Empirical examination of the adoption of WebCT using TAME.W.T.Ngaia,*,J.K.L.Poonb,Y.H.C.ChanaaDepartmentofManagementandMarketing,TheHongKongPolytechnicUniversity,HungHom,Kowloon,HongKong,PRChinabTheInstituteofVocationalEducation,HongKong,PRChinaReceived29March2004;receivedinrevisedform3November2004;accepted17November2004AbstractWeb Course Tools (WebCT) have enhanced the ability and motivation of institutes of higher educationtosupporte-learning. Inthisstudy, weextendedtheTechnologyAcceptanceModel toincludetechnicalsupport as a precursor and then investigated the role of the extended model in user acceptance of WebCT.Responses from 836 university students wereused to test the proposed structuralmodel. Thedata showedthat technical support has a signicant direct eect on perceived ease of use and usefulness, while perceivedeaseofuseandusefulnessarethedominantfactorsaectingtheattitudeofstudentsusingWebCT. Theresultsindicatethe importanceof perceived easeof use and perceived usefulnessin mediating therelation-shipoftechnicalsupportwithattitudeandWebCTusage.2005ElsevierLtd.Allrightsreserved.Keywords:WebCT;Technologyacceptancemodel;Web-basedlearningsystems1.IntroductionOnline teachingandlearningis becominganincreasinglyimportant part of higher educa-tion. Thecurrent deliverymodesandinstructional designsof highereducationmeet thechal-lengesfromtheadvancement of technology. Themajorchangeswill bemostlyfromthearea0360-1315/$-seefrontmatter 2005ElsevierLtd.Allrightsreserved.doi:10.1016/j.compedu.2004.11.007*Correspondingauthor.Tel.:+85227656611;fax:+85227667296.E-mailaddresses:mswtngai@polyu.edu.hk;mswtngai@inet.polyu.edu.hk(E.W.T.Ngai).www.elsevier.com/locate/compeduComputers&Education48(2007)250267ofe-learning. E-Learning, alsoknownasWeb-basedlearning, isdenedasanInternet-enabledlearningprocess(Gunasekaran, Mcneil, &Shaul, 2002). Accordingtothereportconductedbythe International Data Corporation (2000), The number of colleges and universities in US oer-inge-learningwill morethandouble, from1500in1999tomorethan3300in2004. Studentenrollment in these courses will increase 33% annually during this time. They believe the marketfore-learninginhighereducationwillbeworthalmostUS$750millionby2004.Inordertosupporte-learning, variousWeb-basedlearningsystemshavebeendevelopedforhigher education. For example, Web Course Tools (WebCT), the Web Course Homepage System(WebCH), BlackboardLearningSystemandthe Systemfor MultimediaIntegratedLearning(Smile), are the latest waves of technology-based pedagogical tools. They provide e-learning plat-formsthatusetheInternetasadeliverymechanismtoallowstudentsfromallovertheworldtoaccess a number of learning tools such as discussion boards, chat rooms, course content manage-ment, etc. Many institutions of higher education adopt such Web-based learning systems for theire-learningcourses.Forinstance,WebCThasbeenusedby2100institutionsofhighereducationall over the world, including famous universities such as Stanford, UCLA, and so forth (WebCT,2001).However,thereisalackofempiricalexaminationoftheadoptionofweb-basedlearningsystems. In Hong Kong, many institutions of higher education oer e-learning as part of their cur-riculum, and have some form of on-line learning using these Web-based learning systems. WebCTisbeingusednearlyinallinstitutionsofhighereducationinHongKong.In this study, we propose a model, based on the extension of the Technology Acceptance Model(TAM) (Davis, Bagozzi, & Warshaw, 1989), to investigate the factors that aecting the acceptanceofWebCTforsupportinge-learning.Theaimsofthispaperare:(a) to determine the current usage of WebCT teaching and learning in Hong Kong institutions ofhighereducation,(b) toidentifythefactors aectingtheacceptanceof WebCTteachingandlearninginHongKonginstitutionsofhighereducation,and(c) todevelopamodelfortheacceptanceofWebCTinHongKongforhighereducationbasedontheTAM.Thispaperisorganizedasfollows. First, wepresentareviewoftheliteratureonweb-basedlearningsystemsandtheTAM. Then, theresearchmodel andhypothesesareproposed. Next,the research method used in this study is described. The results of the collected data and the pro-posedmodel,whichwereanalyzedusingstructuralequationmodeling(SEM),arereported.Thenalsectiondiscussesthendingsofthestudyandconcludesthepaper.2.Literaturereview2.1.Web-basedlearningsystemsWith the widespread use of the World Wide Web (WWW), manyinstitutions ofhigher educa-tionhaveidentiedopportunitiesfordevelopingcoursesforWeb-basedlearning. Unliketradi-tional classroom-based learning, Web-based learning oers various benets. Some studiesE.W.T.Ngaietal./Computers&Education48(2007)250267 251comparedthelearningoutcomesof onlineandtraditional classroom-basedcoursesandfoundthat students taking the online course outperformed those taking the traditional classroom-basedcourse(Kekkonen-moneta&Moneta, 2002; Hofmann, 2002). Their studyshowedthat Web-basedlearningisappropriateforteachingandlearning.Manyinstitutionsofhighereducationhavealreadymadethetransitiontoonlineteachingbycontractingtechnologyandcoursewareproviderstodesigntheinfrastructureneededtocustom-ize their own online programs. Real improvement began to emerge in the 1990s, when many spe-cially designed Web-based learning systems such as WebCT, WebCH, Smile, etc., wereintroduced.AccordingtothedenitionoftheIEEELearningTechnologyStandardCommittee,aWeb-basedlearningsystemis: AlearningtechnologysystemthatusesWeb-browsersastheprimary means of interaction with learners, and the Internet or an intranet as the primary meansofcommunication among itssubsystems andwith othersystems. These systemswork asa plat-formtofacilitateteachingandlearning. Theyassistinmanagingthemundanetasksaswell asstimulatingnewinsightsintoconductingclassesanddeliveringcourse content.Also,theycanbeusedtopublishanentireonlinecourseormerelytomakesupplemental materialsavailableon-line.NearlyalltheuniversitiesinHongKongareusingWebCTtosupportWeb-basedteachingandlearning. These include the Universityof HongKong, The Chinese Universityof HongKong,TheHongKongPolytechnicUniversity,HongKongBaptistUniversity,CityUniversityofHongKong,Lingnan University,TheHongKongUniversityofScienceandTechnologyandallinstitutionsunderVocationalTrainingCouncil.WebCTisknownasoneofthebestcourse-managementsystemsavailable,andprovidesanumberoflearningtools.Therearealimitedofstudies on Web-based learning using WebCT. Lu, Yu, and Liu (2003) undertook a study to iden-tify the impact of learning styles, learning patterns and other selected factors on the learning per-formanceof students inaWebCTMISgraduatecourse. Morss (1999), Wernet, Olliges, andDelicath(2000) andWithnam, Krockover, Ridgway, andZinsmeister(2002) surveyedtheper-spectives of universitystudents onWeb-basedlearningusingWebCT. Thefeedbackfromthestudentswasgenerallypositive.Overall,studentswerelikelytoacceptusingWeb-basedsystemsforteachingandlearning.2.2.TechnologyacceptancemodelWhile many institutions of higher education have been using the Web for teaching and learning,littleresearchhasbeendonetoidentifythefactorsaectingstudents acceptanceoftheWebCTlearningsystem,norhasanyresearchbeenconductedonattitudestowardsWebCT.TAM, introduced by Davis et al.(1989), was based on the Theory of Reasoned Action (TRA)(Fishbein&Ajzen,1975)andspecicallydesignedforexplainingandpredictinguseracceptanceofspecictypesoftechnology.TAMwasbuiltoncollectivendingssuggestingthatthedesiredtechnologywasgreatlydependentonuseracceptanceoftechnology.Itsuggestedthatperceivedusefulness and perceived ease of use were important factors in determining the use of informationsystems.AnumberofstudieshavesuccessfullyadoptedTAMtoexaminetheacceptanceofnewtechnologiessuchaspersonalcomputers(Igbaria,Zinatelli,Cragg,&Cavaye,1997),wordpro-cessorsandspreadsheets(Chau, 1996). Recently, astheWWWhasemergedasanewformofinformationtechnology, researchershaveusedTAMtoinvestigatevariousWWWcontextsin252 E.W.T.Ngaietal./Computers&Education48(2007)250267predictingacceptanceoftechnology. TheseincludeWebbrowsers(Morris&Dillon, 1997), theuseof Websites (Lederer, Maupin, Sena, &Zhuang, 2002; Lin&Lu, 2002; vander Heijden,2003), Web retailing (Chen, Gillenson, & Sherrell, 2002; Ocass & Fenech, 2003), online purchaseintentions (van der Heijden, Verhagen, & Creemers, 2003), etc. However, there are not many stud-ies explainingtheacceptanceof Web-basedlearningsystems usingtheTAM. Lou, Luo, andStrong (2000) examined the critical mass, as an external variable, of the acceptance of groupware,basedontheTAM. Groupwaretechnologiessupport groupcommunicationandcollaborationsuchase-mailandelectronicbulletinboards,whicharesimilartoWeb-basedlearningsystems.Theyfoundthatall oftheconstructs(critical mass, perceivedusefulnessandperceivedeaseofuse),aecttheintentiontousegroupware.Selim(2003)investigatedstudentuseandacceptanceof course websites based on the usefulness of the course website, ease of use and usage. The resultsshowed that there is a signicant relationship between the usefulness of a course website and easeofuseindeterminingtheusageofacoursewebsite. Infact, otherexternal variablesshouldbeincludedinTAMformeasuringthespecictechnology, sincetheymayinuencetheperceivedeaseofuseandperceivedusefulnessofthattechnology(Davisetal.,1989).3.ResearchmodelandhypothesesThe TAM has been widely used to predict the acceptance of a new technology. In this study, aresearch model is based on the TAM to study the adoption of WebCT for higher education. Thecomponentoftechnical support hasbeenincorporatedintheTAMmodel, andservesasanextension to TAMfor measuring the acceptance of Web-based learning systems. The research mod-el explains the systemusage of Web-based learning systems for higher education. It consists of tech-nical support, perceived usefulness, perceived ease of use, attitude and intention to use (see Fig. 1).TechnicalSupportPerceived Easeof UsePerceivedUsefulnessAttitude Intention to Use System UsageTS1TS2TS3TS4TS5TS6PEOU1 PEOU2 PEOU3 PEOU4 PEOU5PU1 PU2 PU3 PU4 PU5 PU6A1 A2A3 A4IU1 IU2 SU1 SU2H2H1H5H4H7H10H8H12H3H6H9H11Fig.1. ProposedstructuremodelfortheacceptanceofWeb-basedlearningsystems.E.W.T.Ngaietal./Computers&Education48(2007)250267 253Several previous studies have shown that there are various external factors that indirectly inu-encetheacceptanceoftechnologythrough perceivedusefulnessandperceivedeaseofuse(Daviset al., 1989; Szajna, 1996). In this study, we expect technical support to be one such external factoraectingtheacceptanceofWebCTforhighereducation.Ralph(1991)denedtechnicalsupportasknowledgepeopleassistingtheusersofcomputerhardwareandsoftwareproducts, whichcanincludehelpdesks, hotlines, onlinesupportservices, machine-readablesupportknowledge-bases, faxes, automatedtelephonevoiceresponsesystems, remotecontrol softwareandotherfacilities. Technical supportisoneoftheimportantfactorsintheacceptanceoftechnologyforteaching(Hofmann, 2002; Sumner&Hostetler, 1999; Williams, 2002) andinusersatisfaction(Mirani &King, 1994). Highlevelsof organizational support, includingmanagement supportand information center support, were thought to promote more favorable attitudes about the sys-tem among users and information specialists,and lead togreater success for personal computingsystems(Igbaria, 1990). Igbariaet al. (1997) arguedthat internal/external personal computingsupport/trainingwasaectingtheacceptanceofpersonalcomputinginsmallrms.Asaresult,thefollowingthreehypothesesareproposed:H1. TechnicalsupporthasapositiveeectontheperceivedusefulnessofWebCT.H2. TechnicalsupporthasapositiveeectontheperceivedeaseofuseofWebCT.H3. TechnicalsupporthasapositiveeectonattitudestowardsusingWebCT.In the generic TAM, the belief-attitude-intention-behavior relationship has been demonstratedin various studies. However, the TAM-related hypotheses in the context of WebCT have not beenveried. In this study, the perceived ease of use of the WebCT is dened as the degree to which theuser believes thatusing the WebCT will befree ofeort (Davis,1989). Davis(1989) showed thatease of use had a direct eect on perceived usefulness. Further studies on TAM also demonstratedstrongempiricalsupportforapositiverelationshipbetweenperceivedeaseofuseandperceivedusefulness (Adams, Nelson, & Todd, 1992; Szajna, 1996). The perceived usefulness of the WebCTisdenedasthedegreetowhichtheuserbelievesthatusingtheWebCTwouldenhancehis/herlearning performance (Davis, 1989). The TAM posits that perceived usefulness and perceived easeofusehasadirecteectonattitudestowardsusinganewtechnology.Attitudeisthedegreetowhichtheuserisinterestedinspecicsystems, whichhasadirecteectontheintentiontousethose specic systems in the future(Davis et al., 1989) and the actual usage ofthe systems (Bajaj& Nididumolu, 1998). The extent to which systems are used over a xed unit of time is inuencedby the intention to use (Davis et al., 1989). In addition, the usage of the system is also aected byperceived ease of use and perceived usefulness (Davis et al., 1989; Igbaria et al., 1997;Selim, 2003). As a result, the following hypotheses based on the TAM-relationship areproposed:H4. PerceivedeaseofusehasapositiveeectonattitudestowardtheuseofWebCT.H5. PerceivedeaseofusehasapositiveeectontheperceivedusefulnessofWebCT.H6. PerceivedeaseofusehasapositiveeectontheuseofWebCT.H7. PerceivedusefulnesshasapositiveeectonattitudestowardstheuseofWebCT.254 E.W.T.Ngaietal./Computers&Education48(2007)250267H8. PerceivedusefulnesshasapositiveeectontheintentiontouseWebCT.H9. PerceivedusefulnesshasapositiveeectontheuseofWebCT.H10. AttitudestowardsusingWebCThaveapositiveeectontheintentiontousethesystem.H11. AttitudestowardsusingWebCThaveapositiveeectontheuseofWebCT.H12. IntentiontousehasapositiveeectontheuseofWebCT.4.Researchmethod4.1.DevelopmentofinstrumentsThesurveyinstruments consistedof25items(listedinAppendixA)toassesssix constructsoftheproposedmodel.Theseitemswereadaptedfrompreviousstudiedandwererenedtomakethemspecicallyrelevant tothepresent study. Technical support was operationalizedusingascaleadaptedfromIgbaria(1990), andconsistedofsixitemsthatmeasuredtheavailabilityoftechnicalassistance,specializedinstruction andinternaltraininginoperatingWebCT.Theitemsused to construct the perceived usefulness and perceived ease of use were based on the scale fromDavis(1993) withsomemodications. AttitudeandintentiontouseweremeasuredusingthescalerecommendedbyAjzenandFishbein(1980).Theseveconstructsweremeasuredonase-ven-point Likert scale ranging from (1) strongly disagree to (7) strongly agree. To investigatethe acceptance of WebCT, the respondents were asked to rate the frequency of usage on a seven-pointscalewith7beingmorethanonceadayand1beingnotatall.4.2.SampleanddatacollectionTheresearchmethodologywas basedon empiricaldata collectedthrough a questionnairesur-vey of seven universities in Hong Kong. The population in this study was the number of students(bothundergraduatesandpostgraduates)inall sevenofHongKongsuniversities. Thesamplesize was set to be 1400 students, i.e., about 1.77% of the population, in order to be representativeof the population. Since the number of students studying in each university was dierent, dispro-portional stratiedsamplingwasadopted. AsKumar(1999)observed, Whenmultiplegroupsare compared and their respective group sizes are small, a proportional stratied sampling wouldnot yield a sample size large enough for meaningful comparisons, and a disproportional stratiedsamplingisused. Onewayofselectingsamplesizeswithineachgroupwouldbetohaveequalgroupsizesinthesample.Table1adisplaystheidenticationsofstrata,totalnumbersofstu-dentsinstrata,samplesizesandsamplingfractionsinstrata.Thestudywasconductedintwophases:apilotstudyandaquestionnaire.Thequestionnairewaspilot-testedwith30randomlyselectedstudentsfromTheHongKongBaptist University.Based on the feedback from the pilot test, the questionnaire was rened and a revised nal ques-tionnaire was developed. Questionnaires were then distributed to students at common rooms and/or canteens in each university. The response rate in each university was from 86.5% to 97.5%. OfE.W.T.Ngaietal./Computers&Education48(2007)250267 255the1400questionnairesdistributed, 1263werecollectedandusedforanalysis. Theoverall re-sponse ratewasabout90%.Weinvestedmucheortinobtainingahighresponse rate.Thehighresponse rate in our study was due to the personal approach, but other factors of our study designalso contributed. We marked the survey itself with the Hong Kong Polytechnic University(PolyU)logoandexplainedtotherespondentthepurposeofthesurveyandmotivatedhim/herto reply personally. We mentioned that PolyU was sponsoring the survey. Also the condentialityoftheresultshadbeenstressed.Otherfactorsthatmighthaveincreasedthedirectresponserateweretheinvolvement of student interviewers, face-to-faceinterviews at theplaceof commonroomsandorcanteensineachuniversity.5.Analysisandresults5.1.DemographicsanddescriptivestatisticsThereturnedsamplecharacteristicsareillustratedinTable1b. Inthisstudy, atotal of1400questionnairesweredistributed, of which1263werecollected. Table1bshowsthat themajor-ityoftherespondentswereundergraduatesintheirrsttothirdyearsof study, andthattheycamefromavarietyof programs. About 88.6%werefull-timestudentsinthecampus. Of therespondents, 66.2%reportedthat they hadthe experience inusing Web-basedlearning sys-tems, while 714 students out of 836 were using WebCT(See Table 2). WebCTseems tobe the dominant Web-based learning systemin institutions of higher education in HongKong.5.2.DataanalysisIn this study, the sample was split randomly into two sub-samples, S1 and S2, with 418 in eachsub-sample, fromthetotal of836respondentswhoreportedthattheyhadexperienceinusingTable1aStraticationsbyUniversityUniversityName Totalno.ofstudentsSamplesizesinStrataSamplingfractionsinStrata(%)CityUniversityofHongKong(CityU) 16,142 200 1.24TheChineseUniversityofHongKong(CU) 14,161 200 1.41HongKongBaptistUniversity(HKBU) 7400 200 2.70TheHongKongPolytechnicUniversity(PolyU) 17,859 200 1.12UniversityofHongKong(HKU) 14,216 200 1.41TheHongKongUniversityofScienceandTechnology(HKUST) 7332 200 2.73LingnanUniversity(LU) 2000 200 10Total 79,110 1400 1.77256 E.W.T.Ngaietal./Computers&Education48(2007)250267Web-basedlearningsystems. Theinitial analysiswasperformedonsampleS1, toexaminethereliabilityandconstructvalidityusingSPSS. Then, AMOS4wasusedwithadierentsampleS2totesttheproposedstructuralmodel.Table1bProleoftherespondentsFrequency PercentageGenderMale 583 46.2Female 680 53.8Total 1263 100.0AcademiclevelUndergraduate 1086 86.0Master 102 8.1Doctor 10 0.8Other(e.g.,associatedegree) 65 5.1Total 1263 100.0YearofstudyYear1 553 43.8Year2 334 26.4Year3 353 27.9Year4 13 1.0>Year4 10 0.8Total 1263 100.0ModeofstudyFull-time 1119 88.6Part-time 144 11.4Total 1263 100.0CoursescurrentlystudyingBusinessAdministration 262 20.7Management 102 8.1ComputerScience 62 4.9Chemistry 80 6.3Physics 48 3.8Biology 51 4.0SocialWork 23 1.8Medicine 38 3.0MechanicalEngineering 21 1.7CivilEngineering 17 1.3OthersEngineering 96 7.6Language 122 9.7Arts 29 2.3Others(e.g.,Nursing,Journalism) 312 24.7Total 1263 100.0E.W.T.Ngaietal./Computers&Education48(2007)250267 2575.3.AnalysisofvalidityandreliabilityInthisstudy,constructvalidityandreliabilitywereexamined.Constructvalidityreferstothedegreetowhichaconstructdiersfromotherconstructs. It canbeassessedthroughprincipalcomponentanalysis(PCA).Usingvarimaxrotation,PCAwasusedinanattempttoreconstructsix composite factors. When interpreting the rotated factor pattern, an item was said to load on afactor if the factor loading is 0.4 or greater (Nunnally, 1978). Using this criterion, the rotated pat-ternmatrixwasexaminedforitemsthatdidnotloadonafactorwiththeotheritemsfromthesame scale. Items that cross-loaded on multiple factors were also examined and would be deleted.Besides, eigenvalueswereexaminedtodecidethenumberof factorstoextract. Aneigenvaluegreaterthan1wasusedasacriteriontodeterminethenumberoffactors.TheinitialPCAshowedthatvefactorswereextracted.Technicalsupport,perceiveduse-fulness, perceived ease of use and system usage was loaded into four distinct factors. How-ever, attitude and intention to use were collapsed into one factor. We decided to remove theconstruct of intention to use because the survey showed that the requirement of lecturers to usethesystemhasalargeimpactontheuseofWeb-basedlearningsystems.EveniftheattitudeofstudentstowardsWeb-basedlearningsystemsispositive, theymayhavenointentionof usingthemiftheirlecturersdonotrequireit. Therefore, asecondPCAwasconductedbyremovingthe item of intention to use. The results of the PCA for each of the constructs are given in Table3. These results show that the loadings of individual items on the construct exceed 0.4 (Nunnally,1978),whichveriesthattheitemsmeasuredexhibitsucientvalidity.Allfactorloadingswerelargerthan0.5andnocross-loadingonmultiplefactorsexceed0.5. ThecorrelationmatrixforthedatasetisgiveninTable4. Correlationsgreaterthan0.3forasamplesizeof418usedinthe analysiswerestatisticallysignicantatthe0.01level.Aninspection ofthecorrelation matrixrevealedthatthemajorityoftheinter-itemcorrelationsweresignicant(greaterthan0.3)atthe0.01 level. The items associated with a measure correlated more highly with each other than withitems associated with other measures in the model. The intercorrelations among the items associ-atedwiththemeasuresarestrongerthantheircorrelationswithitemsrepresentingothermea-sures. As a result, the convergent and discriminant validity of the measurement can bedemonstrated.Reliabilityreferstotheextenttowhichtheconstructsarefreefromerrorandyieldconsistentresults. Cronbachs a was used to measure the internal consistency of the multi-item scales used inthisstudy.TheCronbachs a(seeTable3)foreachofthevefactorswasmuchhigherthanthealphathresholdlevel of0.7(Nunnally, 1978). Table3showsthattheCronbachsacoecientsTable2ExperienceinusingWeb-basedlearningsystemsFrequency PercentageExperienceinusingWebCTYes 836 66.2No 427 33.8Web-basedsystemcurrentlyusingWebCT 714 85.4258 E.W.T.Ngaietal./Computers&Education48(2007)250267Table3ResultsofprincipalcomponentanalysisComponentFactor1perceivedusefulnessFactor2perceivedeaseofuseFactor3attitude Factor4technicalsupportFactor5systemusagePU1 0.81PU2 0.81PU3 0.84PU4 0.83PU5 0.74PU6 0.83PEOU1 0.83PEOU2 0.80PEOU3 0.80PEOU4 0.75PEOU5 0.85A1 0.61A2 0.54A3 0.64A4 0.57TS1 0.73TS2 0.82TS3 0.80TS4 0.68TS5 0.54TS6 0.57SU1 0.85SU2 0.88Cronbachs a 0.93 0.90 0.91 0.83 0.71Eigenvalue 8.99 2.38 2.26 1.50 1.61Cum.varianceexplained(%) 23.65 41.83 55.30 63.53 70.80E.W.T.Ngaietal./Computers&Education48(2007)250267259Table4CorrelationsmatrixbetweenthemeasurementitemsMean S.D. TS1 TS2 TS3 TS4 TS5 TS6 PU1 PU2 PU3 PU4 PU5 PU6 PEOU1 PEOU2 PEOU3 PEOU4 PEOU5 A1 A2 A3 A4 SU1 SU2TS1 3.85 1.31 1.000TS2 3.41 1.40 0.551*1.000TS3 3.03 1.32 0.456*0.678*1.000TS4 4.16 1.39 0.528*0.414*0.408*1.000TS5 4.63 1.45 0.435*0.254 0.263 0.650*1.000TS6 3.65 1.35 0.385*0.388*0.394*0.384*0.368*1.000PU1 4.31 1.27 0.284 0.223 0.176 0.300*0.276 0.335*1.000PU2 3.86 1.21 0.294 0.265 0.246 0.269 0.234 0.326*0.728*1.000PU3 4.17 1.29 0.262 0.228 0.201 0.263 0.235 0.270 0.720*0.722*1.000PU4 4.25 1.34 0.290 0.189 0.185 0.273 0.285 0.268 0.690*0.685*0.735*1.000PU5 4.15 1.33 0.244 0.146 0.137 0.266 0.230 0.264 0.590*0.617*0.628*0.664*1.000PU6 4.31 1.30 0.341*0.212 0.126 0.293 0.267 0.292 0.734*0.674*0.741*0.745*0.707*1.000PEOU1 4.68 1.19 0.229 0.142 0.171 0.230 0.207 0.167 0.302*0.198 0.268 0.298 0.243 0.318*1.000PEOU2 4.44 1.16 0.208 0.139 0.137 0.314*0.267 0.301*0.371*0.315*0.358*0.388*0.318*0.394*0.649*1.000PEOU3 4.55 1.10 0.271 0.158 0.149 0.280 0.257 0.235 0.341*0.283 0.338*0.370*0.328*0.397*0.592*0.615*1.000PEOU4 4.39 1.16 0.284 0.199 0.149 0.303*0.261 0.311*0.399*0.358*0.383*0.427*0.395*0.414*0.561*0.617*0.60s7*1.000PEOU5 4.61 1.18 0.267 0.125 0.171 0.316*0.307*0.287 0.377*0.308*0.345*0.384*0.357*0.412*0.651*0.632*0.689*0.679*1.000A1 3.97 1.22 0.282 0.246 0.110 0.237 0.215 0.305*0.507*0.468*0.452*0.473*0.427*0.540*0.256 0.375*0.329*0.425*0.336*1.000A2 4.50 1.25 0.304*0.180 0.229 0.275 0.247 0.207 0.543*0.454*0.52s8*0.545*0.430*0.580*0.368*0.362*0.398*0.419*0.443*0.585*1.000A3 4.18 1.27 0.265 0.273 0.186 0.205 0.209 0.251 0.510*0.480*0.505*0.519*0.432*0.542*0.249 0.389*0.322*0.431*0.380*0.722*0.649*1.000A4 4.23 1.23 0.280 0.210 0.151 0.225 0.218 0.258 0.561*0.512*0.563*0.586*0.498*0.631*0.374*0.431*0.428*0.465*0.472*0.666*0.754*0.728*1.000SU1 3.81 1.49 .0.158 0.076 0.079 0.119 0.138 0.182 0.178 0.188 0.204 0.204 0.200 0.226 0.095 0.230 0.155 0.180 0.146 0.113 0.167 0.156 0.223 1.000SU2 3.82 1.28 0.108 0.105 0.077 0.119 0.136 0.177 0.102 0.181 0.152 0.146 0.192 0.145 0.011 0.110 0.045 0.110 0.044 0.119 0.104 0.132 0.154 0.563*1.000*Indicatescorrelationssignicantatthe0.01level.260E.W.T.Ngaietal./Computers&Education48(2007)250267range from 0.71 to 0.93, which indicates that the instrument can be considered reliable and inter-nallyconsistent.5.4.AnalysisofthestructuralmodelAfter removing the construct intention to use, the proposed structural model was revised andexamined using SEM (see Fig. 2). SEM is a comprehensive statistical approach to testing hypoth-esesaboutrelationsamongobservedandlatentvariables(Hoyle, 1995). AmajoradvantageofSEMistheabilitytoestimateacompletemodelincorporatingbothmeasurementandstructuralconsiderations. To analyze and conrm the tness of the revised structural model, a dierent sam-pleS2wasused.Amodel issaidtottheobserveddatatotheextentthatthecovariancematrixitimpliesisequivalent to the observed covariance matrix (Hoyle, 1995). However, there are no recommendedmeasuresofmodelt.Sixcommonindicesoftthatwererecommendedintheliterature(Hair,Anderson, Tatham, & Black, 1995; Hu, Chau, Sheng, & Tam, 1999) were employed in this study.Thecommonlyusedmeasuresofmodel t, basedonresultsfromananalysisofthestructuralmodel, aresummarizedinTable5. Inpractice, aChi-square/degreeof freedomof lessthan3,GFI, NFI, CFI greater than 0.9, an AGFI greater than 0.8 and a RMSR of less than 0.1 are con-sidered indicators of good t. As seen in Table 5, all goodness-of-t statistics are in the acceptableranges, with the exceptions of GFI, which is close enough (0.87) to the recommended value of 0.9.TechnicalSupportPerceived Easeof UseR2=0.25PerceivedUsefulnessR2=0.27AttitudeR2=0.60System UsageR2=0.12TS1TS2TS3TS4TS5TS6PEOU1 PEOU2 PEOU3 PEOU4 PEOU5PU1 PU2 PU3 PU4 PU5 PU6A1 A2A3 A4SU1 SU20.730.820.800.680.540.570.550.370.230.010.750.370.640.570.61 0.540.85 0.880.060.110.180.81 0.83 0.74 0.83 0.84 0.810.85 0.75 0.80 0.80 0.83Fig.2. Resultsofrevisedstructuremodel.E.W.T.Ngaietal./Computers&Education48(2007)250267 261The results of testing the structural model are presented in Table 6 and a graphical presentationof the results is shown in Fig. 2. Table 6 shows the values of the coecient of determination R2foreachendogenousvariable. Table6depictstheR2foreachendogenousvariable. Forperceivedeaseof use, R2= 0.25. For perceivedusefulness, R2= 0.27; whilefor attitude, it is 25%, 27%and60%,respectively.Ontheotherhand,only12%ofthevariationintheWeb-basedlearningsystemcanbeexplainedbytheproposedmodel. Theproposedstructuremodel explained12%ofthevarianceintheuseofWeb-basedlearningsystems.Fig. 2andTable6furtherillustratethesignicant structural relationshipsamongthestudyvariables. Hypotheses1,2 and3postulate thattechnical supporthas apositiveinuence on per-ceived usefulness (H1), perceived ease of use (H2), and attitude (H3). The results show that tech-nical support has a strong direct eect on perceived usefulness and perceived ease of use (b = 0.37and0.55, respectively; p < 0.05). Althoughthedirect eect of technical support onattitudeisinsignicant (b = 0.01, p > 0.05), it should be noted that the indirect eect on attitude through per-ceived usefulness and perceived ease of use is very strong (b = 0.57, p < 0.05). As a result, H1, H2aresupportedwhileH3isrejected.Hypotheses 4, 5 and 6 investigate the relationship of perceived ease of use to attitude (H4), per-ceived usefulness (H5) and WebCT usage (H6). Perceived ease of use has a positive direct eect onattitude (b = 0.37, p < 0.05), perceived usefulness (b = 0.23, p < 0.05), and WebCT usage(b = 0.11,p < 0.05).Inaddition,ithasasignicantindirecteectonattitudethroughperceivedusefulness(b = 0.18,p < 0.05).ThereforeH4,H5andH6aresupported.Table6EectsofvariablesontheacceptanceofWeb-basedlearningsystemsPerceivedeaseofusePerceivedusefulnessAttitude SystemusageDirecteectsIndirecteectsDirecteectsIndirecteectsDirecteectsIndirecteectsDirecteectsIndirecteectsTechnicalsupport 0.55*0.37*0.13 0.01 0.57*0.19Perceivedeaseofuse 0.23*0.37*0.18*0.11*0.08Perceivedusefulness 0.75*0.18*0.05Attitude 0.06R20.25 0.27 0.60 0.12*p 6 0.05.Table5SummarystatisticsofmodeltModelgoodness-tindexes Recommendedvalue ResultsinthisstudyChi-square/degreeoffreedom 63.0 3.0Goodness-of-tindex(GFI) P0.90 0.87Adjustedgoodness-of-tindex(AGFI) P0.80 0.84Normalizedtindex(NFI) P0.90 0.90Comparativetindex(CFI) P0.90 0.93Rootmeansquareresidual(RMSR) 60.10 0.07262 E.W.T.Ngaietal./Computers&Education48(2007)250267Hypotheses7and9focusontheimpactofperceivedusefulnessonattitude(H7)andWebCTusage (H9). Similar to the other studies on TAM (Davis, 1989; Davis, 1993), perceived usefulnesshas apositive direct eect on attitude(b = 0.75,p < 0.05) and WebCT usage(b = 0.18,p < 0.05).Therefore,H7andH9aresupported.Hypotheses 11 postulated a relationship between attitude and the actual use of WebCT (H11).However, attitude has a weak and insignicant direct eect (b = 0.06, p > 0.05) on WebCT usage.Thus,H11isrejected.Theanalysisofthestructural model showsthatperceivedeaseofuseandusefulnessarethedominant factors aecting the attitude of students. The ndings also show that technical supporthas a signicant eect on perceived ease of use and usefulness. The results demonstrate the impor-tanceof perceivedeaseof useandperceivedusefulnessinmediatingtherelationshipsbetweentechnical support andattitudeandsystemusage. Hypotheses8, 10and12arerejectedinthisstudy,sincetheconstructofintentiontouseisbeingremoved.6.DiscussionandconclusionsThisstudyhasinvestigatedtheunderlyingrelationshipbetweentechnical support, perceivedusefulness, perceived ease of use, attitude and the acceptance of the WebCT for higher education.Theempirical examinationof theadoptionof WebCTusingastructural model basedontheextensionofTAMhasbeentestedandvalidated. Mostofthecausal relationshipsbetweentheconstructs postulated by the structural model are well supported. The study provides further evi-denceoftheappropriatenessofapplyingTAMtomeasuretheacceptanceofWebCTinhighereducation.From the results, technical support was found to have a direct eect on the perceived ease of useandperceivedusefulness. It alsohas astrongindirect eect onattitude. This underscores theimportanceofusersupportandtrainingininuencingtheperceptionsofusersand,eventually,their use of the system. As a result, it is essential for universities to provide eective user supportandtoencourageuserstousethesystem.One interesting observationis that, intesting the structural model, attitude only demon-stratedaweakdirecteectonsystemusage. Ontheotherhand, perceivedusefulnessandper-ceivedeaseofusebothdemonstratedasignicantdirecteectonsystemusage, andtheeectstheyshowedwereevenstronger thanthat of attitude. This is dierent fromprevious studiesshowingthat attitudemediatestheeect of perceivedeaseof useandperceivedusefulnessonthe acceptance of a system. One of the possible explanations of the weakeect of attitudeonsystemusageis that, inuniversities, students aretoldtouseWebCTbytheir lecturers asaspecicsubjectrequirement. Asaresult, apositiveattitudeamongstudentstowardsWebCTmaynotgenerateanincreaseintheactualuseofthesystemiflecturersdonotrequirethemtousethesystem.ThisstudyprovidesadditionalinsightsfortheeducatorinimplementingWeb-basedlearningsystems in their online courses. The ndings indicate that perceived ease of use is a key interveningvariablelinkingtheexogenousvariabletechnicalsupportwithperceivedusefulness,attitudeand system usage. The importance of perceived ease of use is further illustrated by its direct eectonattitudeandevenonsystemusage. Thissuggeststhat, inimplementingasystem, thefocusE.W.T.Ngaietal./Computers&Education48(2007)250267 263shouldbeplacedonfosteringtheself-condenceofindividualsandtheirperceptionsconcerningthe system. If users are struggling, they may actually believe that the system is too hard to use andthat the benets of using the system in terms of performance are outweighed by the eort of usingit. Eventually, theymaybecomereluctanttousethetechnology, thusdefeatingthepurposeofintroducingit. Future studycanextendthe results of this studybyinvestigatingthe areaofself-ecacy which addresses ones belief in ones abilities to be able to accomplish a specic task,suchassuccessfullyusingWeb-basedlearningsystem.Asafuturedirectionofresearch,wewillincludeself-ecacyasafactorforfurtherinvestigation.As with all empirical research, this study has a few limitations. First, the majority of the respon-dentsinthisstudywerefull-timeundergraduatestudents.Thegeneralizationofthestudysnd-ingsshouldbedonewithcare.Thelifestyles,educationalbackgroundsandexperiencesofthesestudents may dier from those of part-time students. More research is needed to investigate otherswhoarenotinengagedinfull-timeundergraduatestudies.Second,thereistherelativelylowR2value inthe proposed model, sothat the perceived ease ofuse, perceived usefulness,and attitudeexplained12%ofthevarianceinusingWeb-basedlearningsystems.Thisimpliesthatotherfac-torsinuencethestudentstouseWeb-basedlearningsystems. Futureresearchshouldexploreother variables that may have an eect on user beliefs and usage, such as peer support, peer pres-sure, ane-learningcultureintheinstitution, infrastructurepressurefromlecturers, availabilityandaccessibility.AcknowledgementThis research was supported in part by The Hong Kong Polytechnic University under the pro-ject grant number G-T954. The author is grateful for the constructive comments of the referee onanearlierversionofthispaper.AppendixA.QuestionsusedinthestudyTechnicalsupportTS1 AhelpdeskisavailablewhenthereistechnicalproblemTS2 AhotlineisavailablewhenthereistechnicalproblemTS3 FaxenquiriescanbemadewhenthereistechnicalproblemTS4 Web-basedenquiriescanbemadewhenthereistechnicalproblemTS5 E-mailenquiriescanbemadewhenthereistechnicalproblemTS6 ThetrainingontheoperationoftheWeb-basedlearningsystemissucientPerceivedusefulnessPU1 HavingWeb-basedlearninginthecurriculumenablesmetolearnmoreecientlyPU2 Web-basedlearningimprovesmyacademicperformancePU3 Web-basedlearningenhancestheeectivenessofmylearning264 E.W.T.Ngaietal./Computers&Education48(2007)250267AppendixA(continued)PU4 Web-basedlearningmakesiteasiertolearninuniversityPU5 Web-basedlearninggivesmegreatercontroloverlearningPU6 Overall,IndWeb-basedlearningtobeadvantageoustomylearningPerceivedeaseofusePEOU1 LearningtooperateaWeb-basedlearningsystemiseasyformePEOU2 IbelievethatitiseasytogettheWeb-basedlearningsystemtodowhatIwantittodoPEOU3 TheprocessofusingaWeb-basedlearningsystemisclearandunderstandablePEOU4 ItiseasyformetobecomeskillfulinusingtheWeb-basedlearningsystemPEOU5 Overall,IbelievethattheWeb-basedlearningsystemiseasytouseAttitudeA1 Web-basedlearningisfunA2 UsingWeb-basedlearningisagoodideaA3 AWeb-basedlearningsystemprovidesanattractivelearningenvironmentA4 Overall,IlikeusingWeb-basedlearningIntentiontouseIU1 Totheextentpossible,IwoulduseWeb-basedlearningsystemtododierentthings,fromdownloadinglecturenotesandparticipatinginchatroomstolearningontheWebIU2 IintendtoincreasemyuseoftheWeb-basedlearningsysteminthefutureSystemusageSU1 Onaverage,howoftendoyouusetheWeb-basedlearningsystem?SU2 Onaverage,howmanyhoursperweekdoyouspendusingtheWeb-basedlearningsystem?ReferencesAdams, D. 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