Int.J.Human-ComputerStudies62(2005)784808AnextensionofTrustandTAMmodelwithTPBintheinitialadoptionofon-linetax:AnempiricalstudyIng-LongWu,Jian-LiangChenDepartmentofInformationManagement,NationalChungChenUniversity,160,San-Hsing,Ming-Hsiung,Chia-Yi,TaiwanReceived23August2004;receivedinrevisedform7March2005;accepted22March2005CommunicatedbyP.ZhangAbstractWhile on-line tax is considered as a special type of e-service, the adoption rate of this serviceinTaiwanisstill relativelylow. Theinitial adoptionofon-linetaxistheimportantdrivingforcetofurtherinuencetheuseandcontinueduseofthisservice. Themodel ofTrustandtechnology acceptancemodel (TAM) in Gefen et al. (2003a, MIS Quarterly 27(1), 5190) hasbeenwell studiedinon-line shopping andshowedthat understanding boththe Internettechnology and trust issue is important in determining behavioral intention to use. Besides, thediffusion of on-line tax could also be inuenced by the potential antecedents such asindividuals, organizational members, and social systemwhile the issue for innovativetechnologyiswell discussedinRogers(1995, TheDiffusionofInnovation, fourthed. FreePress,New York). Theory of plannedbehavior (TPB) is the model widely used to discusstheeffect oftheseantecedentsinbehavioral intention. AnextensionofTrustandTAMmodelwith TPB would be in more comprehensive manner to understand behavioral intention to useon-line tax. Furthermore, a large sample survey is used to empirically examine this framework.r 2005ElsevierLtd.Allrightsreserved.Keywords:On-linetax;TrustandTAMmodel;Trust;TPBARTICLEINPRESSwww.elsevier.com/locate/ijhcs1071-5819/$ - seefrontmatter r 2005ElsevierLtd.Allrightsreserved.doi:10.1016/j.ijhcs.2005.03.003Correspondingauthor.Tel.:+886 5 2720411x34620;fax:+886 5 2721501.E-mailaddress:ilwu@mis.ccu.edu.tw(I.-L.Wu).1. IntroductionCustomer service is a series of activities designed for resolving purchasingproblems that customers encounter throughout theproduct lifecycle toenhancecustomer satisfaction. When customer service is supplied over the Internet,sometimes automatically, it is referred to as e-service (Turban et al., 2002). Ingeneral, e-servicecouldincludecustomer serviceas part of on-lineshoppingandpure-play service offered in e-commerce. Initially, on-line consumers did not demandhighlevelsofcustomerservicesandtheInternetservicewasfairlybasicsuchason-linecatalogue,on-linetransaction,andorderfulllment.However,onnoticingtheInternet bubble burst and the prot gained frome-commerce far away frommarketer expectations, business managers begantosearchthenewpotencyof e-commerce. They found that the key to success in the Internet era is mainly attributedto the ability of providing customers with better service to attract and retaincustomers,andeventually,buildingalong-termrelationshipwithcustomers.Incontrast, whilethefunctionsofgovernmentismainlytoprovideinformationand delivery service to citizens and business partners, government with its customerssuch as citizens and business organizations, in essence, can be considered as a specialtype of service industry. This consideration drives us to impose e-commerce featuresonsupportingtheoperationofgovernment.Thisiscallede-governmentandatypeof pure-play service offered in e-government. In particular, on-line tax declaration isan important function of e-government since it is highly related to the life of citizens.Thus, thegovernmentinTaiwanisaggressivelyencouragingcitizenstousethise-servicefortheirtaxdeclaration.Currently,thesurveydataindicatesthattheusagerate is still quite lowregardless the constantly promotional effort. Among theinuential factors of the low usage rate, the key fundamental can be attributed to theinitialadoption(acceptance) oftheinnovative service by ssincethe initialadoptionof an e-service is the important driving force to further inuence continued use of theservice(KwonandZmud,1987).Foradvocatingusersbehaviortowardtheinitialadoptionofon-linetax,systemdevelopers thus require rst understanding their real needs and expectations in ordertooffermorefavorableservices. Infact, anunderstandingof theusers behaviorwouldbefundamentallybenecial tosystemdesignof ane-servicesinceit couldeffectively identify the barriers for designing reference inadvance. However, e-commerce is a less veriable and controllable environment in which on-line service ortransaction is offered without physical face-to-face contact and simultaneousexchange of services and money. The spatial and temporal separation of e-commercebetweencustomers ande-vendors as well as the unpredictabilityof the Internetinfrastructure generate an implicit uncertainty around the initial adoption of on-lineservice (Pavlou, 2003). Accordingly, the initial adoptionof on-line taxbasicallyinvolves the acceptance of both the Internet technology and on-line serviceproviders. As technology acceptance model (TAM) is mainly proposed fortechnology-basedperspectivethroughtwosystemfeaturesof perceivedusefulness(PU)andperceivedeaseofuse(PEOU)(Davisetal.,1989),itisincompleteinthecontextofon-lineservices.ARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 785Amodel,namedTrustandTAM,hasbeenpreviouslypresentedinexploringtheacceptance of on-line shopping setting (Gefen et al., 2003a). This model integrativelyplaceduse of on-line systemintobothsystemfeatures suchas ease of use andusefulness and trust in e-vendors. This result indicated that thesevariables are goodpredictorsforbehaviorintentiontouseon-lineshopping. However, adiffusionofinnovative technology is highly related tocommunicationchannels, individuals,organizationalmembers,andsocialsystemexceptforthetechnologyitself(Rogers,1995). Theory of planned behavior (TPB) is the model widely used in predicting andexplaining human behavior while also considering the roles of individualorganizational members and social system in this process (Ajzen, 1991). Accordingly,the three inuencers inthis theory, i.e. attitude, subjective normandperceivedbehavioral control, canbe interpretedas attitude for technologyrole, subjectivenormfororganizational membersand socialsystemroles,and perceivedbehavioralcontrolforindividualrole.As the focus of this study is on the on-line tax setting, which is considered as a typeof innovative technology, organizational and social systems such as peer or superiorinuenceandself-efcacyincomputerorexternal resourceconstraintshouldplaytheimportantroleindeterminingtheacceptanceofon-linetax(TaylorandTodd,1995). As a result, anextensionof Trust andTAMmodel withTPBincludingsubjectivenormandperceivedbehavioralcontrol shouldbeinamorecomprehen-sive manner toexaminethe acceptance of on-linetax. Inthis extension, trust isplaced as an important antecedent of attitude, subjective norm, and perceivedbehavioral control. Hopefully, thiswill provideusmoreinformationtosolvethisproblemoflowusagerateinusingon-linetax.2. Literaturereview2.1. On-linetaxdeclarationAs the Internet and its applications are increasingly becoming popular in businessorganizationandpublicinstitutionsandgovernmentsareindeedaspecial typeofserviceindustry, itsapplicationsinpublicagenciesore-governmentinTaiwanhasbeengreatlydrivenbycurrentandpreviousadministrationsforprovidingcitizensandorganizations withmore convenient access togovernment informationandbetter services. Among them, on-line tax declaration is one of the top priorities in theconstructionof e-government andbeginsfortrial andexperimental usearound2years agoandis goingfor the third-year period. Taxpayers are still allowedtodeclare their tax for the choice of either paper form or e-form. In order words, it is avoluntary-basedcontext foruseofemergingtechnology. Until now, on-linetaxisstill in the initial stage of its usage and the usage rate is still relatively low for keepingin the interval of 1015% while it was initially launched in the year 2000. There is noindicationinastablegrowthof its usageinthenearfuture. Onthebasis of thedilemmaintheuseofon-linetax,thechallengesmaylieinconvincingtaxpayersofcommunicating with on-line tax in an efcient, effective, and safe manner. This studyARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 786tries to understand, analyse, and solve this problem from the perspective of the initialadoptionofvirtual service. Thismayexplainsomeofthemajorreasonsforalowrateinsystemusage.2.2. RelevantmodelsinITadoptionTAMisanadaptationofthetheoryofreasonedaction(TRA)byFishbeinandAjzen(1975) andmainly designedfor modeling user acceptance of informationtechnology(Davisetal.,1989).Thismodelhypothesizesthatsystemuseisdirectlydeterminedbybehavioral intentiontouse, whichis inturninuencedbyusersattitudetowardusingthesystemandPUofthesystem. AttitudeandPUarealsoaffectedbyPEOU. PU, reectingapersonssalientbeliefinusingthetechnology,will be helpful in improving performance. PEOU, explaining a persons salientbeliefsinusingthetechnology, will befreeofanyeffort(TaylorandTodd, 1995).The appeal of this model lies in both specic andparsimonious as well as anindication of high prediction power of technology usage. These determinants are alsoeasytounderstandforsystemdevelopersandcanbespecicallyconsideredduringsystem requirement analysis and other system development stages. These factors arecommon in technology-usage settings and can be applied widely to solve theacceptanceproblem(TaylorandTodd,1995).TPBunderlyingtheeffortofTRAhasbeenprovensuccessful inpredictingandexplaininghumanbehavioracrossvariousinformationtechnologies(Ajzen, 1991,2002). According toTPB,a persons actualbehaviorin performingcertain action isdirectly inuenced by his or her behavioral intention and in turn, jointly determinedby attitude, subjective norm and perceived behavioral control toward performing thebehavior. Behavioral intention is a measure of the strength of ones willingness to tryandexertwhileperformingcertainbehavior. Attitude(A)explainsthefeelingofapersons favorableor unfavorableassessment regardingthebehavior inquestion.Furthermore, a favorable or unfavorable attitude is a direct inuence to the strengthof behavioral beliefs about the likely salient consequences. Accordingly, attitude (A)is equatedwithattitudinal belief (abi) linkingthe behavior toacertainoutcomeweightedbyanevaluationof thedesirabilityof that outcome(ei) inquestion, i.e.A Sabiei. Subjectivenorm(SN) expressestheperceivedorganizational orsocialpressureofapersonwhileintendingtoperformthebehaviorinquestion.Inotherword, subjective norm is relative to normative beliefs about the expectations of otherpersons. It can be depicted as individuals normative belief (nbi) concerning aparticular referent weightedbymotivationtocomplywiththat referent (mci) inquestion,i.e.SN Snbimci.Perceived behavioral control (PBC) reects a persons perception of ease ordifculty toward implementing the behavior in interest. It concerns the beliefs aboutpresenceof control factorsthat mayfacilitateorhindertoperformthebehavior.Thus, control beliefs about resources and opportunities are the underlyingdeterminant of perceived behavioral control and it can be depicted as controlbeliefs(cbi)weightedbyperceivedpowerofthecontrol factor(pi)inquestion, i.e.PBC Scbipi. In sum, grounded on the effort of TRA, TPB is proposed to eliminateARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 787the limitations of the original model in dealing with the behavior over which peoplehave incomplete volitional control (Ajzen, 1991). In essence, TPB differs from TRAinitsadditionofthecomponentofperceivedbehaviorcontrol.However, TPBdoes not further elaborate the relationshipbetweenthe beliefstructures (i.e. Sabiei, Snbimci, Scbipi) and the antecedents (attitude, subjectivenorm, perceivedbehaviorcontrol) of intention. TPBsimplycombineseachof thebeliefstructuresintooneunidimensional beliefconstructandasaresult,thebeliefstructures, infact, representing a variety of underlying dimensions, may not beconsistently related to the antecedents of intention. Moreover, the underlyingdimensionsofthebeliefsstructuresare,inessence,differentforvariousapplicationsettings and this combination makes TPB difcult to be generalizable across varioussettings. Bydecomposingthe belief structures of TPB(DecomposedTPB), theirrelationships should become clearer, more understandable for practical purpose(TaylorandTodd,1995).Attitudinal belief structureis decomposed into threedimensions: ease of use, PU,andcompatibility. Normativebeliefstructureisdecomposedintotwodimensions:peer and superior inuences. Control belief structure is decomposed into threedimensions:individualself-efcacy,resourcefacilitatingconditions,andtechnologyfacilitatingconditions. Afterthat, whilecomparingDecomposedTPBwithTAM,TAMis, infact, apartofDecomposedTPBandconsequently, DecomposedTPBshouldprovideamorecompleteunderstandingofITadoptionrelativetothemoreparsimoniousTAM(TaylorandTodd,1995).Basedontheabovelogic,itisbetteroff toextendTrust andTAMmodel withTPBor DecomposedTPBtowidelyconsider the potential underlying determinants, system features, individuals,organizational members and social system, for better predicting the intentiontowardtheinitialadoptionofon-linetax.2.3. TrustThefunctionalityandcontributionoftrustcanbeapparentlyidentiedfromtheeconomicframeworkofsocial exchange(KelleyandThibaut, 1978; Kelley, 1979).Within social exchange, business transactions are usually carried out without explicitcontract or control mechanismagainst opportunisticbehavior sothat thepartiesinvolvedinthese activities are not able toattaincomplete legal protectionandexposethemselvesinacomplicatedsocial environment withmassuncertainty. Toinsure better rewards from the economic activities, people make efforts to reduce thissocialcomplexityandavoidriskfrombeingexploited(Wrightsman,1972).Trustisbasically seen as a common mechanism for reducing social complexity and perceivedriskof transactionthroughincreasingtheexpectationof apositiveoutcomeandperceivedcertainty regarding the expectedbehavior of trustee (Luhmann, 1979;Grabner-Kraeuter, 2002; Gefen, 2004). Inparticularforon-linebusiness, withoutreducingsocial complexityandriskresultingfromthe undesirable opportunisticbehaviorof e-vendor, onlyshort-termtransactionswouldbepossible(Kimet al.,2004; Pavlou and Gefen, 2004). Accordingly, trust is an important determinant in e-commerceincludingpublicservices.ARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 788Moreover,trustwasfurtherexplainedmoreclearlyintermsofanumberoftrustantecedents: knowledge-basedtrust, cognition-basedtrust, calculative-basedtrust,institution-based trust, and personality-based trust (Zucker, 1986; Gefen et al.,2003a). Knowledge-based trustis built on familiarity with other parties. Familiaritybuilds trust because it reduces social uncertainty through increased understanding ofwhatishappeninginthepresent(Luhmann,1979).Cognition-basedtrustexamineshowtrust is developedfromrst impressionrather thanthroughexperience ofpersonal interactions. Accordingtothis researchstream, cognition-basedtrust isformedthroughcategorizationprocessandillusionof control (BrewerandSilver,1978; Meyersonetal., 1996). Calculative-basedtrustcanbedevelopedbypeoplesrational assessment of the costs andbenets of another partywhile cheatingorcooperatinginthe relationship. Trust inthis viewis derivedfromaneconomicanalysis occurring in ongoing relationship, namely that it is not worthy for the otherpartytoengage inopportunisticbehavior (Coleman, 1990; Lewicki andBunker,1995; Doney et al., 1998). Institution-based trust refers to an individuals perceptionofaninstitutional context, whichmainlyconcernssecurityfromguarantees, safetynets,orotherimpersonalstructuresinherentinthespeciccontext(Shapiro,1987;McKnight et al., 1998). Personality-basedtrust or propensity trust explains thetendencytobelieveornottobelieveinothersandfurthertrustthem. Thistypeoftrust is basedonabelief that the others aretypicallywell meaningandreliable(Wrightsman,1972;McKnightetal.,2002).Amongthevetypesoftrustantecedents,cognition-based andpersonality-basedtrustsaremorerelevanttotheformationoftheinitialtrust,sincepeopleinherentlyhas cognitive resource limitation for often recognizing subjects by the rstimpressionandpersonalityis animportant determinant inthe initial stage of arelationshipbuilding. Initial trustreferstotrustinanunfamiliartrusteewhiletheactorsdonotyethavecredible, meaningful informationaboutoraffectiveboundswith each other. While people gain experience and familiarity with the trustee in thelater stage, continued trust by people will be more inuenced by experiential personalinteraction(McKnight et al., 1998). Insum, as on-linetaxis atypeof e-servicebetween government agency and citizens, and their transactions are primarilythrough virtual channel without face-to-face contact, perceived uncertainty and riskassociatedwithon-linetaxarethemajorconcernofthecitizensinusingthisnewtechnology. Trust will betheimportant potential inuencertoexaminetheinitialadoptionofon-linetax.2.4. TrustandTAMrelationshipTheconnectionsbetweentrustandTAMhavebeenwidely discussedinliteraturein that the relationships between PU, PEOU, and trust are hypothesized in many on-line-basedbusinesssettings(Gefenetal.,2003a, b;Pavlou,2003;Saeedetal.,2003;Gefen,2004).Inparticular,amodelofTrustandTAMwaswelldenedinon-lineshopping setting (Gefen et al., 2003a). This model explicitly indicated theirrelationshipastrustisanantecedentofPU, PEOUisanantecedentoftrust, andtrust has a direct inuence onbehavioral intentiontouse. Trust is one of theARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 789determinants of PU, especially in an on-line environment, because part of theguaranteethat consumers will sensetheexpectedusefulness fromthewebsiteisbasedonthe sellers behindthe website. Moreover, trust is recognizedtohavepositive effect on PU since trust allows consumers to become vulnerable to e-vendortoensurethattheygaintheexpectedusefulinteractionandservice(Pavlou,2003).Whileconsumersinitiallytrusttheire-vendorsandhaveanideathatadoptingon-line service is benecial to their job performance, they will believe the on-line serviceisuseful(Gefenetal.,2003a).Onthe other hand, PEOUis hypothesizedtohave positive inuence ontrustbecausePEOUcanhelppromotecustomers favorableimpressionone-vendorsintheinitial adoptionofon-lineserviceandfurther, causecustomerstobewillingtomade investment and commitment in buyer-seller relationship (Ganesan, 1994;Gefenet al., 2003a). Ingeneral, whilefollowingthedenitionof social cognitivetheory, PEOUcanbearguedtopositivelyinuenceapersonsfavorableoutcomeexpectationtowardtheacceptance of aninnovative technology(Bandura, 1986).This is because cognition-based trust,as discussed previously,is mainly builton therst impression of a person toward certain behavior and extensively, PEOU in termsof on-lineservicecanbeconsideredtherstfeelingorexpectationestablishedforfurther continuedon-line transaction. Insum, while on-line tax is consideredaspecial type of e-service, the Trust and TAM model is partly tted to this on-line taxsetting while there are additional variables, as discussed below, to be included in theparticularcontext.2.5. TrustandTPBrelationshipThe relationship between trust and TPB can be examined in a variety of aspects inwhich trust is hypothesized as the common antecedent of attitude, perceivebehavioral control, andsubjectivenorm. Forattitudeconstruct, trust ine-vendoris viewed as a salient behavioral belief that directly affects customers attitude towardthe purchase behavior. While an e-vendor is trustworthy, it is more possible that theconsumerwill gainbenetsandavoidpossiblerisksfromadoptingon-lineservice(McKnight andChervany2002; Pavlou, 2003). As cost-benet paradigmgreatlyinuencespeoplesattitudinalbeliefsandoutcomejudgments,trustcanbeadirectinuencerthatdeterminespeoplesattitudetowardbehavior(Bandura,1986;Daviset al., 1989). Besides, research has shown that trust denitely increases thecondentialityof business relationshipanddetermines the qualityof transactionbetween buyers and sellers as well as peoples outcome expectation on manycommerce activities (Luhmann, 1979; Lewis andWeigert, 1985; Hosmer, 1995).According to social cognitive theory, outcome expectation refers to peoplesestimation of a given behavior yielding a particular outcome, which is closely relatedto peoples attitude toward behavior (Bandura, 1986). Therefore, trust is apparentlyanimportantantecedentofattitudetowardtheon-linetransactionbehavior.For perceived behavioral control construct, trust can increase perceivedbehavioral control overon-linetransactionssincethevirtual interactionsbetweencustomersande-vendorsbecomemoreexpectable(Pavlou, 2002). Explicitly, trustARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 790inuencesperceivedbehavioral control throughcontrol factorsofself-efcacyandfacilitating favorable conditions. According to the psychological reports, self-efcacyinpersonal relationships is constructedfromself-condence andmutual trust infriendships (Matsushima and Shiomi, 2003). Hence, mutual trustin the relationshipbetween customers and e-vendors should increase customer self-efcacy and in turn,increaseperceivedbehavioralcontrol.Ontheotherhand,trustcanbeaperceptualresourcethatfacilitatescustomerstogaincontrol overon-linetransactions. Whilecustomerstrustane-vendorthatbehavesinaccordancewiththeirexpectation,thetrustbeliefsarelikelytoincreasecustomersperceivedbehavioralcontrol overon-linetransactions(Pavlou,2002).For subjective normconstruct, researchers have foundthat mutual trust andmutual inuence between users and IS units are highly correlated to each other basedonastudyconcerningtheperformanceof informationsystemgroup(NelsonandCooprider,1996).Furthermore,DecomposedTPBrevealedthattherearepeerandsuperior inuences on users for determining subjective normtoward IS usage(TaylorandTodd, 1995). Derivatively, itcanbepredictedthat trust inpeersandsuperiors about their beliefs of IS usage should play a role in determining subjectivenorm.Similarly,trustine-vendorsabouttheirreputation,brandname,andservicemaypositivelyinuencesubjectivenormoverthebehaviorofon-linetransactions.Besides,theymayindicatecertainrelationshipbetweentrustinpeersandsuperiorsandtrustinvendors.Astheopinionsfromthereferentsofpeersandsuperiorsarepositive for certaine-vendors inthe market, trust inpeers andsuperiors inthissituationcanenhanceuserbeliefsintrustingthesee-vendorsandinturn,subjectivenorm toward the behavior of on-line transactions. Therefore, whatever types of trustare with direct and indirect inuences on subjective norm, they are all the importantantecedentsofsubjectivenorminon-lineservice.3. ResearchmodelWhile on-line tax is considered as a special type of e-service, the initial adoption inon-linetax, inessence, concerns boththeroles of theInternet technologyande-vendorinprovidingservice.TheTrustandTAMmodelinGefenetal.(2003a)hasbeenwell studiedinon-lineshoppingsettingandshowedthatunderstandingboththe Internet technology and trust issue is critical in determining behavioral intentionto use on-line shopping, as discussed in Section 2.3. Besides, the diffusion of on-linetax could also be inuenced by the potential antecedents such as individuals,organizational members, and social system while the issue for innovative technologyis well discussed in Rogers (1995). An extension of Trust and TAM model with TPBwouldbeinmorecomprehensivemanner tounderstandtheacceptancebehaviortoward on-line tax and hopefully, this extension would provide us with higherexplanatorypowertoexaminethisproblemandeffectivelyimprovethelowusagerate. This extensionmodel inon-linetaxis indicatedinFig. 1. Accordingly, thehypothesesarepresentedasbelow.ARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 791Hypotheses 1, 2, 5, 6, and 10 are proposed based on TAM as discussed in Section2.1whileHypotheses3and4areinitiatedunderlyingTPBasdescribedinSection2.1. More importantly, Hypotheses 79 are the unique features from Trust and TAMmodel,whicharederivedfromthedetaileddiscussionintherst,second,andthirdparagraphs of Section 2.4, respectively. Hypotheses 11 and 12 are mainly developedbasedonTrust andTAMmodel inSection2.3, i.e. PEOUindicatedas adirectpredictiontotrust andtrust toPU. Furthermore, these hypotheses were furtherveriedfortheirvaliditybyempiricaldata.Hypothesis1. PUhaspositiveeffectonintentiontouseon-linetax.Hypothesis2. Attitudehaspositiveimpactonintentiontouseon-linetax.Hypothesis3. Perceivedbehaviorcontrol positivelyinuencesintentiontouseon-linetax.Hypothesis4. Subjectivenormhaspositiveeffectonintentiontouseon-linetax.Hypothesis5. PUhaspositiveimpactonattitudetouseon-linetax.Hypothesis6. PEOUpositivelyinuencesattitudetouseon-linetax.Hypothesis7. Trusthaspositiveeffectonattitudetouseon-linetax.Hypothesis 8. Trust has positive impact on perceived behavior control to use on-linetax.Hypothesis9. Trustpositivelyinuencessubjectivenormtouseon-linetax.Hypothesis10. PEOUhaspositiveimpactonPUtouseon-linetax.ARTICLEINPRESSPEOUTrustPUAttitude IntentionSNPBCH1H2H3H4H5H6H7H8H9H10H11H12TAMTPBFig.1. Researchmodel.I.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 792Hypothesis11. TrusthaspositiveeffectonPUtouseon-linetax.Hypothesis12. PEOUpositivelyinuencestrustinusingon-linetax.4. ResearchdesignA largesample survey of on-line tax declarationwas employedto empirically testthisresearchmodel.Theinstrumentandrespondentsamplearedesignedasbelow.4.1. InstrumentdevelopmentTheinstrument isdesignedtoincludeafour-part questionnaireaspresentedinAppendixA. Therst part is nominal scales andtheremainders areseven-pointLikertscales.4.1.1. BasicinformationThispartofquestionnaireisusedtocollectbasicinformationaboutrespondentcharacteristicsincludinggender, age, education, occupation, andexperience(one-timeusersfortherstyear,orcontinuedusersformorethan1-yearexperience)inon-lineincometaxdeclaration.4.1.2. TAMThis part of questionnaire is constructedbasedonthe constructs of PUandPEOUinTAMmodelandisadaptedfromthemeasurementdenedbyVenkateshandDavis(1996,2000),containingfouritemsforbothconstructs.4.1.3. TPBThis part of questionnaire is developed based on the constructs of attitude,perceived behavior control, subjective norm, and intention to use. Attitude isadapted fromthe measurement dened by Bhattacherjee (2000), including fouritems. Perceived behavior control was adapted fromthe measurement denedby Taylor and Todd (1995) and Bhattacherjee (2000), including three items.Subjective normis adaptedfromthe measurement denedbyTaylor andTodd(1995)andBhattacherjee(2000), includingthreeitems. Intentiontouseisadaptedby the measurement dened by Venkatesh and Davis (1996, 2000), includingthreeitems.4.1.4. TrustTrustitemsarecomposedtoreecttrustbeliefsofcitizensinusingon-linetax.ThispartofquestionnaireisthusadaptedfromthestudyofGefenetal. (2003a).Because the measurement in Gefen et al. is originally developed for on-line businessandits focus is oncustomerseller relationship, therefore, acoupleof measuringitemsconcerningmarket, opportunistic, andhonestissues, whichareirrelevanttoARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 793theon-linetaxsetting, aredroppedfromthelist. Afterthescreenandshorteningprocess,thispartcomprisesthreeitems.4.2. SampleorganizationsandrespondentsInorder tocollect on-line taxdeclarationusers information, researchers rstrequiredgettingpermissionfromtheTaxBureautoexpresstheneedforacademicresearchpurpose.Basically,thepersonalinformationoftheusersinon-lineincometax declarationis condential under the lawof privacy right andforbiddentodistributeit. However, undercertaincircumstances, theTaxBureaucanpermittoprovidecertaintypes of thepersonal informationfor academicresearchpurposewhileatthesametimewithoutviolatingthelawofprivacyright. Theapplicationprocedureforthisserviceisdescribedasbelow.Whiletheapplicationgetsapproval, theTaxBureauwill helpe-mail invitationletterstotheusersinthee-servicewithanelicitationmessageforthepurposeofunderstanding their experience in the initial adoption of on-line income taxdeclaration. Theinvitationletteralsoindicatesawebsitefortheuserstoinstantlyhyperlink to an on-line questionnaire. The users are free to participate in thisinvitation. After that, 8000 users were randomly selected from the population sampleand accordingly, invitation letters were sent out by e-mail. Furthermore, in order toimprove surveyreturn, follow-upprocedure wascarriedout withanotherinvitationletterfornon-respondingusersafter3weeks.4.3. SampledemographicsOf the 8000 on-line questionnaires distributed, 1383 users were replied, withincomplete response and not the one-time users (the continued users) deleted,resulting in a sample size of 1032 users for an overall response rate of 12.9%. SampledemographicsaredepictedinTable1. Theseeminglylowresponserateraisestheconcern about non-response bias.A test for non-response bias wasconducted usingtworesponding subsamples: earlyandlate respondents. These twogroups werecorrelatedonthesamplecharacteristicsofgender,age,education,occupation,andexperience.Theresultindicatesthatthereisnosignicantsystematicnon-responseARTICLEINPRESSTable1SampledemographicsGender Age Educationlevel OccupationFemale 20.1% o20 0.3% Highschool 8.9% Finance 7.5%Male 79.9% 2029 10.9% College 60.4% Institution 22.9%3039 44.8% Graduate 26.5% Information 20.3%4049 30.2% Doctorate 4.2% Service 15.2%450 13.8% Manufacturing 11.9%Others 22.2%I.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 794bias in the respondent sample, suggesting that the respondent sample was a randomsubsetofthesampleframe.5. Analysisandndings5.1. AnalysisofthemeasurementmodelFirst, contentvaliditiesshouldberelativelyacceptablesincethevariouspartsofquestionnairewerealladaptedfromtheliteratureandhavebeenreviewedcarefullybypractitioners.Next,conrmatoryfactoranalysisinAMOSsoftwarewasusedtoanalyse construct validities, basically the analytical procedure including threestages as described below. First, a measurement model should be assessed forgoodness-of-t. The literature suggested that, for a good model t, chi-square/degrees of freedom(w2=df) shouldbe less than3, adjustedgoodness-of-t index(AGFI) shouldbelarger then0.8, goodness-of-t index(GFI), normedt index(NFI), and comparative t index (CFI) should all be greater than 0.9, and root meansquareerror (RMSE) shouldbeless than0.10(HenryandStone, 1994). Second,convergentvalidityisassessedbythreecriteria.Itemloading(l)isatleast0.7andsignicant, composite construct reliability is a minimum of 0.8, and average varianceextracted(AVE) for a construct is larger than0.5(Fornell andLarcker, 1981).Finally, discriminant validity is assessedby the measure that the AVEof eachconstruct should belarger than itssquare correlation with other constructs (FornellandLarcker,1981).The indices for the measurement model indicateagoodt withw2=df (991.1/231 4.29), AGFI(0.90), GFI(0.93), NFI(0.97), CFI(0.98), andRMSE(0.056).The results of reliability as well as convergent and discriminant validities forthis model are reported in Table 2. The itemloading (l) for these constructsranges from0.78to0.98andis alsosignicant at 0.01level, construct reliabilityARTICLEINPRESSTable2Constructreliability,convergentvalidityanddiscriminantvalidityConstruct Itemloading Constructreliability FactorcorrelationsAVE ATT PEOU INT PBC PU SN TSTATT 0.800.90 0.92 0.75 PEOU 0.930.97 0.96 0.87 0.54 INT 0.970.98 0.98 0.95 0.82 0.50 PBC 0.920.94 0.95 0.85 0.75 0.65 0.73 PU 0.840.92 0.93 0.77 0.67 0.48 0.59 0.52 SN 0.780.98 0.86 0.67 0.24 0.16 0.24 0.17 0.22 TST 0.840.98 0.92 0.79 0.63 0.44 0.57 0.55 0.45 0.24 Attitude (ATT), Perceived ease of use (PEOU), Intention (INT), Perceived belief control (PBC), Perceivedusefulness(PU),Subjectivenorm(SN),Trust(TST).I.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 795ranges from0.86to0.98, andAVEranges from0.67to0.95. AppendixBalsoreports thecovariancematrixgeneratedbyAMOS. Moreover, theAVEof eachconstruct is all above its square correlation with other constructs. Thus, thismeasurementmodelindicatesahighdegreeofreliabilityaswellasconvergentanddiscriminantvalidities.5.2. AnalysisofthestructuralmodelThetechniqueof structuredequationmodelingwasusedtoexaminethecausalstructure of the proposed model in this study. The evaluation of this research modelcan be carried out in three steps. First, a GFI for the structural model was examinedas the same GFIs applied in assessing the measurement model. Second, thestandardizedpathcoefcientsandtheirstatisticalsignicanceforthehypothesesinthis model were estimated. Finally, as a measure of the entire structural equation, anoverall coefcient of determination R2 was calculated, similar tothat foundinmultiple regression analysis. The testingresults of GFIs are all under the acceptablelevels with, w2=df (1049.2/236 4.45), AGFI (0.90), GFI(0.92), NFI(0.97), CFI(0.97), andRMSE(0.06). Furthermore, thestandardizedpathcoefcients areallsignicant at 0.01level except for thepaths fromPUtointentionandsubjectivenorm to intention. As a result, Hypothesis 1 and 4 are not supported while the otherhypotheses are all supported. In general, trust indicates important relationships withthe three antecedents of intention to use in TPB while the relationships in Trust andTAM model are maintained in on-line tax. The detailed discussion of the results willbepresentedbytheorderoftheantecedentsofintentiontouse,attitude,perceivedbehavioral control, andsubjectivenormaswell astherelationshipsamongtrust,PEOU,andPUinTrustandTAMmodel(Fig.2).ARTICLEINPRESSPEOUTrustR2 = 0.19PUR2 = 0.31AttitudeR2 = 0.59IntentionR2 = 0.69SNR2 = 0.08PBCR2 = 0.270.080.55*0.27*0.080.34*0.21*0.40*0.33*0.24*0.35*0.30*0.44*Fig. 2. Standardizedsolutionof thestructural model. Number onpath: standardizedcoefcient, R2:coefcientofdetermination,*:po0:01.I.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 796Intentiontouseon-linetaxinthisresearchisjointlypredictedbyPU(b 0:08,Standardized path coefcient), attitude (b 0:55), perceived behavior control(b 0:27), andsubjectivenorm(b 0:05)andthesevariablestotallyexplain69%ofthevarianceonintentiontouse(R2 0:69,Coefcientofdetermination).WhilecomparingthepresentedresultswithpreviousTPB-basedstudiesinISacceptance,the explanatory powerof the currentresearch model for behavioral intention to useishigherthanTaylorandTodd(1995) withR2 0:60, Bhattacherjee(2000) withR2 0:52, andChauandHu(2001) withR2 0:42. Amongtheserelationships,attitude toward the behavior and perceived behavior control are two majorinuencers on individuals behavioral intention to use on-line tax. Moreover,attitudeindicatesmoreimportancethanperceivedbehaviorcontrol indeterminingbehavioral intentiontouseon-linetax. Theresult quiteconformstothendingsreported with business-based setting in prior research. Nevertheless, PUandsubjective norm do not produce signicant impacts on behavioral intention to use inthisresearch.For the result in PU, previous empirical studies on TAM and extended TAM haveshown inconsistence for either with signicant inuence (Moore and Benbasat, 1991;Chau, 1996) or withinsignicant inuenceonbehavioral intentiontouse (Chenet al., 2002). Indeed, it, in essence, implies an indirect inuence of PU on behavioralintentiontouse viathe mediator, attitude towardusingon-line tax. Aplausiblereasonforthismaybeexplainedasbelow. Theon-linetaxcontextinthisstudyisfocusedonthestageof theinitial adoptionandvoluntaryuseintaxdeclaration.In other words, users in the on-line tax are still in a trial and experimentalmanner. Users positive PUinusingon-line taxmaynot immediatelyleadtoabehavioral intention to use, rather than rstly form a favorable attitude/belief to useon-line tax. The favorable attitude/belief to use on-line tax is just like a time cushionbefore directly taking behavioral intentiontouse on-line tax. This implies thatpotential users would need to take a period of time to carefully change theirpsychological state to adopting on-line tax. Consequently, the attitude towardadopting on-line tax demonstrates a larger inuential power on behavioral intentiontouse(b 0:55).Forsubjectivenorm, theresult issimilartothendingreportedinTaylorandTodd (1995) and Chau and Hu (2001), but differs from the conclusion inBhattacherjee (2000) for exploring the adoption of e-service with the case ofelectronicbrokerage. Thelatteroneindicatedthatsubjectivenormcouldinuenceintentiontouseasstrongasattitudedoes. However, VenkateshandDavis(2000)gaveamorecompletereportinthatsubjectivenormcouldsignicantlydetermineintentiontouseinamandatory-usagecontext, but itsimpact wouldbecomelesssignicantwhileusersareinavoluntary-usagecontextasthecaseofon-linetaxinthis study. Inparticular, while on-line taxinthis study is placingat the initialadoptionstage, there are lackof enoughreferences fromprior adopters suchasfriends, peers and superiors (perceived social pressure). From the perspective, on-linetaxinthis studyquitediffers fromthecaseof e-serviceinBhattacherjees study.Accordingly, it is reasonable to expect that the effect of subjective norm on intentiontouseon-linetaxshouldindicateinsignicance.ARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 797Attitudeis predictedbyPU(b 0:34), PEOU(b 0:21), andtrust (b 0:40)withjointly59%of the total variance explained. Inthat, the effect of trust onattitude is greater than PU and PEOU. This implies an important fact for researchersthattraditionalTAMmaynotcompletelyexplaintheattitude/behaviortowardtheacceptanceofon-linetax.TheresultalsopartiallyvalidatestheconclusionofTrustand TAM model by Gefen et al. (2003a) since the inuential relationship is in termsoftrustandbehavioral intentiontouseintheTrustandTAMmodel. Ingeneral,trust should be necessarily included in TAMfor effectively understanding theacceptance of e-service. Moreover, trust (b 0:33) explains 27%of the totalvariance in determining perceived behavioral control and is considered as animportant antecedent of perceived behavioral control in on-line tax. In other words,whilecitizenstrusttheon-linetaxproviderthatbehavestoimproveself-efcacyincomputer or external resource constraint suchas the Internet infrastructure forcitizens, the trust beliefs will be able to increase citizens perceived behavioral controlinperformingthebehavior.Ontheotherhand,trust(b 0:24)signicantlyinuencessubjectivenormwhileexplainingonly8%ofthetotalvarianceinsubjectivenorm. Thereasonforthisistwo-fold. First, this indicates that while users establish the initial trust in on-line tax,itwillhelpenhancetheusersnormativebeliefsabouttheexpectationsofreferentssuchasfriends,peers,and superiorswhoconcerntheinitialadoptionoftheon-linetax. Theconnectionbetweenuserstrust andperceivedsocial pressuretoperformon-line taxbehavior seems tobe expectable as the underlying denitioninthismodel. Next, thereasonfor8%of thetotal varianceexplainedmight bebecausethere are a number of potential inuencers tosubjective normremaining tobeidentiedforaccountingfortherestofthetotal varianceexplained. Insum, trust,generally,iscloselylinkedtothethreeantecedentsofbehavioralintentiontouseinTPB in the on-line tax setting. This validates the necessity to extend Trust and TAMmodel with TPB in this study in order to have larger explanatory power in the initialadoptionofon-linetax(R2 0:69asindicatedabove).Finally, trust (b 0:30) and PEOU (b 0:35) both signicantly inuence PU andjointly explain 31% of the total variance in PU. The former is similar to the ndingsreportedintheliteraturesuchas Trust andTAMmodel inGefenet al. (2003a)and this model discussed in Pavlou (2003). The latter regularly corroboratesmost prior research on TAMin both on-line and general information techno-logies. Furthermore, PEOU (b 0:44), as discussed earlier in the literature,signicantlyaffectstrustand explains19% ofthe totalvariance intrust.This resultalsoconformstoTrustand TAMmodelinGefen etal.(2003a)inon-lineshoppingsetting.6. GeneraldiscussionsThere are many issues inuencing users decision in the initial adoption of on-lineservice. While considering both the Internet and e-vendor issues in the acceptance ofon-lineservice,TrustandTAMmodel,asdiscussedinGefenetal.(2003a),iswellARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 798dened for its validityin exploring on-lineservice setting. Extensively,on-line tax isconsidered as a special type of e-service and the diffusion of this servicemight concernthe roles of individual state, organizational members, andsocialsystemexcept for the factors inTAM. Anextensionof Trust andTAMmodelwithTPBaimsat increasingthepredictivepowerofbehavioralintentiontouseon-linetax. Empirical datashowthat trust isconsideredasanimportant antecedentof the three determinants of intentiontouse, attitude withb 0:40, perceivedbehavioral control with b 0:33, and subjective normwith b 0:24, and inturn, jointlycontributes ahighexplanatorypower withR2 0:69tobehavioralintention to use on-line tax. While compared to other models with trust and TAM inthe literature, this extension with TPB empirically demonstrates substantialimprovement intheexplanatorypowerof behavioral intentiontouseon-linetax.This result indeed provides more insight for understanding the low usage rate in on-linetax.Next, although PU(b 0:34) and PEOU(b 0:21) in TAMmore likelyrepresenting technology-based antecedents both signicantly inuence attitudetowardthe behavior; however, trust (b 0:40) indicatingtrust-basedantecedentdemonstratesmorepositiveimpactontheattitude. Theresultsindicateafactthatinitial users tend to rely more on trust in non-technology features than on PEOU andusefulnessintechnology-basedfeaturestoformtheirattitudetowardthebehavior.As a result, they jointly determine 59%of the total variance in the attitude.Moreover,trust(b 0:30)and PEOU(b 0:35)bothhavepositiveimpactonPU.As we knew fromprevious research, PUalways showed it as an importantdeterminant of attitude inTAMandPEOUmayoftenindicate its inuence onattitude through the mediator of PU. The reason can be explained by that PEOU hasbeenwell recognizedasabasicrequirementforsystemdesignanddeductively, itsimpact on attitude toward adopting information technology has increasingly becomeoflessimportance(Davisetal., 1989; Chau, 1996). Thiscanalsobefoundinthisstudy, b 0:35 for PEOUlinking toPUversus b 0:21 for PEOUlinking toattitude. In addition, this study indicates that trust almost plays an equallyinuencingroleonPUasPEOU.7. ConclusionsThe purpose of this research is to propose an extension of Trust and TAM modelwithTPBinamorecomprehensivemanner that jointlypredicts user acceptance(initial adoption) in on-line tax. A large sample survey from users of on-line tax wasemployed to empirically examine this research model. There are several new ndingsregardingtherolesofTrust,TAM,andTPBinon-linetaxasdiscussedpreviously.Thesendingshaveimportantimplicationsforbothpractitionersandresearchers.For practitioners, although on-line tax is mainly presented for usage by thefeatures of the Internet and communication technologies, however, this studyshows that recognizing both technological and trust-based issues are importantinincreasing citizens behavioral intentiontouse this service. The TAMbeliefsARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 799(PUandPEOU) andtrust areshowntobetwosetsof underlyingantecedentsindeterminingbehavioral intentiontouse, eachcontributingitssignicant inuenceonbehavioral intentiontouse throughanumber of mediators suchas attitude,perceivedbehavioral control, andsubjectivenorm. This means that toeffectivelyattract citizens to use on-line tax, the design of on-line tax needs to carefullypayattentiontobothaspects.Besides,asdiscussedpreviously,noviceuserstendtorely more on trust in non-technology features than on PEOUand usefulnessin technology-based features to develop their attitude toward the behavior. Inother words, trust is more important indeterminingusers attitude thanPEOUand usefulness in on-line tax. The major trust-based concerns may includeprivacy protection, accuracy to declaration, and unauthorized access andsoon.Fundamentally, while trust is empirically identied as an antecedent of PU and inturn, an antecedent of attitude, this has some practical implications in enhancing theattitudetowardusingon-linetax. On-linetaxprovidershouldrst developtrust-buildingmechanismsforcitizensinordertoattractnoviceuserstoaccepton-linetax. Examples of the mechanisms include statements of guarantees, increasedfamiliarity through advertising, long-term customer service, and offeringincentives touse. After that, PUof on-linetaxemergesas animportant issueinattracting new users and should be carefully designed in terms of users requirementstoreectPUofthisservice.Withoutanoriginalconsiderationfromtrustaspect,awell-designedon-line taxwithsignicant PUwill not well performinattractingnoviceusers.Forresearchers, pastresearchontechnologyacceptanceimplicitlyassumedthatthesuccessofsystemuseismainlydependentontechnologicalaspectanddoesnotconsider the notion of uncertainty. However, the advent of the Internet hasintroduceduncertaintyandriskinsystemacceptanceandusebecausepeopleoftenneedtousetheInternettocommunicate,collaborate,andtransactwithindividualsandorganizations without physical face-to-face interaction. Thus, uncertainty isincreasingly becoming the underlying determinant of the Internet-base system usage.Traditionally, TAMmainlyfocuses ontheaspect of systemfeatures andthus, isinsufcient in capturing the roles of individuals, organizational members, andsocial systeminthe Internet-basedsystemusage, inparticular, on-line tax. TPBwiththeantecedentsofattitude,perceivedbehavioralcontrol,andsubjectivenormwill beinacomplementarymannertoenhancethepredictioncapabilityofTAM.ThisstudyextendsTrustandTAMmodel withTPBinexploringon-linetaxandfurther, empirically demonstrates relatively satisfactory results for providingmoreinsighttothisproblem.Thisapproachmaybeasabasisforsimilarresearchinthearea.Furthermore, subsequentresearchcanbefoundedonthiswork. Thisstudyhasfocused on users who are inexperienced or the initial adoption in e-service. However,prior researchhas suggestedthat determinants of behavioral intentionchangeintermsofuserslevelofexperience(McKnightetal.,1998;Karahannaetal.,1999).Additional research, both longitudinal and cross-sectional, is needed to examine thedifferences of this framework as users evolving from being aware of the e-vendor, toARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 800having experience with the e-vendor, to being continued use of the e-vendor.Despitethesignicant inuenceof trust onsubjectivenorm, thereis only8%oftotal variance explained in subjective norm. Thus, it is possible to identifypotential factors that could inuence subjective normto some extent. Futureresearchcouldbeexploredonthemattertobetterpredictsubjectivenormandinturn, behavioralintentiontouse. Otherpossiblebeliefshavebeensuggestedinthemanagement andpsychological areas, includingloyalty, reliability, andopenness(Hosmer, 1995). More research with the alternative conceptualization of trust wouldbeuseful inmoreunderstandingtheroleoftrustintheinitial adoptionofon-lineservice.Finally, although this study has produced some interesting results, it may still havesome limitations. First, approximately 80%of the respondents are male inthisempirical study. Much research has shown that gender difference could causediscrepanciesintheeffectsofattitude,perceivedbehavioralcontrol,andsubjectivenormonusersbehavioralintention(VenkateshandMorris,2000;Armitageetal.,2002). Although gender does not produce statistical signicance on systematicnon-responsebias inthesamplerespondents, theempirical ndings maybelittlebiased for not reecting the population distribution of gender. Next, there areapproximately1015%oftaxpayersinadoptingon-linetax.Obviously,theon-linetax is still at the early stage of adoption. Denitely, this research is greatly necessaryfor us to gain more insight on further promoting its widespread usage. This imposesa limitation of generalizability to the population. However, the same respondents arerandomly selected fromthe sample frame and thus, in a position to be wellrepresentative of the population. As a result, the empirical ndings should be free forthepopulationproblemandcanbewidelygeneralizedforitspracticaluse.ARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 801ARTICLEINPRESSAppendixA. QuestionnairePart1.Basicinformation1.Gender: &Female &Male2.Age: &Lessthan20yearsold &2030yearsold &3040yearsold&4050yearsold &Largerthan50yearsold3.Education: &Highschool &College &Graduateschool &Doctorate4.Occupation: &Finance &Institution &Information &Service &Manufacturing&Other5.Experienceinusingon-lineincometaxdeclaration: &One-timeuser &ContinueduserPart24.ConstituentconstructsinhypotheticresearchmodelScaledesignforthefollowingquestionnaire:1:Stronglydisagree(SD) 2:Moderatelydisagree 3:Somewhatdisagree4:Neutral(N) 5:Somewhatagree 6:Moderatelyagree7:Stronglyagree(SA)Note:OITD:abbreviationofon-lineincometaxdeclaration.I.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808802ARTICLEINPRESSSD N SAPerceivedusefulness(adaptedfromVenkateshandDavis,1996,2000)PU1 UsingtheOITDwouldimprovemyperformanceinincometaxdeclaration.1 2 3 4 5 6 7PU2 UsingtheOITDwouldimprovemyproductivityinincometaxdeclaration.1 2 3 4 5 6 7PU3 UsingtheOITDwouldenhancemyeffectivenessinincometaxdeclaration.1 2 3 4 5 6 7PU4 IndtheOITDtobeusefulinincometaxdeclaration. 1 2 3 4 5 6 7Easeofuse(adaptedfromVenkateshandDavis,1996,2000)EOU1 MyinteractionwiththeOITDisclearandunderstandable. 1 2 3 4 5 6 7EOU2 InteractionwiththeOITDdoesnotrequirealotofmentaleffort. 1 2 3 4 5 6 7EOU3 ItiseasytogettheOITDtodowhatIwantittodo. 1 2 3 4 5 6 7EOU4 ItiseasytousetheOITD. 1 2 3 4 5 6 7Attitude(adaptedfromBhattacherjee,2000)ATT1 UsingOITDforincometaxdeclarationwouldbeagoodidea. 1 2 3 4 5 6 7ATT2 UsingOITDforincometaxdeclarationwouldbeawiseidea. 1 2 3 4 5 6 7ATT3 IliketheideaofusingOITDforincometaxdeclaration. 1 2 3 4 5 6 7ATT4 UsingOITDforincometaxdeclarationwouldbeapleasantexperience.1 2 3 4 5 6 7Subjectivenorm(adaptedfromTaylorandTodd,1995;Bhattacherjee,2000)SN1 PeoplewhoareimportanttomewouldthinkthatIshoulduseOITD.1 2 3 4 5 6 7SN2 PeoplewhoinuencemewouldthinkthatIshoulduseOITD. 1 2 3 4 5 6 7SN3 PeoplewhoseopinionsarevaluedtomewouldpreferthatIshoulduseOITD.1 2 3 4 5 6 7I.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808803ARTICLEINPRESSBehavioralcontrol(adaptedfromTaylorandTodd,1995;Bhattacherjee,2000)PBC1 IwouldbeabletousetheOITDwellforincometaxdeclaration. 1 2 3 4 5 6 7PBC2 UsingOITDwasentirelywithinmycontrol. 1 2 3 4 5 6 7PBC3 Ihadtheresources,knowledge,andabilitytouseOITD. 1 2 3 4 5 6 7Intentiontouse(adaptedfromVenkateshandDavis,1996,2000)INT1 AssumingIhaveaccesstotheOITD,Iintendtouseit. 1 2 3 4 5 6 7INT2 GiventhatIhaveaccesstotheOITD,IpredictthatIwoulduseit. 1 2 3 4 5 6 7INT3 IfIhaveaccesstotheOITD,Iwanttouseitasmuchaspossible. 1 2 3 4 5 6 7Trust(adaptedfromGefenetal.,2003a)TST1 BasedonmyperceptionwithOITD,Iknowitispredictablefortheservice.1 2 3 4 5 6 7TST2 BasedonmyperceptionwithOITD,Ibelieveitprovidesgoodservice.1 2 3 4 5 6 7TST3 Based on my perception with OITD, I believe it helps or cares citizensintaxdeclaration.1 2 3 4 5 6 7I.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808804ARTICLEINPRESSAppendixB. CovariancematrixPU1 PU2 PU3 PU4 EOU1EOU2EOU3EOU4ATT1ATT2ATT3ATT4SN1 SN2 SN3 PBC1PBC2PBC3INT1INT2INT3TST1TST2TST3PU1 0.943PU2 0.8921.392PU3 0.8190.9621.056PU4 0.7010.73 0.7480.804EOU10.4120.4760.4560.4611.314EOU20.4510.5160.49 0.4881.213 1.332EOU30.44 0.5270.49 0.4741.204 1.228 1.339EOU40.4690.5230.5070.49 1.169 1.229 1.228 1.397ATT1 0.4830.5160.5190.4890.432 0.469 0.472 0.487 0.845ATT2 0.4080.4130.4380.4280.402 0.415 0.425 0.436 0.679 0.892ATT3 0.4490.4720.4870.4680.417 0.441 0.463 0.475 0.662 0.611 0.805ATT4 0.5350.5950.5780.5640.667 0.711 0.698 0.766 0.737 0.627 0.731 1.189SN1 0.32 0.3260.3540.3050.298 0.319 0.305 0.33 0.401 0.302 0.38 0.576 1.747SN2 0.2170.2430.26 0.2010.192 0.204 0.19 0.245 0.243 0.115 0.204 0.392 1.3592.152SN3 0.2530.2810.2940.2210.211 0.22 0.215 0.271 0.262 0.148 0.218 0.404 1.3861.7991.909PBC1 0.4160.46 0.4460.44 0.629 0.648 0.683 0.666 0.562 0.511 0.557 0.745 0.3380.2020.2170.959PBC2 0.4340.4890.4710.4570.717 0.723 0.757 0.763 0.582 0.531 0.577 0.827 0.3880.2550.2560.937 1.228PBC3 0.3830.4310.4170.4160.635 0.619 0.663 0.641 0.534 0.497 0.541 0.689 0.2690.1110.13 0.836 0.929 0.969INT1 0.5130.5560.5460.5210.535 0.561 0.578 0.582 0.656 0.582 0.655 0.794 0.4380.2880.3230.701 0.734 0.679 1.046INT2 0.5060.5480.5460.5130.514 0.547 0.572 0.569 0.666 0.581 0.659 0.791 0.4420.2930.3220.696 0.718 0.659 1.002 1.05INT3 0.5120.5540.5450.5250.513 0.551 0.568 0.56 0.672 0.582 0.663 0.789 0.4270.2880.3090.69 0.703 0.653 0.977 0.997 1.029TST1 0.4390.5030.4970.4310.542 0.552 0.559 0.604 0.549 0.467 0.51 0.73 0.4540.4140.4070.539 0.671 0.514 0.621 0.632 0.612 1.764TST2 0.4410.4950.4840.4450.527 0.543 0.555 0.584 0.593 0.529 0.551 0.745 0.4330.3540.3540.571 0.679 0.547 0.641 0.65 0.634 1.286 1.366TST3 0.4390.4940.4760.4340.509 0.535 0.544 0.573 0.601 0.525 0.552 0.754 0.4420.3460.3530.578 0.678 0.554 0.645 0.66 0.643 1.243 1.291 1.378I.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808805ReferencesAjzen,I.,1991.Thetheoryofplannedbehavior.OrganizationalBehaviorandHumanDecisionProcess50,179211.Ajzen, I., 2002. 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He gained a Bachelor in Industrial Management fromNational Cheng-KungUniversity,anM.S.inComputerSciencefromMontclairStateUniversity,andaPh.D.inManagementfrom Rutgers, the State University of New Jersey. He has published a number of papers in Information &Management, Decision Support Systems, Behavior and Information Technology, Psychometrika, AppliedPsychologicalMeasurement,andJournalofEducationalandBehavioralStatistics.Hiscurrentresearchinterestsareintheareasofe-commerce,customerrelationshipmanagement,supplychainmanagement,strategicinformationsystems,andbusinessprocessreengineering.ARTICLEINPRESSI.-L.Wu,J.-L.Chen/Int.J.Human-ComputerStudies62(2005)784808 808