separate thesubparts of question PBC3 into three specific questionsto avoid respondent bias. In other words, the researchercan separate the original item PBC3, ‘I have theresources and the knowledge and the ability to makeuse of the computing resource center (CRC)’, into thefollowing questions: ‘I have the resources necessary tomake use of IB’; ‘I have the knowledge necessary tomake use of IB’ and ‘I have the ability to make use ofIB’. There are five items altogether covered under PBC:(1) ‘I would be able to use IB’; (2) ‘I have the resourcesnecessary to make use of IB’; (3) ‘I have the knowledgenecessary to make use of IB’; (4) ‘I have the ability tomake use of IB’ and (5) ‘Using IB would be entirelywithin my control’. These five items were adapted fromTaylor and Todd (1995a, 1995b). Appendix 1 shows theitems selected to measure PBC.

5.2. Indirect measurement of PBC

The meta-analysis conducted by Armitage and Conner(2001) distinguishes between three types of PBCmeasure: SE, ‘perceived control over behaviour’ andPBC. SE was defined as ‘confidence in one’s ownability to carry out a particular behaviour’; perceivedcontrol over behaviour was defined as ‘perceivedcontrollability of behaviour’ and PBC was defined asthe perceived ease or difficulty of performing thebehaviour. Armitage and Conner show some evidencefor discriminant validity and for a distinction betweenSE and perceived control over behaviour. Moreover,the study’s findings showed that PBC independentlypredicted intentions and behaviour in a wide numberof domains. In Rhodes and Courneya (2003b), PBCwas measured by three items used to assess the SEconcept and three items used to assess the controll-ability concept. SE was measured on a seven-pointscale by the following items: (1) ‘How confident areyou that you will be able to exercise regularly in thenext 2 weeks?’; (2) ‘How confident are you that youwill be able to overcome obstacles that prevent youfrom exercising regularly over the next 2 weeks?’ and(3) ‘I believe I have the ability to exercise regularly overthe next 2 weeks’ (Rhodes and Courneya 2003b).Controllability was measured with these items: (1)‘Whether or not I exercise regularly in the next 2 weeksis entirely up to me’; (2) ‘How much personal control

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do you feel you have over exercising regularly in thenext 2 weeks?’ and (3) ‘How much do you feel thatexercising regularly in the next 2 weeks is beyond yourcontrol?’ (Rhodes and Courneya 2003b). The research-ers showed that the SE and volitional-control scaleshad acceptable reliability and good concept differentia-tion in PCA.

Trafimow et al. (2002) measure PBC as anamalgamation of two variables: (1) perceived controland (2) perceived difficulty. Perceived-control measuresthe extent to which people consider their behaviour tobe under their voluntary control, while perceiveddifficulty measures whether they consider the beha-viour in question to be easy or difficult to perform.These findings support the distinction between per-ceived control and perceived difficulty and also suggestthat perceived difficulty is a better predictor of mostBIs and behaviours than is perceived control.

The indirect measure of the PBC construct dependson four control-belief dimensions suggested by Taylorand Todd (1995a) and Tan and Teo (2000). These are(1) SE, (2) FT, (3) FR and (4) GS. The followingsubsections explain each component in further detail.

5.2.1. Detailed explanation of SE

SE is the potential adopter’s judgement of his/her owncapabilities to use IB and get access to information,financial services and transactions online (Compeauand Higgins 1995, Shih 2006). Gist and Mitchell (1992)point out that the traditional measurement of SE usesa nominal scale (‘yes’ or ‘no’). In this scale, anindividual’s sum of positive responses is the magnitudeof SE for that person, while, on other occasions,Likert-type scales, which simply ask how well theperson thinks he or she can do on the task and thencarry out the statistical correlation between scale scoreand performance, have been used. Igbaria and Iivari(1995) reported that SE had both direct and indirecteffects on usage, demonstrating its importance in thedecision to use computer technology. There are someSE models which are helpful tools in identifying the SEattributes of the current study. For example, Lopezand Manson’s (1997) proposed model depicted com-puter SE as a function of the two determinants: ‘socialpressure’ and ‘organisational support’. Another model,proposed by Igbaria and Iivari (1995), viewed SEconstruct as a function that can be predicted by thetwo variables of ‘computer experience’ and ‘organisa-tional support’. Compeau and Higgins’ (1995) modelidentifies two new constructs: ‘encouragement byothers’ and ‘others’ use’; these are in addition to‘support’, which is also demonstrated to have aninfluence on SE. In this article, the SE construct wasmeasured using five items on a seven-point Likert

scale. Two items were adapted from Igbaria and Iivari(1995) and Hill et al. (1987), in which individuals wereasked to indicate the extent of their disagreement oragreement with two statements on a seven-point scale.The scale ranges from (1) strongly disagree to (7)strongly agree. The statements are ‘I will understandhow IB works’ and ‘I am confident that I could learnIB applications’. The other three items are adaptedfrom Lassar et al. (2005). They are ‘I feel comfortableusing computers in general’; ‘I feel comfortable usingthe Internet’ and ‘my current skills in using the Internetenable me to do everything that I want to do online’(Lassar et al. 2005). According to Grant et al. (2009),research has often demonstrated that computer ex-perience has positive impacts on computer SE. The SEdecomposed belief items are presented in Appendix 1.The study will adopt the salient belief of the SEconstruct from Taylor and Todd (1995a), and the itemsselected will be rephrased to suit the study context.Decomposing beliefs regarding SE structures, accord-ing to Taylor and Todd, somewhat increases theexplanatory power of the model for BI. Furthermore,the decomposed TPB model suggests specific beliefsthat can be targeted by a designer or managerinterested in influencing system usage.

5.2.2. ‘FT’ conditions

FT refers to users’ perceptions of easy of accessibilityto the Internet and ease of use and access of hardware,software and network connections for IB services(Thompson et al. 1991, Tan and Teo 2000). Thisvariable is operationalised by asking respondents somequestions to account for technological situations inwhich an individual lacks substantial control over thetargeted behaviour (Ajzen 1991). The study has eightitems, which ask respondents about technology controland their perceptions of the importance of facilitatingand maintaining access to this technology. Five itemswere adapted from Taylor and Todd (1995a), Tan andTeo (2000), Shih and Fang (2004) and Brown et al.(2004). Appendix 1 shows the items developed tomeasure the FT construct.

5.2.3. ‘FR’ conditions

FR refers to an individual’s perception of their abilityto gain access to resources and opportunities requiredto facilitate IB-adoption behaviour (Ajzen 1991). Thebelief construct of FR is operationalised by asking therespondents five questions. In order to ensure con-struct content validity, all five control beliefs (cb) andtheir perceived-facilitation questions were adaptedfrom Taylor and Todd (1995a, 1995b). Respondentswere given two types of statements to be answered on a

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seven-point Likert scale. In the first item, theresearcher asked a question that aimed to probe intoa respondent’s belief in a particular resource, while thesecond question was intended to probe the motivationof this respondent to comply with that belief.Mathematically, the score of the respondent’s beliefon a given statement multiplied by the score of therespondent’s motivation to comply with that belief willresult in what Ajzen called salient belief (cb 6 pf).Appendix 1 indicates the items developed to measurethe ‘FR’ construct.

5.2.4. Government support

This refers to user perceptions of GS for e-commerce(Tan and Teo 2000). In the literature, government-support belief is a new construct, which is recom-mended by Tan and Teo (2000). Although the GSvariable is regarded as very important to examine IB inan economically developed environment (Singapore),this variable is still not widely utilised in the studies ofIB in less developed environments. In this study, theresearcher believes that investigating the effect of thisvariable will be fruitful. By studying this variable, theleadership role and intervention actions of the govern-ment in the diffusion of innovation will be explained.According to Lee-Partridge and Ho (2003), thegovernment plays an important support role, forexample, in terms of regulation and action to persuadeonline traders to accommodate the e-lifestyle. Anotherform of GS is to facilitate e-commerce (Tan and Teo2000). Therefore, this study has adapted Tan and Teo’s(2000) instrument to measure the construct. Four itemswere selected to account for the respondents’ controlbeliefs (cb) as well as their perceptions of GS (pf).Appendix 1 shows the items developed to measure theconstruct of government-support belief.

5.3. Hypotheses

In the IS field, Pavlou and Fygenson (2006) viewed PBCas a two-dimensional construct formed by two under-lying dimensions (SE and controllability), allowing amore detailed examination of external control beliefs. Inthis context, the more conditions that are facilitated fornew technologies and the more SE individuals haveconcerning the use of new technologies, themore successthere is in the adoption or diffusion of these technolo-gies. Hypotheses H1 and H2 will address the impact ofall the sub-constructs related to exogenous variables(SE, GS, FR and FT) on both endogenous variables:individual BI and PBC towards IB.

H1: There will be a positive relationship between userBI towards the use of IB and all the sub-constructs

related to conditions facilitated (technology, resourcesand GS), on the one hand, and individual SE, on theother.

H1 comprises the following four sub-hypotheses:

H1a: There will be a positive relationship between BItowards IB and individual SE.

H1b: There will be a positive relationship between BItowards IB and facilitated technology.

H1c: There will be a positive relationship between BItowards IB and facilitated resources.

H1d: There will be a positive relationship between BItowards IB and GS.

H2: There will be a positive relationship betweenindividual PBC towards the use of IB and success inthe adoption or diffusion of IB.

H2 comprises the following four sub-hypotheses:

H2a: There will be a positive relationship betweenindividual PBC and individual SE towards IB.

H2b: There will be a positive relationship between PBCand facilitated technology for IB.

H2c: There will be a positive relationship between PBCand facilitated resources for IB.

H2d: There will be a positive relationship between PBCand GS for IB.

5.4. Sample and data collection

A self-administered survey was distributed randomlyto respondents, who were bank customers in Yemen.The centres of distribution were the 14 bank head-quarters located in the city of Sana’a. Due toconfidentiality and other concerns that restricted thebanks from providing a mailing list of their customers,convenience sampling was used. This study distributed1000 questionnaires, of which 623 were returned, aresponse rate of 62%. Out of the received forms, therewere 254 incomplete. The remaining 369 represent thefinal achieved sample size with a gross response rate of59.22% (of the received forms).

The items in the questionnaire were constructedand selected based on the IB literature, as discussedabove. Constructs were operationalised using validateditems from prior research, chosen to fit the studycontext. A seven-point Likert scale was utilised toensure statistical variability among survey responsesfor all constructs. To ensure that measurement scaleswere appropriate to the current context, qualitativeinterviews were conducted with two academics inrelated fields to carry out the necessary wordingchanges.

The respondent profiles show that the majority ofrespondents are males (81.8%), have Yemeni citizen-ship (94.9%), reside in the city of Sana’a (78.6%), aremarried with children (61.8%), have a bachelor’s

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degree (55.0%) and are in their thirties (39.8%). Table1 presents information on the 12 demographicvariables to visually explain the respondents’ profile.

Based on the comparison to some other studies(Polatoglu and Ekin 2001, Mattila et al. 2003, Luarnand Lin 2005), the response rate for the present study is

considered satisfactory. Furthermore, this study fol-lows the guidelines provided by Malhotra (2009), Hairet al. (2006) and Tabachnick and Fidell (2007), whichis as follows. First, as a rule of thumb, sample sizeshould be larger than 30 and less than 500 (Hair et al.2006). Second, where the sample is to be broken into

Table 1. Summary of respondents’ demographic profile.

Variable Value Frequency Percentage

Gender Male 302 81.8Female 67 18.2

Age Twenties (19–29 years) 135 36.6Thirties (30–39 years) 147 39.8Forties (40–49 years) 74 20.1Older (� 50 years) 13 3.5

Marital status Single 86 23.3Married with children 228 61.8Married without children 55 14.9

Nationality Yemeni 350 94.9Non-Yemeni 19 5.1

Resident area Sana’a area 290 78.6Other areas 79 21.4

Personal income Less than 30,001 Yemeni Rail (YER) 55 14.930,001–60,000 YER 111 30.160,001–120,000 YER 140 37.9120,001–180,000 YER 27 7.3Above 180,001 YER 36 9.8

Profession Managerial 132 35.8Clerks 65 17.6Specialists 43 11.7Technicians 31 8.4Agricultural 5 1.4Engineers 27 7.3Handcraft 5 1.4Simple job 12 3.3Other 49 13.3

Sector Public sector 91 24.7Private sector 216 58.5Individual business 62 16.8

Education Elementary 31 8.4Secondary and diploma 86 23.3Undergraduate 203 55.0Postgraduate and professional 49 13.3

Residence ownership Own 154 41.7Family house 63 17.1Own with mortgage 12 3.3Rent 126 34.1Given for services 12 3.3Other 2 .5

Employer’s industry Manufacturing 28 7.6Services 83 22.5Government 26 7.0Commercial 99 26.8Banking and finance 127 34.4Other 6 1.6

Household income Less than 40,001 YER 31 8.440,001–80,000 YER 90 24.480,001–120,000 YER 78 21.1120,001–160,000 YER 66 17.9160,001–200,000 YER 27 7.3200,001–240,000 YER 29 7.9Above 240,001 YER 48 13.0

Total 369 100.0

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subsamples, a minimum of 30 cases for each categoryare necessary (Hair et al. 2006). Third, in order to meetthe assumptions necessary for multivariate analysis interms of factor analysis and regression, the number ofcases in the sample should be more than 10 times thenumber of the variables in the study (Tabachnick andFidell 2007).

6. Data analysis

6.1. Factors analysis of control belief

The 19 items assessing SE and facilitating conditionswere subject to PCA using SPSS version 16. Prior toPCA, the suitability of the data for factor analysis wasassessed. Inspection of the correlation matrix revealedthe presence of a coefficient of .3 and above. PCArevealed the presence of five components with eigen-values exceeding 1, explaining 28.418%, 9.517%,8.302%, 6.753% and 5.573% of the variance, respec-tively. An inspection of the scree plot revealed a clearbreak after the fourth component. Using Catell’s(1966) scree test, it was decided to retain fourcomponents for further investigation. This was sup-ported by the result of parallel analysis, which showedonly four components with eigenvalues exceeding thecorresponding criterion values for randomly generateddata matrices of the same size (19 variables 6 369respondents). To aid in the interpretation of these fourcomponents, oblimin rotation was performed. Therotated solution revealed the presence of a simplestructure with four components showing a number ofstrong loadings and all variables loading substantiallyon only one component. The result of this analysissupports the use of the SE items, GS items, FR itemsand FT items as separate scales, as suggested by theconceptual framework.

The coding of items related to IB control belief(salient belief) included the individual’s expectations ofsalient SE (six items), beliefs dealing with the presenceor absence of GS for IB (four items), technologicalfacilitating conditions (three items) and resource-related facilitating conditions (three items). As shownin Table 2, the control-belief structure decomposes intotwo major dimensions: (1) SE and (2) facilitatingconditions.

The study scales are sufficiently reliable. Tabach-nick and Fidell (2007) and Hair et al. (2006)recommend using Cronbach’s a to assess scalereliability, with a values greater than or equal to .70indicating sufficient reliability. As reported in Table 2,a scores for all of the measurement scales in the presentstudy exceeded the .60 cut-off value. The final a valuesfor SE, GS, FR and FT are .85, .82, .60 and .70,respectively.

According to Ajzen (2002), the belief-based mea-sures approach has the advantage of providing aninsight into the cognitive foundation underlyingperceptions of behavioural control. In this approach,two sets of questions can be posed with respect to eachfactor. Respondents can be asked to indicate (a) theperceived likelihood of the behaviour (strength ofcontrol belief) and (b) the power to facilitate perfor-mance of the behaviour (power of control belief). APCA was followed by oblique rotation, since anoblique-factor solution can provide a good fit to thedata (Ajzen 2002). Oblique rotation was chosenbecause some correlation was expected among thevariables. A factor loading of .3 was used as a lowercut-off value, as recommended for exploratory analysis(Pallant 2005). The factor-correlation matrix, afteroblique rotation, showed no correlation greater than.30, indicating that the oblimin rotation was reason-able. The correlations between the components arequite low, so in this study very similar solutions areexpected by either varimax or oblimin rotation (Pallant2005). The facilitating conditions construct is furtherbroken down into three dimensions: the resource-related facilitating condition, the technological facil-itating condition and the government-support condi-tion. The result of the analysis indicates the following:

Table 2. PCA structure matrix result: control belief.

Item a 6 b

Components

SE GS FR FT 5

DSE2 .769DSE1 .762DSE3 .587FT1 .754FR5 .752FR4 .737GS3 .815GS1 .807GS4 .806GS2 .774FR3 .734FR1 .673FR2 .638FT7 .845FT8 .783FT6 .770FT5 .425FT2 .421FT3 7.770Eigenvalues 5.684 1.903 1.660 1.351 1.115Variance explained 28.418 9.517 8.302 6.753 5.573Cronbach’s a .85 .82 .60 .70 –

Notes: (a) Total variance extracted by the four factors: 58.56%;KMO ¼ .858; Bartlett’s test 5 .001. (b) Extraction method: PCA.(c) Rotation method: oblimin with Kaiser normalisation. (d) DSE—decomposed self-efficacy.

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(1) Respondents involved in the study sampledistinguished variation among the four dimen-sions of control belief, whereby these findingsare moderately close to the TPB classificationof control belief.

(2) The assessment of control-belief constructs,according to the respondents, appears to showthat there are items which coincide partially.According to Ajzen’s (2002) study, there wasconsiderable overlap between control beliefsthat predicted controllability (facilitation) andSE.

(3) Ajzen (2002) pointed out that measures of PBChave often lacked high internal consistency.

(4) The findings also show that PCA is signifi-cantly appropriate, with a Kaiser–Meyer–Olkin(KMO) measure of sampling adequacy of .858.

Although the first reliability test on items expectedto underlie beliefs regarding FR was found to be belowthe satisfactory reliable level (.6301), this result can stillbe accepted, since Ajzen (2002) has mentioned thatmeasures of PBC have often lacked high internalconsistency. Contrary to the present researcher’sexpectations, the result of factor analysis revealedthat there are three items (FT1: ‘Have the computers,Internet access and applications needed to use IB’;FR4: ‘Have the time to set up IB services’ and FR5:‘Have enough money to use IB services’) which, whenloaded together with all three SE items forming thefirst component, explain 28.418% of the variance inPBC. In developing countries like Yemen, these threeitems can be considered highly relevant to individualSE rather than being part of the facilitation items. Thestudy investigated these three items (FT1, FR4 andFR5) and decided to retain them as measurements ofthe SE scale. A possible explanation as to why thesevariables are retained for the SE scale may be found inArmitage and Conner (1999a, 1999b), Liu et al. (2007),Amireault et al. (2008) and Trafimow et al. (2002), whoprovided evidence that PBC is a multidimensionalconstruct. In addition, while Ajzen (2002) points out

that it should be sufficient to compute a single overallindex of PBC, sometimes a research objective mayrequire separate measures of SE and controllability. Apossible explanation for retention of these items couldbe based on the fact that they render themselves morerelevant as a SE group than facilitation items. Inpractical terms, the study may suggest that individualSE can be measured if there is an understanding of theextent to which individuals perceive the importance ofthe availability of computers, Internet access andapplications needed to use IB (FT1), have the time toset up IB services (FR4) and have enough money to useIB services (FR5). Moreover, the inclusion of thesethree variables raises questions of both constructreliability and validity. Component one, with itsloaded items, was tested again for reliability, and theresult showed that internal consistency reliability wasobtained, with Cronbach’s a of .85.

6.2. Testing of hypotheses

Ajzen (1991) said that ‘the more resources andopportunities individuals believe they possess, andthe fewer obstacles or impediments they anticipate, thegreater should be their perceived control over thebehaviour’. Table 3 displays the relationships betweenthe individual’s control beliefs regarding IB (asmeasured by facilitating condition and SE) as investi-gated using Pearson’s correlation coefficient. Prelimin-ary analyses were conducted to ensure that there wasno violation of the assumptions of linearity, normalityor homoscedasticity.

Table 3 displays the descriptive analysis andcorrelation-test results for these variables. There wasa strong, positive correlation between the individualSE with respect to IB and PBC (r ¼ .798, p 5 .005).SE was found to be correlated, strongly and positivelydirection, with BI for IB (r ¼ .564, p 5 .005). Con-cerning the three facilitation variables, positive,significance and satisfactory relationships were foundbetween PBC and FR (r ¼ .285, p 5 .005) andbetween PBC and GS (r ¼ .272, p 5 .005). There

Table 3. Means, standard deviations, a reliability and zero-order correlation (control-belief variables vs. PBC and BI).

Variables M SDDependent Variable

(DV)1 DV2

Control-belief IVs

IV1 IV2 IV3 IV4

DV1–PBC 22.85 8.55 (.90)DV2–BI 25.04 8.07 .620** (.91)IV1–SE 131.82 60.70 .798** .564** (.85)IV2–technology (FT) 62.96 34.77 .372** .384** .450** (.70)IV3–resources (FR) 66.04 31.40 .285** .287** .334** .234** (.60)IV4–government (GS) 80.37 42.46 .272** .320** .362** .364** .253** (.82)

Notes: **Significance at p 5 .01. Figures in brackets represent a-reliability coefficients.

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was a positive correlation of moderate magnitudefound between FT and PBC (r ¼ .372, p 5 .005).

Hypotheses H2a, H2b, H2c and H2d were testedusing hierarchical regression, wherein the four controlbeliefs were regressed against the criterion variablePBC. The results are shown in Table 4. The resultsreveal that:

. In model 1, the standardised coefficient (b) valuesfor technology (FT), resources (FR) and GS arepositive and significance. The p values werep ¼ .000 4 .001, p ¼ .000 4 .001 and p ¼ .0194 .05 for technology, resources and GS, respec-tively. Therefore, there are significant relation-ships between PBC and individual perception ofthese three variables. Thus, the results of model 1support hypotheses H2b, H2c and H2d.

. In model 2, an examination of the variables inthe equation table indicates that the standar-dised coefficient (b) value for SE is positive andsignificance at p ¼ .000 5 .001. Meanwhile,the b values for technology, resources and GSare no longer significance when these variablesare entered into the regression equation withSE.

. This indicated that there is a positive relationshipbetween PBC and individual SE for IB services;this supports hypothesis H2d. This study canascertain that facilitating condition variables ontheir own are salient (marginal) predictors ofPBC. However, in combination with the SEvariable their effects are not significance.

. The results of model 2 demonstrated that thestandardised coefficient (b) value of SE was 79%,

Table 4. Results of hierarchal regression: control belief vs. PBC.

Unstandardisedcoefficients

Standardisedcoefficients

Predictor variable b Std. error b t

Step 1Technology facilitating condition .070 .013 .284 5.555*Resources facilitating condition .051 .013 .188 3.815*GS .024 .010 .121 2.347**

R .438R2 .192Adjusted R2 .185

Analysis of variance (ANOVA)

df Sum of squares Mean square F Significance of F

Regression 3 5,162.091 1,720.697 28.906 .000 (c)Residual 365 21,727.411 59.527

Unstandardisedcoefficients

Standardisedcoefficients

Predictor variable b Std. error b t

Step 2Technology facilitating condition .005 .009 .020 .554Resources facilitating condition .006 .009 .023 .685GS 7.006 .007 7.028 7.788SE .111 .005 .791 21.181*

R .799R2 .638Adjusted R2 .634

Analysis of variance (ANOVA)

df Sum of squares Mean square F Significance of F

Regression 4 17,157.002 4,289.251 160.420 .000(d)Residual 364 9,732.499 26.738

Notes: *p 5 .001, **p 5 .05.

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which implies that when the four variables areentered in the regression equation, SE has themajor contribution in predicting the variance inIB.

Control-belief components of SE and facilitatingcondition were treated as the independent variables(IVs) and regressed separately with the dependentvariable PBC. The following relationships wereobtained.

Step 1 examines the three IVs (facilitating conditionvariables) entered into the regression equation as

formative predictors for the PBC; this provides threeregression-equation models, as follows:

PBC ¼ 17:092þ :091 FTð Þ þ e ð2Þ

PBC ¼ 14:085þ :079 FTð Þ þ :057 FRð Þ þ e ð3Þ

PBC ¼ 13:120þ :070 FTð Þ þ :051 FRð Þ þ :024 GSð Þ þ e

ð4Þ

Step 2 shows the simple regression for SE as an IVfor the criterion variable PBC, which indicates that

Table 5. Summary assessment of research hypotheses.

Criterion(dependent variable) Hypotheses

Predictor (IV)

Statistic tests

ResultsAnalysis techniques BI H1 t Sig. b

Stepwise regression Step 1 H1b Technological facilitation 3.997 .000 .200 SupportedH1c Resource-related facilitation 7.085 .000 .349 SupportedH1d GS 2.737 .007 .133 Supported

Step 2 H1a SE 6.968 .000 .444 SupportedH1b Technological facilitation 2.377 .018 .131 SupportedH1c Resource-related facilitation 2.309 .022 .086 SupportedH1d GS 1.768 .038 .090 Supported

PBC H2Stepwise regression Step 1 H2b Technological facilitation 5.555 .000 .284 Supported

H2c Resource-related facilitation 3.815 .000 .188 SupportedH2d GS 2.347 .050 .121 Supported

Step 2 H2a SE 21.181 .000 .791 SupportedH2b Technology facilitating .554 .853 .020 RejectedH2c Resource-related facilitation .685 .310 .023 RejectedH2d GS 7.788 .112 7.028 Rejected

Figure 2. Control-belief components.

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79% of the variance in PBC can be explained by thevariance of individual SE.

PBC ¼ 11:144þ :111 SEð Þ þ e ð5Þ

As can be seen from the hierarchal-regressionresults in Step 1 (as shown in Table 4), hypothesesH2b, H2c and H2d are supported with significantpositive relationships to PBC. In Step 2 (as shown inTable 4), it is shown that hypothesis H2a is supported.Hypotheses H1a, H1b, H1c and H1d (as shown inFigure 2) are also supported and indicate positive,significance relationships between the individual’s BIand all tested sub-constructs, namely SE, technology,resources and GS. Table 5 displays a summaryassessment of the research hypotheses.

7. Discussion and conclusion

This research used the construct of PBC, based onTPB, supplemented by multiple tools used to investi-gate control beliefs and facilitating conditions. In thecontrol-belief structure emerging from the currentstudy, it is seen that control belief decomposes intotwo dimensions: SE and facilitating conditions. Inturn, the facilitating conditions construct broke downinto three other dimensions: the resource-relatedfacilitating condition, technological facilitating condi-tion and government-support facilitating condition. InAjzen’s (2002) words,

‘The hierarchical model implies that although per-ceived SE and perceived controllability can be reliablydistinguished, they should nevertheless be correlatedwith each other. Unfortunately, the studies that haveprovided evidence for the discriminant validity of SEand controllability have failed to examine convergence’

(p. 14)

The results of this study are not consistent withthose of previous studies, in that this study argues thatSE measures accounted for additional variance inintention, but controllability items predicted intentiononly when combined with SE items (Ajzen 2002). Thestepwise-regression findings presented in Table 4 showthat the controllability factor, represented by the threefactors FT, FR and GS, contributes significantly to thevariation of intention, but when combined with SE itdoes not. One of the complexities of TPB comes fromcontrol variables, an issue discussed by Mathiesonet al. (2001), who noted that control variables for aparticular system vary by context. The study findingsand hypothesis testing for SE are in agreement withTaylor and Todd (1995a, 1995b), who hypothesisedthat SE is positively related to BI. The hierarchalmultiple-regression analysis provides a strong explana-tion of this by showing that the three facilitating

condition components (FT, FR and GS) on their ownare salient predictors of the PBC. However, incombination with SE, their effects are not significance.Armitage and Conner (1999b) examined the relation-ship between specific beliefs and the separate measuresof SE and controllability, demonstrating the proble-matic nature of the distinction between them.Although much of this research tends not to contradictAjzen, the findings of previous studies by Liu et al.(2007), Amireault et al. (2008), Trafimow et al. (2002)and Kraft et al. (2005), as well as the empirical findingsof the current study, seem to support the argumentthat PBC is a multidimensional construct that consistsof two separate but related components. The findingsof this study provide valuable insights into the under-lying contextual factors of PBC-adoption behaviourfor researchers and practitioners. In other words, thisstudy provides insight into the two factors involved ininfluencing individuals’ intentions as well as their PBC.The findings have implications for government, espe-cially in developing countries such as Yemen, in thatthe adoption of IB services does not only occurbecause of the potential adopter’s SE, but also as aresult of government facilitation of more and bettertechnology, resources and support.

This study also provides information about theeffects of those factors on the IB-adoption behaviourof customers from a developing country. The resultsimply that SE is an important mechanism for influen-cing IB adoption, while facilitating conditions in termsof technology, resources and GS are not as importantin determining the PBC adoption behaviour of IBservices. This might be due to decreasing access to theInternet and computer technology at home, at workand in libraries. Meanwhile, facilitating conditions arestill important mechanisms for influencing intention-adoption behaviour. In other words, the results showthat although facilitating conditions have a fairlysignificance effect on PBC, they can determine adop-tion intention almost alone.

SE is an important predictor of PBC and adoptionintention behaviour (see Figure 2). Consequently,positive customer assessment of their own SE in theIB context regulates the influence of their PBC andultimately their BI towards IB acceptance. Conversely,the non-significance influence of perceived facilitatingconditions on PBC depends on customer SE (see Step 2in Table 4). Since there is a positive and significanceinfluence of perceived facilitating condition constructson individual BI to use IB services (as shown in Figure2), which theoretically depends on customers’ PBC,managers, banks and policymakers should worktogether to foster people’s interest in IB services byproviding more resources, technology and GS for theirdiffusion. This will not only serve the potential

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adopters, but will also provide support to thetechnological updating of the banking system bybuilding customers’ SE. The findings of this studyhave implications for IB-system providers, IB de-signers and IB managers. Considering that Yemenibanks have been investing in IB systems, it is veryimportant to ensure that customers use and benefitfrom IB services. This goal might be achieved ifattention is given to providing IB systems thatcustomers deem unproblematic to control, and forwhich SE is attainable. Marketing and promotionalefforts for IB should focus on creating favourable userperceptions of IB systems by addressing the questionsof resources, technology and government-supportavailability as well as the beneficial outcomes of IB.Regular IB seminars, training and educational pro-grammes for customers may foster a feeling of SEwhich will also be of great value. As well, providinginformation on services, technical support and anyother needed support to enlarge the general SE andconfidence of potential users, exposing these potentialusers to the concept of IB and creating awareness ofthe benefits, user-friendliness and security of IBtechnology all will have an impact on IB diffusion.However, there is limited published work exploring thefactors that influence the acceptance of IB in thecontext of developing countries in the Middle East.This article has focused on Yemen, which has a diverseimmigrant population, a legal system and a developingeconomy, and, therefore, makes an interesting andunique study. The researcher believe that the findingsfrom this study will aid local and multinational banks todevelop strategic plans in order to promote products andservices, introduce more sophisticated electronic facilitiesand applications and, most importantly, design simple,useful and trustworthy IB systems. The study’s implica-tions will enable not only researchers but also practi-tioners to develop further survey and marketingprogrammes. Furthermore, it will raise awareness aboutthe effects of GS on IB, which can also help planners toencourage decision makers to provide infrastructure andsupport to develop e-commerce in Yemeni society whichis crucial for the development of IB. Accordingly, it isimportant for IB researchers, planners and policymakersto provide sufficient rules, regulations, time and funding,as well as ICT-related resources.

Finally, I would like to mention that the ambiguityof the conceptualisation and operationalisation of thePBC concept cause me to feel that I did not tackle thistopic very well because the IT domain literate review(banking behaviour) has been used extensively in thepresent study and this is might be a sort of short-comings. Forthrightly, I suggest that one task forfuture research could be a possible re-operationalisa-tion of the PBC construct. Also, further replications of

this study’s results in the bigger domain of thepsychology literature (not only in the banking andtechnology-acceptance domains) will certainly help tostrengthen the findings.

References

Ajzen, I., 1991. The theory of planned behaviour. Organiza-tional Behaviour and Human Decision Processes, 50 (2),179–211.

Ajzen, I., 2002. Constructing a theory of planned behaviourquestionnaire: conceptual and methodological considera-tions. Available from Ajzen Homepage at http://www.people.umass.edu/aizen/pdf/tpb.measurement.pdf [Ac-cessed 26 April 2005].

Ajzen, I. and Madden, T.J., 1986. Predication of goal-directed behavior: attitude, intentions, and perceivedbehavioural control. Journal of Experimental SocialPsychology, 22 (1), 453–474.

Al-Sabbagh, I. and Molla, A., 2004. Adoption and use ofInternet banking in the sultanate of Oman: an explora-tory study. Journal of Internet Banking and Commerce;Web Archive, 9 (2).

Amireault, S., et al., 2008. Moderators of the intention–behaviour and perceived behavioural control-behaviourrelationships for leisure-time physical activity [online].International Journal of Behavioral Nutrition and PhysicalActivity, 5 (7). Available from: http://www.ijbnpa.org/content/pdf/1479-5868-5-7.pdf [Accessed 2 November2011].

Armitage, C.J. and Conner, M., 1999a. Distinguishing percep-tions of control from self-efficacy: predicting consumptionof a low-fat diet using the theory of planned behaviour.Journal of Applied Social Psychology, 29 (1), 72–90.

Armitage, C.J. and Conner, M., 1999b. The theory ofplanned behaviour: assessment of predictive validity andperceived control. British Journal of Social Psychology,38 (1), 35–54.

Armitage, C.J. and Conner, M., 2001. Efficacy of the theoryof planned behaviour: a meta-analytic review. BritishJournal of Social Psychology, 40 (4), 471–499.

Bentley, L.D. and Whitten, J.L., 2007. Systems analysis anddesign for the global enterprise. 7th ed. New York:McGraw-Hill.

Bhattacherjee, A., 2000. Acceptance of e-commerce services:the case of electronic brokerages. IEEE Transactions onSystems, Man and Cybernetics – Part A: Systems andHumans, 30 (4), 411–420.

Bhattacherjee, A. and Sanford, C., 2009. The intention–behaviour gap in technology usage: the moderating roleof attitude strength. Behaviour & Information Technology,28 (4), 389–401.

Brown, I., et al., 2004. The impact of national environmenton the adoption of Internet banking: comparing Singa-pore and South Africa. Journal of Global InformationManagement, 12 (2), 1–26.

Catell, R.B., 1966. The Scree test for number of factors.Multivariate Behavioural Research, 1 (1), 245–276.

Celik, H., 2008. What determines Turkish customers’acceptance of internet banking. International Journal ofBank Marketing, 26 (5), 353–370.

Chang, M.K. and Cheung, W., 2001. Determinants of theintention to use Internet/WWW at Work: a confirmatorystudy. Information & Management, 39 (1), 1–14.

Behaviour & Information Technology 17

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ahra

in]

at 1

4:51

08

Oct

ober

201

2

Chen, C.D. and Cheng, C.J., 2009. Understanding consumerintention in online shopping: a respecification andvalidation of the DeLone and McLean model. Behaviour& Information Technology, 28 (4), 335–345.

Cheung, W., Chang, M.K., and Lai, V.S., 2000. Prediction ofInternet and World Wide Web usage at work a test of anextended Triandis model. Decision Support Systems, 30(1), 83–100.

Compeau, D.R. and Higgins, C.A., 1995. Computer self-efficacy: development of a measure and initial test. MISQuarterly, 19 (2), 189–211.

Dabholkar, P.A., 1996. Consumer evaluations of newtechnology-based self-service options: an investigationof alternative models of service quality. InternationalJournal of Research in Marketing, 13 (1), 29–51.

Davis, F.D., 1986. A technology acceptance model forempirically testing new end-user information systems:theory and results. Thesis (PhD), Sloan School ofManagement, MIT.

Davis, F.D., Bagozzi, R.P., and Warshaw, P.R., 1989. Useracceptance of computer technology: a comparison of twotheoretical models. Management Science, 35 (8), 982–1003.

Doll, J. and Ajzen, I., 1992. Accessibility and stabilityof predictors in the theory of planned behaviour.Journal of Personality and Social Psychology, 63 (5),754–765.

Fishbein, M. and Ajzen, I., 1975. Belief, attitude, intentionand behaviour: an introduction to theory and research.USA: Addison-Wesley. Electronic version available fromhttp://www.people.umass.edu/aizen/f&a1975.html [Ac-cessed 23 January 2005].

Gist, M.E. and Mitchell, T.R., 1992. A theoretical analysis ofits determinants and malleability. The Academy ofManagement Review, 17 (2), 183–211.

Grant, D.M., Malloy, A.D., and Murphy, M.C., 2009. Acomparison of student perceptions of their computerskills to their actual abilities. Journal of InformationTechnology Education, 8 (1), 141–160.

Hair, J.F., et al., 2006. Multivariate data analysis. 6th ed.Upper Saddle River, NJ: Prentice Hall International.

Hartshorne, R. and Ajjan, H., 2009. Examining studentdecisions to adopt Web 2.0 technologies: theory andempirical tests. Journal of Computing in Higher Educa-tion, 21 (1), 183–198.

Hartzel, K., 2003. How self-efficacy and gender issues affectsoftware adoption and use. Communications of the ACM,46 (9), 167–171.

Hernandez, J.M.C. and Mazzon, J.A., 2007. Adoption ofInternet banking: proposition and implementation of anintegrated methodology approach. International Journalof Bank Marketing, 25 (2), 72–88.

Hill, T., Smith, N.D., and Mann, M.F., 1987. Role of efficacyexpectations in predicting the decision to use advancedtechnologies: the case of computers. Journal of AppliedPsychology, 72 (2), 307–313.

Hung, S-Y.,Ku, C-Y., andChang, C.-M., 2003. Critical factorsof WAP services adoption: an empirical study. ElectronicCommerce Research and Applications, 2 (1), 42–60.

Igbaria, M. and Iivari, J., 1995. The effects of self-efficacy oncomputer usage, Omega. International Journal Manage-ment Science, 23 (6), 587–605.

Jiang, J., et al., 2000. E-commerce user behaviour model: anempirical study. Human Systems Management, 19 (4),265–276.

Kraft, P., et al., 2005. Perceived difficulty in the theory ofplanned behaviour: perceived behavioural control oraffective attitude. British Journal of Social Psychology, 44(1), 479–496.

Lai, V.S.L. and Li, H.L., 2004/2005. Technology acceptancemodel for Internet banking: an invariance analysis.Information & Management, 42 (2), 373–386.

Lassar, W., Manolis, C., and Lassar, S.S., 2005.The relationship between consumer innovativeness,personal characteristics, and online banking adoption.International Journal of Bank Marketing, 23 (2), 176–199.

Laudon, K. and Laudon, J., 2010. Management informationsystems: managing the digital firm. 11th ed. Upper SaddleRiver. NJ: Pearson Prentice Hall.

Lee-Partridge, J. and Ho, P.S., 2003. A retail investor’sperspective on the acceptance of Internet stock trading.In: Proceedings of the 36th Hawaii international con-ference on system sciences – 2003. http://www.hicss.hawaii.edu/HICSS36/HICSSpapers/INEMG02.pdf [Accessed16 July 2005].

Liao, S., et al., 1999. The adoption of virtual banking: anempirical study. International Journal of InformationManagement, 19 (1), 63–74.

Liu, Y., Doucette, W.R., and Farris, K.B., 2007. Perceiveddifficulty and self-efficacy in the factor structure ofperceived behavioural control to seek drug informationfrom physicians and pharmacists. Research in Social andAdministrative Pharmacy, 3 (2), 145–159.

Lopez, D.A. and Manson, D.P., 1997. A study of individualcomputer self-efficacy and perceived usefulness of theempowered desktop information system. Available fromhttp://www.csupomona.edu/*jis/1997/Lopez.pdf [Ac-cessed 11 April 2009].

Lu, J., et al., 2003. Technology acceptance model forwireless Internet. Internet Research, 13 (3), 206–222.

Luarn, P. and Lin, H., 2005. Toward an understanding of thebehavioural intention to use mobile banking. Computersin Human Behaviour, 21 (6), 873–891.

Malhotra, N.K., 2009. Marketing research: an appliedorientation. 6th ed. Upper Saddle River, NJ: PearsonPrentice Hall.

Malhotra, P. and Singh, B., 2004. Adoption of Internetbanking: an empirical investigation of Indian bankingsector. Journal of Internet Banking and Commerce, 9 (2).Web Archive.

Mathieson, K., 1991. Predicting user intention: comparingthe technology acceptance model with the theory ofplanned behaviour. Information Systems Research, 2 (3),173–191.

Mathieson, K., Peacock, E., and Chin, W., 2001. Extendingthe technology acceptance model: the influence ofperceived user resources. The DATA BASE for Advancesin Information Systems, 32 (3), 86–112.

Mattila, M., Karjaluoto, H., and Pento, T., 2003. Internetbanking adoption among mature customers: earlymajority or laggards. Journal of Services Marketing, 17(5), 514–528.

Murphy, G.D., 2009. Improving the quality of manuallyacquired data: applying the theory of planned behaviourto data quality. Reliability Engineering and SystemSafety, 94 (1), 1881–1886.

Ndubisi, N.O., 2007. Customers’ perceptions and intentionto adopt Internet banking: the moderation effectof computer self-efficacy. AI & Society, 21 (3), 315–327.

18 A.H.S. Zolait

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ahra

in]

at 1

4:51

08

Oct

ober

201

2

Nielsen, J.F., Viggo, H., and Niels, P.M., 2003. Drivers ofadoption of Internet-based marketing channels. In:University of AARHUS Denmark, Department of Man-agement, Working Paper 2003-3.

Pallant, J., 2005. SPSS survival manual: a step by step guideto data analysis using SPSS version 12. 2nd ed. NewYork: Open University Press, McGraw-Hill Education.

Pavlou, P.A., 2002. What drives electronic commerce? Atheory of planned behaviour perspective. In: Academy ofmanagement proceedings, 2002, A1–A6 [Accessed 28 May2005, from ebscohost].

Pavlou, P.A. and Fygenson, M., 2006. Understanding andpredicting electronic commerce adoption an extension ofthe theory of planned behaviour. MIS Quarterly, 30 (1),115–143.

Polatoglu, V.N. and Ekin, S., 2001. An empirical investiga-tion of the Turkish consumers’ acceptance of Internetbanking services. International Journal of Bank Market-ing, 19 (4), 156–165.

Ratnasingam, P., Gefen, D., and Pavlou, P.A., 2005. Therole of facilitating conditions and institutional trust inelectronic marketplaces. Journal of Electronic Commercein Organizations, 3 (3), 69–82.

Rhodes, R. and Courneya, K., 2003a. Self efficacy, controll-ability and intentions in the theory of planned behaviour:measurement redundancy or causal independence. Psy-chology and Health, 18 (1), 79–91.

Rhodes, R. and Courneya, K., 2003b. Investigating multiplecomponents of attitude, subjective norm and perceivedcontrol: an examination of the theory of plannedbehaviour in the exercise domain. British Journal ofSocial Psychology, 42 (1), 129–146.

Rotchanakitumnuai, S. and Speece, M., 2003. Barriers toInternet banking adoption: a qualitative study amongcorporate customers in Thailand. International Journal ofBank Marketing, 21 (6/7), 312–323.

Sciglimpaglia, D. and Ely, D., 2003. Internet banking: acustomer-centric perspective [online]. In: Proceedings of the35th Hawaii international conference on system sciences, 7–10 January, 2420–2429. Available from: http://ieeexplore.ieee.org/iel5/7798/21442/00994179.df?tp¼&isnumber¼&arnumber¼994179 [Accessed 29 October 2011].

Sekaran, U., 2003. Research methods for business: a skillbuilding approach. 4th ed. New York: John Wiley &Sons.

Shih, Y., 2006. The effect of computer self-efficacy onenterprise resource planning usage. Behaviour & Informa-tion Technology, 25 (5), 407–411.

Shih, Y. and Fang, K., 2004. The use of a decomposed theoryof planned behaviour to study Internet banking inTaiwan. Internet Research, 14 (3), 213–223.

Stafford, B., 2001. Risk management and Internet banking:what every banker needs to know. Community Banker, 10(2), 48–49.

Suoranta, M. and Mattila, M., 2004. Mobile banking andconsumer behaviour: new insights into the diffusionpattern. Journal of Financial Services Marketing, 8 (4),354–366.

Sutton, S., et al., 2003. Eliciting salient beliefs in research onthe theory of planned behaviour: the effect of questionwording. Current Psychology: Developmental, Learning,Personality, Social, 22 (3), 234–251.

Tabachnick, B.G. and Fidell, L.S., 2007. Using multivariatestatistics. 5th ed. Boston: Pearson Education.

Tan, M. and Teo, T.S.H., 2000. Factors influencing theadoption of Internet banking. Journal for association ofinformation system, 1 (5) [online]. Available from www.isworld.org [Accessed 18 March 2007].

Tavousi, M., et al., 2009. Are perceived behavioural controland self-efficacy distinct constructs? European Journal ofScientific Research, 30 (1), 146–152.

Taylor, S. and Todd, P.A., 1995a. Understanding informa-tion technology usage: a test of competing model.Information Systems Research, 6 (2), 144–176.

Taylor, S. and Todd, P., 1995b. Decomposition and cross-over effects in the theory of planned behaviour: a study ofconsumer adoption intentions. International Journal ofResearch in Marketing, 12 (2), 137–155.

Teo, T., 2009. The impact of subjective norm and facilitatingconditions on pre-service teachers’ attitude towardcomputer use: a structural equation modeling of anextended technology acceptance model. Journal ofEducational Computing Research, 40 (1), 89–109.

Thompson, R.L., Higgins, C.A., and Howell, J.M., 1991.Personal computing: toward a conceptual model ofutilization. MIS Quarterly, 15 (1), 124–143.

Thompson, R.L., Higgins, C.A., and Howell, J.M., 1994.Influence of experience on personal computer utilization:testing a conceptual model. Journal of ManagementInformation Systems, 11 (1), 167–187.

Trafimow, D., et al., 2002. Evidence that perceived beha-vioural control is a multi-dimensional construct: Per-ceived control and perceived difficulty. The BritishJournal of Social Psychology, 41 (1), 101–121.

Trafimow, D., et al., 2004. Affective and cognitive control ofpersons and behaviours. British Journal of SocialPsychology, 43 (1), 207–224.

Triandis, H.C., 1980. Values, attitudes, and interpersonalbehaviour. In: M.M. Page, ed. Nebraska symposium onmotivation, 1979: beliefs, attitudes, values. Lincoln:University of Nebraska Press, 195–259.

Venkatesh, V., 2000. Determinants of perceived ease of use:integrating control, intrinsic motivation, and emotioninto the technology acceptance model. InformationSystems Research, 11 (4), 342–365.

Venkatesh, V., et al., 2003. User acceptance of informationtechnology: toward a unified view.MIS Quarterly, 27 (3),425–478.

Wang, J. and Kim, S., 2007. Time to get in: The contrastingstories about government interventions in informationtechnology standards (the case of CDMA and IMT-2000 inKorea). Government Information Quarterly, 24 (1), 115–134.

Wang, Y., et al., 2003. Determinants of user acceptance ofInternet banking an empirical study. InternationalJournal of Service Industry Management, 14 (5), 501–519.

Zolait, A.H., 2010. An examination of the factors influencingYemeni bank users’ behavioural intention to use Internetbanking services. Journal of Financial Services Market-ing, 15 (1), 76–94.

Zolait, A.H.S. and Ainin, S., 2008. Incorporating theinnovation attributes introduced by Rogers’ theory intotheory of reasoned action: an examination of Internetbanking adoption in Yemen. Journal of Computer andInformation Science, 1 (1), 36–51.

Zolait, A.H.S., Sulaiman, A., and Alwi, S.F.S., 2008.Prospective and challenges of Internet banking inYemen: an analysis of bank websites. InternationalJournal of Business Excellence, 1 (3), 353–374.

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Appendix 1. A seven-point Likert scale (1) strongly disagree–(7) strongly agree

Construct Items developed to operationalise PBC References

PBC PBC1: I would be able to use IB. Taylor and Todd (1995a)PBC2: I have the resources necessary to make use of IB.PBC3: I have the knowledge necessary to make use of IB.PBC4: I have the ability to make use of IB.PBC5: Using IB would be entirely within my control.

SE cb1: I would feel comfortable using IB on my own. Taylor and Todd (1995a)pf1: For me , feeling comfortable using IB on my own is

important.cb2: If I wanted to, I could easily operate (application/

software) for IB from the bank portal (website) on my own.pf2: For me, being able to easily operate (application/software)

for IB from the bank (portal/website) on my own isimportant.

cb3: I would be able to use the (application/software) for IBeven if there was no one around to show me how to use it.

pf3: For me, being able to use the (application/software) for IBeven if there is no one around to show me how to use it isimportant.

Technologyfacilitating conditions

cb1: I have the computers, Internet access and applicationswhich I need to use IB.

Taylor and Todd (1995a) andShih and Fang (2004)

pf1: For me, availability of the computers, Internet access andapplications to use IB is important.

cb2: IB application ‘software’ might not be compatible withthe current system I use.

Taylor and Todd (1995a)

pf2: For me, a service having software that is compatible withthe current system I use is important.

cb3: I will have trouble accessing bank’s website/applicationwhen I want to use IB to manage my account online.

pf3: For me, whether or not I have trouble using IB is notimportant.

cb4: I am concerned about the security of IB services. Tan and Teo (2000) and Brownet al. (2004)pf4: For me, advances in Internet security, which provide a

safer IB, are important.cb5: I would have Internet access speed problems when I want

to make use of the IB services.pf5: For me, faster Internet access speed is important for IB.cb6: Banks’ transactional websites are available to use IB. Sciglimpaglia and Ely (2002)

and Malhotra and Singh(2004)

pf6: For me, the availability of the bank’s transactionalwebsites is very important to use IB.

cb7: A reliable Internet connection is available when I want touse IB.

Sciglimpaglia and Ely (2002)and WSIS (2003)*

pf7: For me, reliability of Internet connection services is veryimportant to use IB.

cb8: Wireless connection with high quality of Internet isavailable to use IB.

Suoranta and Mattila (2004)

pf8: For me, availability of wireless connection with highquality Internet is very important to use IB.

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Constructs Items developed to measure the construct References

FR cb1: There will not be enough computers for everyone to use IBservices.

Taylor and Todd (1995a)

pf1: For me, having computers for everyone is important.cb2: Being connected to the Internet will be too expensive.pf2: For me, being able to access Internet at a low price is important.cb3: I would not be able to use IB when I need it.pf3: For me, being able to use IB when I need it is important.cb4: I have the time to set up IB services. Taylor and Todd (1995b)pf4: Having the time to set up IB services is important to me.cb5: I have enough money to use IB services.pf5: Having enough money to use IB services is important to me.

GS cb1: The government gives support for electronic commerce. Tan and Teo, (2000)pf1: For me, GS for electronic commerce is important to use IB

services.cb2: The government endorses Internet commerce in Yemen.pf2: For me, the government endorsing electronic commerce is

important to use IB services.cb3: The government is active in setting up the facilities to enable

Internet commerce.pf3: For me, setting up the facilities to enable Internet commerce is

important to use IB services.cb4: The government promotes the use of the Internet for commercepf4: For me, the government promoting the use of the Internet for

commerce is important.

Notes: cb, actor’s control belief; pf, perceived facilitated (motive to comply with actor’s belief). *WSIS (2003) IT Master plan for Yemen, WorldSummit on the Information Society (WSIS), Beirut, 4–6 February 2003.

The coding of items and constructs of the control belief of IB.

Constructs coding Items coding Items’ statements

SE SE1 I am confident that I could learn IB applications.SE2 I feel comfortable using computers in general.SE3 I feel comfortable using the Internet.SE4 My current skills in using the Internet enable me to do everything that I want.DSE1 I feel comfortable using IB on my own and for me this aspect is important.

(contblf1 6 pf1)DSE2 Can easily operate IB from bank’s website on my own and for me this aspect is

important. (contblf2 6 pf2)DSE3 Can use IB without others’ help and for me this aspect is important. (contblf3 6 pf3)

GS FGS1 GS e-commerce. (govs1 6 govs2)FGS2 Government endorses e-commerce. (govs3 6 govs4)FGS3 Setting up the facilities to enable e-commerce. (govs5 6 govs6)FGS4 Government promotes e-commerce. (govs7 6 govs8)

FT condition FT6 Facilitate bank’s IB transactional websites to use IB. (tav11 6 tav12)FT7 Facilitate a good quality of Internet connection to use IB. (tav13 6 tav14)FT8 Facilitate high quality of Internet wireless to use IB. (tav15 6 tav16)

FR FT1 Have the computers, Internet access and applications needed to use IB. (tav1 6 tav2)FR1 Facilitate computers for everyone to use IB services. (rav1 6 rav2)FR2 Facilitate the accessing Internet for low prices attainable to use IB. (rav3 6 rav4)FR3 Facilitate IB availability then I would be able to use IB when I need it. (rav5 6 rav6)FR4 Have the time to set up IB services. (rav7 6 rav8)FR5 Have enough money to use IB services. (rav9 6 rav10)

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