Customers’ Perceived Risk and Trust in Using Mobile Money ... ·...
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DOI: 10.4018/IJEBR.2019010101
International Journal of E-Business ResearchVolume 15 • Issue 1 • January-March 2019
Copyright©2019,IGIGlobal.CopyingordistributinginprintorelectronicformswithoutwrittenpermissionofIGIGlobalisprohibited.
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Customers’ Perceived Risk and Trust in Using Mobile Money Services—an Empirical Study of GhanaIbn Kailan Abdul-Hamid, University of Professional Studies, Accra, Ghana
Aijaz A. Shaikh, University of Jyväskylä, Jyväskylä, Finland
Henry Boateng, University of Technology Sydney, Ultimo, Australia
Robert E. Hinson, University of Ghana and University of the Free State Business School, Accra, Ghana
ABSTRACT
Althoughmobilemoney(MM)hasbeenexploredintheexistingliterature,therolesoftrustandriskinMMusehavereceivedlittleattentionfromresearchers.Furthermore,manyoftheexistingstudieshavetreatedtheseconstructsasunidimensional.TheextantliteraturealsoshowsthatperceivedriskhasreceivedlittleattentionfromresearcherswhohaveconductedstudiesonMM.Thus,theobjectiveofthisstudyistoexaminetherolesoftrustandperceivedriskincustomers’intenttoadoptMMservicesinGhana.Bothtrustandperceivedriskwerebrokendownintovariousdimensions.Thestudyinvolved671respondentswhowereselectedviaaninterceptapproachandanonlinesurvey.Structuralequationmodellingwasusedtotestthestudy’shypotheses.Thefindingsshowthatperceivedriskisnegativelyassociatedwithcustomers’intenttouseMMservicesandeconomy-basedtrust.Trustinserviceprovidersandeconomy-basedtrustarepositivelyassociatedwithcustomers’intenttouseMMservices.Theimplicationsofthefindingsareprovidedinthelatterpartofthisstudy.
KEywoRdSGhana, Mobile Money, Mobile Networks, Mobile Payments, Perceived Risk, Trust
INTRodUCTIoN
Mobilemoney(MM),alsoreferredtoasbranchlessbanking,isabankingandpaymenttoolthatusesportabledevices toallowindividuals toaccessandcomplete financial transactions,suchasfundstransfersandpaymentofutilitybills.Inlightoftheexponentialgrowthofcellphonecoverageandusage(Gichuki&Mulu-Mutuku,2018),MMservicesareseenasalikelysolutiontofinancialexclusionon theAfricancontinent(Tchouassi,2012), including inGhana. IncreasingcellphonediffusioninGhanahasledtoaremarkablegrowthintheadoptionandusageofMMservices.Forexample,in2015,therewere13.1millionusersofMMservices;by2017,thisfigurehadjumpedto23.95million—agrowthrateofmorethan83%intwoyears(www.bog.gov.gh).However,whilethisincreaseintheusageofMMserviceslookspromising,thetotalpopulationinGhanaaccessingandusingtheMMservicesnowstandsatabout50%.Theremaining50%oftheGhanaianpopulationisstilloutsidetheMMservicesnet;therefore,theindustry,regulatorsandacademicsneedtodetermine
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howtoincreaseMMusageandfinancialinclusionfortheremotelylocatedandpreviouslyunbankedsegmentofthepopulation.
OnlinetransactionsareassociatedwithcustomerconcernsaboutconsumertrustandperceptionsofrisklinkedtoremotelyexecutedpaymentservicessuchasMM(PWC,2016).Priorresearchers(e.g.,Yangetal.,2015;Ratnasingametal.,2005)haveconsideredthefactors‘trust’and‘risk’ascentraltotheadoptionanduseofelectronicpaymentsystemsandelectroniccommerce.Someresearchers(e.g.,Imetal.,2008)havealsonotedthatthetechnologyadoptionandusageliteraturehasoftenneglectedtheroleofperceivedriskintechnologyuse,andthereisaneedtofurtherinvestigatethisissueinrelationtoMMuse.ThefewexistingstudiesinGhanatreatedtrustandriskasunidimensionalconstructs(seeforexampleTobbin&Kuwornu,2011),buttheexistingliterature(e.g.,Balletal.,2016)arguedthat,trustandriskshouldbeconsideredasmultidimensionalconstructs.Moreover,theassociationbetweenriskandtrustissuesandMMserviceuseintentinGhanahasrarelybeenexamined.Againstthisbackdrop,thisstudyfirstcontributestotheexistingliteratureonMMuseintentinadevelopingcountrycontextbytreatingtrustandriskasmultidimensionalconstructs.Second,itexamineshowperceivedriskinfluencestheconsumer’sintenttouseMMservices.Third,itexploreseconomy-basedtrustandintenttousetheMMservicenexus.Fourth,itexaminestrustintheMMserviceproviderandintenttoengageinaMMservicerelationship.
Therestofthepaperisorganizedasfollows.Afterareviewofthetheoreticalbackgroundandexistingliterature,theresearchmodelandstudyhypothesesareelaborated,followedbyanaccountof samplingdesign,measures, descriptive statistics,modelmeasurement and structural equationmodelling.Thepaperconcludeswithimplications,limitationsanddirectionsforfutureresearch.
THEoRETICAL BACKGRoUNd ANd LITERATURE REVIEw
MM ServicesMMinvolvestheuseofmobilephonestoaccessfinancialservicesremotelythathavetypicallybeenprovidedoveracounter(Mothobi&Grzybowski,2017).MMserviceslargelyincludetransfersandpayments.MMisalsoreferredtoascellphonebanking,over-the-counterbanking,agentbankingandbranchlessbanking(Dinizetal.,2012).
Theweaknessesoftraditionalandotherelectronicbankingchannels(ATMsandonlinebanking)infulfillingcustomers’financialneedsandrequirementsduetolimitedavailability,weekinternetconnectivityandotheraccessissuesmaybethereasonfortheincreasingpopularityofMMservices.
MMservicesareanovelwayofaccessingandconductinglow-costfinancialtransactions.Unlikeotherdigitalbankingchannels,MMservicesoperateattheintersectionoffinanceandtelecomandinvolveadiversesetofstakeholderswithcompetingplayersfromvariousfields(Donovan,2012).Amongthesestakeholdersoractors,MMagents(individualsorfirmswhooperatemobilemoneyservices)areatthefrontlineofMMservicedeploymentandplayadecisiveroleinthesuccessofMMservicesinGhanaandelsewhere.
Trust and Risk in digital ServicesItiswidelybelieved(e.g.,Safeenaetal.,2018)that‘trust’and‘risk’occupysignificantpositionsin e-business services (including e-commerce). This is perhaps because of the uncertainty andriskinvolvedinonlinetransactionsconductedremotelyusingportabledevices,whicharepronetohacking,lossandmisuse(Shaikhetal.,2018).Trustreflectsawillingnesstobevulnerablebasedonthepositiveexpectationofanotherparty’sfuturebehaviour(Kimetal.,2018).WithregardtoMMservicesinadevelopingcountry,trustreferstotheconsumer’sbeliefthatMMserviceswillbecost-andtime-effectiveandthatserviceproviderswillsafeguardtheirclients’moneyandinformation.Asathird-partyprovider,suchasanagent,normallydeliversMMservices,theroleoftrustinconsumerintenttouseMMservicesrelatesdirectlytothecustomer’strustinthatagent.
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Trust iscrucial ineconomic transactionsbecause itmayreduce the riskof fallingvictimtoopportunisticbehaviour,andlackoftrustisamongthemainreasonsconsumersdecidenottoengagewithMMandothersimilarservices(Chauhan,2015).Scholars(e.g.,Balletal.,2016;Gefenetal.,2003)havesuggestedthattrustisamultidimensionalconcept.Trustisassociatedwithperceivedrisk,anditcanbearguedthatlackoftrustincreasesperceivedrisk(Kim&Peterson,2017).Inthemobilebankingcontext,riskorperceivedriskisdefinedastheexposuretodangerthatarisesfromthepurchaseofabankingproductorservice,suchasm-banking(Shaikhetal.,2018).Inthisstudy,wehavedefinedperceivedriskastheamountofriskanunderbankedconsumerseeswhenusingMMservicesinadevelopingcountry.Asriskcanbedifficulttomeasureobjectively,theliteraturefocusesonusers’riskperceptions.Whentheperceivedriskinanytransactionishigh,consumersmaydecidenottoengageinthattransactionormayterminatetheongoingexchangerelationship(Al-Gahtani,2011).Inthepresentcontext,then,itcanbearguedthatperceivedriskmayincreaseconsumers’unwillingnesstouseMMservices.
Technology Adoption and Usage ModelsManystudieshavedeployedtheoriestoexplaintechnologyutilization;prominentamongthesearetheunifiedtheoryofacceptanceanduseoftechnology(UTAUT)(Venkateshetal.,2003),diffusionofinnovationtheory(Rogers,1995),thetheoryofplannedbehaviour(Ajzen,1991),thetechnologyacceptancemodel(TAM)(Davisetal.,1989)andthetheoryofreasonedaction(Fishbein&Ajzen,1975).Applicationofthesetheorieshasyieldedvaryingresults(Venkateshetal.,2003).Forexample,Rogers’sdiffusionofinnovationtheoryidentifiesfiveessentialfeaturesthatdriveuseradoptionofaninnovation:complexity,relativeadvantage;trialability,observabilityandcompatibility(Rogers,1995). The theories of planned behaviour and reasoned action (Ajzen, 1991) have shown thattechnologyadoptioncanbedeterminedbyperceivedbehaviouralcontrol,aswellasbysubjectivenormsandattitudes.
Davisetal.(1989)developedtheTAMtoexplainuseradoption.Davisetal.(1989)notedthattheperceivedusefulnessandperceivedeaseofuseofagiventechnologydetermineuserattitudestowardit,andanumberofotherstudieshaveprovidedempiricalsupportforthisview.Forexample,TaylorandTodd(1995)reportedthatusersexhibitpositiveattitudeswhentheyperceivethatatechnologyiseasytouse.FollowingtheTAMmodel,researchershaveidentifiedmanyotherfactorsthatdriveuseradoptionoftechnology.Venkateshetal.(2003)attemptedtointegrateallofthesefactorsintheir UTAUT framework, which integrates performance expectancy and effort expectancy withperceivedusefulnessandeaseofuse.Thereisevidencethatthismodelisrobusttotheextentthatithasbeenfoundtoexplain70%ofthevarianceintechnologyusageintentions(Wuetal.,2008).However,thesedominantmodelsrarelyaddressissuesoftrustandriskthatareofconcerntomanyusersoftechnology-basedservices,especiallyinthefinancialservicessector(Yangetal.,2015).Thepresentstudyseekstobridgethatgapbyexaminingcustomerintenttousemobileservicesintermsofcustomertrustandperceivedrisk.
Research Model and Hypothesis developmentThe various dimensions of risk seem likely to differ in their influence, contributing to varyingdegreestotheeffectofoverallperceivedriskonintenttouseMMservices.Asconceptualizedhere,perceivedriskisunderstoodtoinvolveconcernsabouteconomic,functional,security,privacy,time,service,psychologicalandsocialrisk.ApositiveassociationishypothesizedtoexistbetweentrustandconsumerintenttouseMM,twocomponentsoftrust:economy-basedandserviceprovider-based.Asperceivedriskislikelytoreducetrustinmobile-basedpayments(Yangetal.,2015),itisfurtherhypothesizedthatperceivedriskisnegativelyassociatedwithbotheconomy-basedtrustandserviceprovider-basedtrust.Figure1depictstheresearchmodeldeveloped.
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TRUST, PERCEIVEd RISK ANd INTENT To USE MM SERVICES
Trustandperceivedriskareprevalentconcepts that influencebehavioural intentions touseMMservices.Themanagementofmoney(physicalorelectronic)isexpectedtoproducesomebenefitstoitsowners.Accordingly,theuseofMMservicesisexpectedtodeliverbenefitstoaswellascreatefearamonguserswhoarelargelyeitherunderbankedorunbanked.Forexample,auser’sperceptionthattheuseofaMMservicemayexposehimorhertothelossofmoneycouldinfluencetheadoptionofMMservices.MMusersmaynotbeabletopredicttheoutcomeofsuchdecisionsaccurately,andthoseoutcomesmayproveundesirable(Yangetal.,2015).Moreover,becauseofthenatureofMMtransactions,theyusuallylacktheassuranceprovidedintraditionalsettings,suchasintheformofreceipts,makingitmoredifficultforconsumerstoseekcompensationwhentransactionerrorsoccur.Basedonthesearguments,weproposethefollowinghypothesis:
H1a:EconomicriskinfluencestheperceivedriskofMMservices.
Afunctionalriskisaprobabilitythataservicemaynotbeperformedasexpected(Yangetal.,2015).Thus,functionalriskrelatestotransaction-specificuncertaintyaboutMMservices(Yousafzaietal.,2003;Martinsetal.,2014).Aconsumer’sperceptionoffunctionalriskissignificant,asitaffectsthedecisiontouseMMservices.Accordingly,aconsumer’sconcernaboutthestabilityandreliabilityofanMMserviceinfluencesthedegreeofperceivedrisk(Yangetal.,2015).Thus,weproposethefollowinghypothesis:
H1b:FunctionalriskinfluencestheperceivedriskofMMservices.
Securityriskrelatestothreatsthatproducesituations,conditionsoreventswiththepotentialtocauseeconomichardshiptodataornetworkresourcesintheformofdamage,leaks,alterationofdata,denialofservice,fraud,wasteorabuse(Nofer,2015).MMcustomersmayfearidentitytheft,asthereisevidencethatcustomersusingelectronicplatformsbelievetheyarehighlyvulnerabletosuchtheft(Hilleetal.,2015).Inaddition,perceivedthreatssuchasnetworkanddatatransactionattacksandunauthorizedaccesstoanaccountbymeansoffalseordefectiveauthenticationcreatesecurityissues(Nofer,2015).SomestudieshavepredictedthatthegreatestchallengeforMMservicesinwinningcustomers’trustisinrelationtosecurityissues(Jang-Jaccard&Nepal,2014).Weproposethefollowinghypothesis:
H1c:SecurityriskinfluencestheperceivedriskofMMservices.
Perceivedprivacyreferstoaconsumer’spreferencetocontrolwhoispresentduringamarkettransactionandthediffusionofinformationassociatedwithsuchtransactionstopersonswhowerenotpresent(Ortizetal.,2018).MMcustomersmaybeconcernedthatathirdpartycouldstealtheirinformation (personal or transactional) during MM transactions. Perceived privacy has becomeespeciallycriticalsincecustomerscannowlinktheirMMaccountstotheirbankaccounts.Theseformsofriskareknowntohavenegativelyimpactedelectronicbankingservices(SumChau&Ngai,2010)andarethereforelikelytohaveanegativeeffectonintenttouseMMservices.SomestudieshavepredictedthatthegreatestchallengeforMMserviceswillbewinningcustomers’trustinrelationtoprivacyissues(Aboobucker,&Bao,2018).Basedontheabove,weproposethefollowinghypothesis:
H1d:PrivacyriskinfluencestheperceivedriskofMMservices.
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OnebenefitofMMisthatcompletingatransactionrequireslittletime(Omwansaetal.,2013).However,manypotentialusersofMMservicesmayalsoperceivethisbenefitasarisk.AccordingtoYangetal.(2015),timerisk—thatis,inconvenienceandlossoftimeduetodelaysinreceivingapaymentortransferringmoney—isnegativelyassociatedwithmobilepaymenttransactions.Thisform of risk may be a consequence of functional risk. Network failure is very common amongtelecommunicationsfirmsinGhana,whichcancauseunduedelaysinsendingandreceivingmoney.Consequently,consumerswhoareverytime-orientedandconcernedabouttheriskof‘wastingtime’arelesslikelytouseMMservices(Featherman&Pavlou,2003).Thus,weproposethefollowinghypothesis:
H1e:TimeriskinfluencestheperceivedriskofMMservices.
MMusersmayviewMMservicesasriskyforanumberofreasons.Forexample,apotentialusermaybeuncertainwhetherornotaservicewillbeperformedasexpected,whichcouldcreatefinancial,functionalandevenphysicalrisk(Carnevaleetal.,2018).Accordingly,perceivedservicerisknegativelyaffectsconsumers’perceivedservicevalueandthuslowerspurchaselikelihood(Chang&Hsiao,2008;Carnevaleetal.,2018).Weproposethefollowinghypothesis:
H1f:ServiceriskinfluencestheperceivedriskofMMservices.
Psychologicalriskreferstothechancethatauserwillexperiencenegativeemotionslikefear,frustration, pressure or anxiety from the use of MM services (Ortiz et al., 2018). The potentialexperienceoffearandanxietyinsendingorreceivingmoneymayaffectusers’decisionstouseMMservices.
H1g:PsychologicalriskinfluencestheperceivedriskofMMservices.
AfurtherrelevantdimensionthatmayaffectintenttouseMMservicesissocialrisk.SocialriskreferstothepossibilitythattheuseofMMmaypromptdisapprovalfromone’sfriends,familyorworkgroup.DependingonhowMMisviewed,usingitmayenhanceordiminishone’ssocialstanding.Itmaybethatpeople’sperceptionsofMMaffecttheirviewsofitsusers,ornotusingMMmayhavecertainsocialconnotations.Inbothattitudinalandtechnologyacceptanceresearch,this‘referencegroupeffect’isknowntoplayasignificantroleriskperception(Lee&Lemyre,2009).Itfollowsthatperceivedsocialrisksmayaffectcustomers’willingnesstouseMMservices.Weproposethefollowinghypothesis:
H1h:SocialriskinfluencestheperceivedriskofMMservices.
Trustinvolvesapositiveexpectationregardingservicesinriskysituations(Das&Teng,2012).Aneconomy-basedtrustisaformoftrustbasedoneconomicbenefits(Chai&Kim,2010).Theperceptions of MM users about the time- and cost-saving benefits and economical use of MMservicesrelatetotheireconomy-basedtrustinsuchservices(Chai&Kim,2010).WhencustomersbecomeconsciousofMMservicechargesorcoststhatexceedtheirgainfromtheMMservicetheyturntolosetrustintheservice.Consequently,perceivedriskofusingMMservicescouldaffecttheeconomy-basedtrust.Weproposethefollowinghypothesis:
H2:Perceivedriskinfluenceseconomy-basedtrust.
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PerceivedriskmayberegardedasuncertaintyaboutthepossiblenegativeeffectsofusingMMservices.Accordingly,perceivedriskisablendofuncertaintyandtheseriousnessofMMserviceoutcomes(Featherman&Pavlou,2003).BecausetheoutcomesofMMservicedecisionscanbeuncertain,theperceivedriskofMMservicesmaynegativelyinfluencecustomers’intenttouseMMservices(Lopez-Nicolas&Molina-Castillo,2008).Similarly,Parketal.(2005)foundanegativerelationshipbetweenperceivedriskandintenttopurchaseonline.GiventhelackofcustomertrustinmobilepaymentsystemsandtheperceivedriskassociatedwithMMsystems(Yangetal.,2015),itislogicaltoarguethatperceivedriskinfluencesconsumerintenttouseMMservices.Riskisalsoassociatedwithtrust(Pavlouetal.,2007)andservestounderminecustomertrustinmobilepaymentsystems(Yangetal.,2015).MMisefficientandcost-effectiveincomparisontomanyotherformsofbankorpaymentservices,butperceivedriskmaystillaffectaconsumer’sintenttouseMMservices(Baueretal.,2005).Therefore,wehypothesizethat:
H3:Perceivedriskinfluencesthecustomer’sintenttouseMMservices.
Perceivedriskandtrustareknowntoinfluenceconsumerbehaviouralintentions(Martinsetal.,2014;Namahoot&Laohavichien,2018).Thus,aconsumer’sperceptionofriskinfluenceshisorherbehaviourtowardMMservices(Baueretal.,2005).CustomersofMMservicesbelievethatMMservicespossessseveralriskelements(timerisk,securityrisk,privacyrisk,etc.)(Namahoot&Laohavichien,2018).ConsumersofMMservicesmayattempttominimizeperceivedriskwhilemaximizing theservices’utility(Baueretal.,2005).Similarly,Liebana-Cabanillasetal. (2013),foundthatperceivedriskhasapositiveinfluenceontrustinMMservices.Thus,weproposethefollowinghypothesis:
H4:PerceivedriskinfluencestrustinMMserviceproviders.
TrustplaysacentralroleinMMutilization(Tobbin&Kuwornu,2011).Forpresentpurposes,trust is understood to relate to both economic and service-provider factors, essential aspects ofcustomerdecision-makinginanelectronicenvironment(Chai&Kim,2010).Assuch,trustaffectstherelationshipbetweenriskandMMserviceuseintent.Sincetrustisrelatedtopositivecustomerattitude(Song&Zahedi,2002),itislikelytofavourablyinfluenceMMtransactionintent;conversely,lackoftrustislikelytoadverselyaffectusageofMMservices.AccordingtoGefenetal.(2003),customersmakerationalassessments inelectronicenvironments toensure that theyobtainvaluefortheirmoney.Thereisalsoevidencethatcustomersdonotwanttowastetimeintheirbusinesstransactionsandthereforefavourelectroniccommerce,whichreducestransactiontime(Kuismaetal.,2007).Astudyby,ChaiandKim(2010)showsthateconomy-basedtrustinfluencesconsumerbehaviour,consequently,wealsoarguethataconsumer’slevelofeconomictrustislikelytoaffectconsumerbehaviourinrelationshiptoMMservicesusage.Ascustomersprefertodealwithvendorstheycantrust(Pavlouetal.,2007),itseemslikelythattrustinMMserviceproviders(telecoms,banksandagents)willaffectcustomerintenttouseMMservices.Onthatbasis,weproposethefollowinghypotheses:
H5:Economy-basedtrustinfluencescustomerintenttouseMMservices.H6:TrustinMMserviceprovidersinfluencescustomerintenttouseMMservices.
MeasuresAlltheitemsusedformeasuringtheconstructswereadaptedfromtheexistingliterature.Specifically,allitemsoncustomers’intenttouseMMserviceswereadaptedfromYangetal.(2015).Similarly,itemsmeasuring thedimensionsofperceivedrisk (economic, functional, security,privacy, time,
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service,psychologicalandsocialrisk)wereadaptedfromYangetal.(2015)andfromFeathermanandPavlou(2003).Theitemsmeasuringeconomy-basedtrustandtrustintheserviceproviderwereadaptedfromChaiandKim(2010).Forclarity,allitemsweremeasuredonafive-pointLikertscale(1=stronglydisagree,2=disagree,3=neutral,4=agreeand5=stronglyagree).Becausealloftheitemsmeasuringperceivedriskwerenegativeinform,codingwasreversedduringtheanalysis.However,codingforservice-providertrustandeconomy-basedtrustandintenttouseMMremainedthesamebecausetheitemswerepositiveinform.Thequestionnaireitemsareattachedasanappendix.
RESEARCH METHodoLoGy
Totestthemodelandthehypotheses,weusedasurvey-basedresearchdesigntocollectquantitativedatafromalargenumberofrespondents.ThequestionnairedevelopedonthebasisoftheproposedmodelwasproducedinEnglishandwasdistributedonlytorespondentswhocouldreadandunderstanditsufficientlywelltorespond.Thequestionnairewaspre-testedinJanuaryandFebruary2017withtheassistanceofgraduatestudentsfromanexecutivemasterofbusinessadministrationcourseandamasterofaphilosophycourseattheUniversityofGhanaBusinessSchool.Arevisedversionofthedatacollectioninstrumentwasproducedbasedoninsightsfromthepre-testing.ThatrevisedversionwasusedtocollectthestudydataacrossGhanafromApriltoJune2017.ThetargetrespondentswereusersofMMservices—forexample,peoplewhoeithertransferorreceivemoneyusingtheseservices.TherespondentsweredrawnfromalltenregionsofGhana(seeTable1).Tospeedupdatacollectionandtocapturealargerportionofthetargetpopulation,graduateandundergraduatestudentsweretrainedasresearchassistants.
Datacollectioninvolvedtwocomponents.First,theinterceptapproachwasusedtocollectdatafromrespondentsintheGreaterAccraregion.TheresearchassistantsinterceptedsomepotentialrespondentswhiletheywereusingMMservices;otherswerecapturedafterusingsuchservices.Thisapproachtodatacollectionhasbeenusedasameansofimprovingresponserates(e.g.,Boatengetal.,2016).Thesecondcomponentofdatacollectionwasanonlinesurvey,whichwasusedtocapturerespondentsfromtheremainingnineregions.AlthoughlimitedaccesstotheInternetandInternetliteracymadeitchallengingtocapturedifferenttypesofrespondents,theinterceptapproachhelpedtoaddressthisissue,giventhecosmopolitannatureoftheGreaterAccraregion.Alloftheresponseswerethencombinedandanalysedtogether.
Inall,therewere671usablequestionnairesforthefinalanalysis.Amajorityofrespondents(54.2%)weremale,andtheremaining45.8%werefemale.Mostoftherespondents(83.5%)rangedinagefrom18to27years,andmost(71.7%)hadcompletedauniversitydegree.Overall,48.7%usedtheMMservicesofMTN,andmost(74.7%)usedMMservicesbetweenonetofivetimesamonth.Additionally,mostoftherespondents(93.9%)wereChristians.ThereweremorerespondentsfromtheGreaterAccraregion(33.7%)thanfromotherregions.Asmostoftherespondentswereunwillingtodisclosetheiraveragemonthlyincome,thisinformationwasnotcapturedinthefinalanalysis.Table1includesdetailsofalldemographicdata.
Wecheckedforcommonmethodvariance(CMV)beforetestingourhypothesesusingHarman’sone-testfactor(Podsakoffetal.2003).Thiswasdonebyenteringallthevariablesintoanexploratoryfactoranalysis(EFA).FromthefindingsoftheEFA,morethanonefactoremergesfromafactoranalysis.Additionally,whenallourmeasurementitemswereforcedtoloadontoasinglefactor,itdidnotaccountfor50%ofthevariance.Consequently,therewasnoseriousconcernsofCMV.
ResultsWeusedthepartialleastsquaresstructuralequationmodelling(PLS-SEM)approachtoassessourmeasurementmodelandthehypothesesof thisstudybyrunningSmartPLS3.0(Ringle,Wende,&Becker, 2015).PLS is appropriate for testinghierarchical latent variablemodels, hierarchicalcomponentmodelsorhigher-orderconstructs.Comparedtocovariance-basedstructuralequation
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modelling, PLS-SEM is designed to handle observable lower-order components (LOCs) andunobservablehigherordercomponents(HOCs)toreducemodelcomplexityandthismakesitmoretheoreticallyparsimonious(Lohmöller,1989).
Table 1. Demographic characteristics
Details Frequency Percent
Gender Male 364 54.2
Female 307 45.8
Educationallevel HND 31 4.6
Degree 481 71.7
Postgraduate 52 7.7
Other 107 15.9
Age 18-27years 560 83.5
28-37years 57 8.5
38-37years 27 4.0
48-57years 21 3.1
58yearsandabove 6 .9
Network MTN 327 48.7
AirtelTigo 202 30.1
Vodafone 142 21.1
ThefrequencyofUse(Month)
1-5times 501 74.7
6-10times 97 14.5
11-15times 39 5.8
16-20times 12 1.8
morethan20times 22 3.3
Religion Christian 630 93.9
Moslem 36 5.4
Region GreaterAccra 226 33.7
CentralRegion 83 12.4
EasternRegion 117 17.4
WesternRegion 23 3.4
VoltaRegion 87 13.0
AshantiRegion 79 11.8
Brong-AhafoRegion 19 2.8
NorthernRegion 15 2.2
UpperEastRegion 15 2.2
UpperWestRegion 7 1.0
Total 671 100.0
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Measurement Model AssessmentTheassessmentofthemeasurementmodelsisthefirststepinanySEMprocess,asitensuresthatstatements(unobservedvariables)areactuallymeasuredconstructs(observedvariables).Weassessedourmeasurementmodelusingthreemaincriteria:convergentvalidity,reliabilityanddiscriminantvalidity,followingthesuggestionofHairetal.(2014).
Convergentvalidityoftheitemswasassessedbyouterloadingsandaveragevarianceextracted(AVE).Allouterloadingsforitemsareabove0.6,whichisabovetheminimumthresholdvalueof0.6BagozziandYi(1988)suggested.Theouterloadingstherefore,providedthesupportforconvergentvalidity(seeHairetal.,2014).TheAVEvaluesof0.599to0.814arewellabovetheminimumrequiredlevelof0.50,asFornellandLarcker(1981)suggested,thusalsodemonstratingtheconvergentvalidityforallconstructs.Compositereliability(CR)measureswereallhigherthanthe0.7thresholds(Table1)ofChin(2010).Thatis,amongmeasurementmodels,thereliabilitymeasureofconstructsinthisstudywereabovetheacceptablesatisfactorylevels(seeTable2).
DiscriminantvaliditywasevaluatedbasedontheFornell-Lackercriterionandcross-loadings.TheFornell-LarckercriterionFornellandLarcker(1981)suggestedstatesthatthesquarerootoftheAVEshouldbegreaterthanthecorrelationsharedbetweentheconstructandtheotherconstructs(seeTable3).
Test of the Hierarchical Factor StructureThepresentstudyviewsperceivedriskasasecond-orderfactor.PLSwas,therefore,suitableforthisanalysis;perceivedriskwastreatedasanHOCwithitsLOCseconomicrisk,functionalrisk,privacyrisk,psychologicalrisk,securityrisk,serviceriskandsocialrisk.Thefindingsshowthatthesecond-orderandfirst-orderfactorloadingsarehighandstatisticallysignificant.Economicrisk(γ=.676;p<.001),functionalrisk(γ=.642;p<.001),privacyrisk(γ=.737;p<.001),psychologicalrisk(γ=.620;p<.001),securityrisk(γ=.789;p<.001),servicerisk(γ=.601;p<.001)andsocialrisk(γ=.372;p<.001)exhibitedsignificantcoefficientsinsupportofH1a,H1b,H1c,H1d,H1e,H1f,H1gandH1h.Theseresultsprovidestrongsupportforthecustomerperceivedriskscaleasasecond-orderconstruct.
Structural Model AssessmentAftervalidatingthemeasurementmodel,weassessedourstructuralmodel,whichinvolvedtestingourhypothesis.Ofthefivehypotheses,one(1)wasnotsupported;therelationshipbetweenperceivedriskandeconomy-basedtrustwasnotsignificant(β=0.021;p=0.605).However,therelationshipbetweenperceivedriskandcustomerintent touseMMserviceswassignificant(β=0.065;p=0.016).Additionally, therewasa significant relationshipbetweenperceived riskand trust in theserviceprovider(β=0.159;p=0.000).Thestructuralpathsshowedasignificantrelationshipbetweeneconomy-basedtrustandcustomerintenttouseMMservices(β=0.441;p=0.000)andbetweentrustintheserviceproviderandcustomerintenttouseMMservices(β=0.266;p=0.000),asseeninTable4.Figure2showsthestructuralmodel.
Ascanbeobservedfromthemodel(Figure2andTable4),therewerethreeoutcomeconstructswiththefollowingR-squares(R2):customerintenttouseMMservices(.371),economy-basedtrust(.000)andtrustintheserviceprovider(.025).Theseresultsindicatethatperceivedriskdoesnotexplaineconomy-basedtrust.Also,thefindingsshowthatperceivedriskprovidesamoderateexplanation(37.1%)oftrustincustomerintenttouseMMservices.Howeverperceivedriskprovidesaweakexplanation(2.5%)ofthevarianceintrustintheserviceprovider.
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dISCUSSIoN ANd CoNCLUSIoNS
ThisstudyinvestigatedthevariouscomponentsoftrustandriskthataffectcustomerintenttouseMMservicesinGhana.Thestudyhadsixmainhypotheseswitheightsub-hypotheses.Thefindings
Table 2. Summary results for measurement model quality
Construct Items Loadings AVE CR
Economic risk 0.554 0.788
ERK1 0.707
ERK2 0.77
ERK3 0.755
Functional risk 0.627 0.834
FRK1 0.742
FRK2 0.814
FRK3 0.817
Security risk 0.643 0.878
SRK1 0.79
SRK2 0.85
SRK3 0.826
SRK4 0.737
Privacy risk 0.708 0.879
PRK1 0.852
PRK2 0.87
PRK3 0.801
Time risk 0.704 0.877
TMK1 0.816
TMK2 0.866
TMK3 0.834
Service risk 0.683 0.866
SK1 0.847
SK2 0.873
SK3 0.755
Psychological risk 0.625 0.833
PK1 0.75
PK2 0.798
PK3 0.822
Social risk 0.599 0.817
SKk1 0.845
SKk2 0.781
SKk3 0.689
Economy-based trust 0.726 0.888
ET1 0.838
ET2 0.85
ET3 0.867
Trust in service providers 0.744 0.897
TP1 0.853
TP2 0.896
TP3 0.837
Intentions to use mobile money services
0.814 0.929
IUM1 0.904
IUM2 0.928
IUM3 0.874
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suggestthatbothtrustandperceivedriskaremultidimensionalconstructs.However,theirimportanceincustomers’purchasedecision-makingvariesfromonecontexttoanother,andthevariousdimensionsoftrustandperceivedriskdonotcontributeequallytooveralltrustandperceivedrisk.TheresultsalsosuggestthatseveralotherformsoftrustandperceivedriskareassociatedwithMMservices;apartfromperceivedrisk,otherelementswithintheMMserviceenvironmentmayalsoreducecustomers’trustinMMserviceprovidersandintheeconomicsofMMservices.
HypothesisH1awassupported,implyingthateconomicriskpositivelyimpactsperceivedriskofMMservices.HypothesisH1bwasnotsupported,implyingthatfunctionalriskdoesnotpositivelyimpactperceivedrisk.OurstudysuggestedthatthefunctionalriskofusingMMservicesdoesnotaffectMMservices.HypothesisH1cwassupported,implyingthatsecurityriskpositivelyimpactstheperceivedriskofMMservices.HypothesisH1dwasnotsupported,implyingthatprivacyriskdoesnotpositivelyimpacttheperceivedriskofMMservices.HypothesisH1ewassupported,implyingthattimeriskpositivelyimpactstheperceivedriskofMMservices.HypothesisH1fwasnotsupported,implyingthatserviceriskdoesnotpositivelyimpacttheperceivedriskofMMservices.HypothesisH1gwasnotsupported,implyingthatpsychologicalriskdoesnotpositivelyimpacttheperceivedriskofMMservices.HypothesisH1hwassupported,implyingthatsocialriskpositivelyimpactstheperceivedriskofMMservices.
Table 3. Construct correlation matrix
1 2 3 4 5 6 7 8 9 10 11
1Economicrisk 0.745
2Economy-basetrust 0.051 0.852
3Functionalrisk 0.429 0.054 0.792
4Intentiontousemobilemoney 0.097 0.550 0.025 0.902
5Privacyrisk 0.433 0.043 0.39 0.036 0.842
6Psychologicalrisk 0.341 0.062 0.332 0.030 0.384 0.791
7Securityrisk 0.471 0.018 0.401 0.128 0.591 0.352 0.802
8Servicerisk 0.309 0.056 0.296 0.110 0.279 0.313 0.324 0.827
9Socialrisk 0.155 0.048 0.129 0.118 0.192 0.222 0.196 0.177 0.774
10Timerisk 0.225 0.13 0.221 0.115 0.18 0.231 0.264 0.381 0.231 0.839
11TrustinServiceProvider 0.144 0.404 0.022 0.454 0.122 0.038 0.171 0.104 0.041 0.107 0.863
Table 4. Summary results of hypothesis testing
β T Statistics P Values
Perceivedrisk->Economy-basetrust 0.021 0.558 0.577
Perceivedrisk->Intentiontousemobilemoney 0.065 2.513 0.012
Economy-basetrust->Intentiontousemobilemoney 0.441 13.452 0.001
Perceivedrisk->TrustinServiceProvider 0.159 4.374 0.001
TrustinServiceProvider->Intentiontousemobilemoney 0.266 8.22 0.001
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HypothesisH2wassupported,implyingthatperceivedriskpositivelyimpactsintenttouseMMservices.HypothesisH3wassupported,implyingthatperceivedriskpositivelyimpactstrustintheproviderofMMservices.HypothesisH4wasnotsupported,implyingthatperceivedriskdoesnotpositivelyimpacteconomy-basedtrustofMMservices.HypothesisH5wassupported,implyingthateconomy-basedtrustpositivelyimpactsintenttouseMMservices.HypothesisH6wassupported,implyingthattrustintheserviceproviderpositivelyimpactsintenttouseMMservices.
Figure 1. Hypothesis testing
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BothH5andH6indicatedthattrustaffectsacustomer’sdecisionstouseMMservices.Previousscholars (Tobbin&Kuwornu,2011;Donovan,2012) shared thisobservation.Accordingly,MMserviceprovidersmayconsiderdeployingstrategiesthatcreateorincreasescustomers’trustinthem.SuchtrustmayreducetheperceivedriskofcustomersinusingtheMMservice(Luetal.,2011;Malaquias&Hwang,2016).
Managerial and Theoretical ImplicationsFor practitioners, these findings confirm that MM service providers must assure customers thattheywillsafeguardtheirmoneyandanypersonalinformationthatthecustomersmakeavailable.AttractingmorecustomerswillalsorequireserviceproviderstoreassurecustomersthatMMservicesaretime-savingandcost-effective.Althoughfunctionalriskwasnotasignificantissue,itcannotbedismissedasirrelevant.MMserviceprovidersmustensurethattheirservicesarethefastestmeansofsendingmoneytoothers.ThefindingsalsoindicatethattheriskassociatedwithMMservicesshouldbestudiedfromacustomerperspective,focusingonspecifickindsofriskratherthanoverallrisk.ItfollowsthataddressingperceivedriskinMMserviceswillalsorequirespecificstrategiesfordealingwiththevariousdimensionsofrisk.
Figure 2. Structural model
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Perceivedriskgenerallymakescustomersanxiouswhentheyperformmonetarytransactions,reducingthelikelihoodthattheywilluseMMservices.Perceivedriskalsorelatestothecustomer’strustinserviceproviders.Customersareconcernedaboutsafeguardingpersonalinformationandaboutagentsrunningawaywiththeirmoney.CustomersaremorelikelytouseMMservicesiftheytrustserviceprovidersandagents—thatis,ifagentsandmobiletelecommunicationcompaniescanpersuadecustomersthattheirpersonalinformationandmoneyaresafe.Furthermore,customersaremorelikelytouseMMservicesiftheycanbesurethatdoingsoiseconomicalintermsofthetimeandmoneythatmustbeexpended.
TrustintheserviceproviderreducestheanxietyandpressurethatcustomersmayexperiencewhensendingmoneyviaMMservices.Again,therelationshipbetweenprivacyriskandperceivedriskcanbeattributedtocustomers’trustintheserviceproviders,inthiscase,telecommunicationserviceproviders.Customersmaytrustnetworkoperatorstoprotecttheirpersonalinformation,andasonlylimitedpersonaldetailsaregiventoagentsforthepurposesofanMMtransaction,limitedpersonaldetailsmaynotbeanissueforthecustomer.Thenon-significantrelationshipbetweenserviceriskandperceivedriskmayreflectthefactthatcustomerscancommunicatewithserviceprovidersandobtainhelpwhenthereisaproblemwithanMMtransaction.
Limitations and directions for Future StudiesAll studies naturally confront some limitations. First, this study used only MM users withoutconsideringtheviewsofnon-users.Theviewsofnon-usersofMMservicesmayprovideadditionalinsights into the risk and trust issues in MM services. Future studies may also address certainmethodological(ordemographical)issues.Forinstance,therespondentsherecouldallreadandwrite,butasMMservicesareseenasawayofpromotingfinancialinclusion(seePWC,2016),futurestudiesshouldfocusontheruralpoorandtheuneducated.AnotherlimitationofthisstudyisthatitdidnotexplorehowtheperceivedriskassociatedwithMMservicesmightbereduced,andfuturestudiesshouldaddressthisissue.AsYangetal.(2015)noted,customers’trustevolves,soitisreasonabletolookforwaystoenhancecustomertrustinMMservicesovertime.WhencustomerstrustMMserviceprovidersorMMservice,thetrustmightinfluencetheircommitmentstotheMMserviceproviderorMMservice.Accordingly,MMserviceprovidersmayconsiderbuildingtrustoftheircustomers.
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REFERENCE
Aboobucker,I.,&Bao,Y.(2018).Whatobstructcustomeracceptanceofinternetbanking?Securityandprivacy,risk, trust andwebsiteusability and the roleofmoderators.The Journal of High Technology Management Research,29(1),109–123.doi:10.1016/j.hitech.2018.04.010
Ajzen,I.(1991).Thetheoryofplannedbehavior.Organizational Behavior and Human Decision Processes,50(2),179–211.doi:10.1016/0749-5978(91)90020-T
Al-Gahtani, S. S. (2011). Modeling the electronic transactions acceptance using an extended technologyacceptancemodel.Applied computing and informatics, 9(1),47-77.
Bagozzi,R.P.,&Yi,Y.(1988).Ontheevaluationofstructuralequationmodels.Journal of the Academy of Marketing Science,16(1),74–94.doi:10.1007/BF02723327
Ball,J.G.,Manika,D.,&Stout,P.(2016).Causesandconsequencesoftrustindirect-to-consumerprescriptiondrugadvertising.International Journal of Advertising,35(2),216–247.doi:10.1080/02650487.2015.1009346
Bauer,H.H.,Reichardt,T.,Barnes,S.J.,&Neumann,M.M.(2005).Drivingconsumeracceptanceofmobilemarketing:Atheoreticalframeworkandempiricalstudy.Journal of Electronic Commerce Research,6(3),181.
Boateng,H.,Adam,D.R.,Okoe,A.F.,&Anning-Dorson,T.(2016).Assessingthedeterminantsofinternetbankingadoptionintentions:Asocialcognitivetheoryperspective.Computers in Human Behavior,65,468–478.doi:10.1016/j.chb.2016.09.017
Carnevale,M.,Loureiro,Y.K.,&Kabadayi,S.(2018).CustomerValueCreationforRiskyProducts:TheRoleofBrandTrustandTrustingBeliefs.Journal of Creating Value.
Chai,S.,&Kim,M.(2010).Whatmakesbloggersshareknowledge?Aninvestigationintotheroleoftrust.International Journal of Information Management,30(5),408–415.doi:10.1016/j.ijinfomgt.2010.02.005
Chang,H.S.,&Hsiao,H.L.(2008).Examiningthecausalrelationshipamongservicerecovery,perceivedjustice,perceivedrisk,andcustomervalueinthehotelindustry.Service Industries Journal,28(4),513–528.doi:10.1080/02642060801917646
Chauhan,S.(2015).AcceptanceofmobilemoneybypoorcitizensofIndia:Integratingtrustintothetechnologyacceptancemodel.info, 17(3),58-68.
Chin,W.W. (2010).How towriteup and reportPLSanalyses. InHandbook of partial least squares (pp.655–690).Springer.doi:10.1007/978-3-540-32827-8_29
Das,T.K.,&Teng,B.S.(2002).Thedynamicsofallianceconditionsinthealliancedevelopmentprocess.Journal of Management Studies,39(5),725–746.doi:10.1111/1467-6486.00006
Davis,F.D.,Bagozzi,R.P.,&Warshaw,P.R.(1989).Useracceptanceofcomputertechnology:Acomparisonoftwotheoreticalmodels.Management Science,35(8),982–1003.doi:10.1287/mnsc.35.8.982
Diniz,E.,Birochi,R.,&Pozzebon,M.(2012).Triggersandbarrierstofinancialinclusion:TheuseofICT-basedbranchlessbankinginanAmazoncounty.Electronic Commerce Research and Applications,11(5),484–494.doi:10.1016/j.elerap.2011.07.006
Donovan,K.(2012).Mobilemoneyforfinancialinclusion.Information and Communications for development, 61(1),61-73.
Featherman,M.S.,&Pavlou,P.A.(2003).Predictinge-servicesadoption:Aperceivedriskfacetsperspective.International Journal of Human-Computer Studies,59(4),451–474.doi:10.1016/S1071-5819(03)00111-3
Fishbein,M.,&Ajzen, I. (1975).Belief.Attitude, Intention, and Behavior: An Introduction to Theory and Research, 216.
Fornell,C.,&Larcker,D.F.(1981).Structuralequationmodelswithunobservablevariablesandmeasurementerror:Algebraandstatistics.JMR, Journal of Marketing Research,18(3),382–388.doi:10.2307/3150980
Gefen,D.,Karahanna,E.,&Straub,D.W.(2003).TrustandTAMinonlineshopping:Anintegratedmodel.Management Information Systems Quarterly,27(1),51–90.doi:10.2307/30036519
International Journal of E-Business ResearchVolume 15 • Issue 1 • January-March 2019
16
Gichuki,C.N.,&Mulu-Mutuku,M.(2018,April).Determinantsofawarenessandadoptionofmobilemoneytechnologies:Evidencefromwomenmicro-entrepreneursinKenya.Women’s Studies International Forum,67,18–22.doi:10.1016/j.wsif.2017.11.013
Hair,F.Jr,J.,Sarstedt,M.,Hopkins,L.,&G.Kuppelwieser,V.(2014).Partialleastsquaresstructuralequationmodeling (PLS-SEM)Anemerging tool inbusiness research.European Business Review,26(2), 106–121.doi:10.1108/EBR-10-2013-0128
Hille,P.,Walsh,G.,&Cleveland,M.(2015).Consumerfearofonlineidentitytheft:Scaledevelopmentandvalidation.Journal of Interactive Marketing,30,1–19.doi:10.1016/j.intmar.2014.10.001
Im,I.,Kim,Y.,&Han,H.J.(2008).Theeffectsofperceivedriskandtechnologytypeonusers’acceptanceoftechnologies.Information & Management,45(1),1–9.doi:10.1016/j.im.2007.03.005
Jang-Jaccard,J.,&Nepal,S.(2014).Asurveyofemergingthreatsincybersecurity.Journal of Computer and System Sciences,80(5),973–993.doi:10.1016/j.jcss.2014.02.005
Kim,T.Y.,Wang,J.,&Chen,J.(2018).Mutualtrustbetweenleaderandsubordinateandemployeeoutcomes.Journal of Business Ethics,149(4),945–958.doi:10.1007/s10551-016-3093-y
Kim,Y.,&Peterson,R.A.(2017).AMeta-analysisofOnlineTrustRelationshipsinE-commerce.Journal of Interactive Marketing,38,44–54.doi:10.1016/j.intmar.2017.01.001
Kuisma,T.,Laukkanen,T.,&Hiltunen,M.(2007).MappingthereasonsforresistancetoInternetbanking:A means-end approach. International Journal of Information Management, 27(2), 75–85. doi:10.1016/j.ijinfomgt.2006.08.006
Lee,J.E.,&Lemyre,L.(2009).Asocial‐cognitiveperspectiveofterrorismriskperceptionandindividualresponseinCanada.Risk Analysis: An International Journal,29(9),1265–1280.doi:10.1111/j.1539-6924.2009.01264.xPMID:19650811
Liébana-Cabanillas,F.,Nogueras,R.,Herrera,L.J.,&Guillén,A.(2013).Analysingusertrustinelectronicbanking using data mining methods. Expert Systems with Applications, 40(14), 5439–5447. doi:10.1016/j.eswa.2013.03.010
Lohmoeller, J. B. (1989). Latent variable path analysis with partial least squares. Heidelberg: Physica.doi:10.1007/978-3-642-52512-4
Lopez-Nicolas,C.,&Molina-Castillo,F.J.(2008).CustomerKnowledgeManagementandE-commerce:Theroleofcustomerperceivedrisk.International Journal of Information Management,28(2),102–113.doi:10.1016/j.ijinfomgt.2007.09.001
Lu,Y.,Yang,S.,Chau,P.Y.,&Cao,Y.(2011).Dynamicsbetweenthetrusttransferprocessandintentiontousemobilepaymentservices:Across-environmentperspective.Information & Management,48(8),393–403.doi:10.1016/j.im.2011.09.006
Malaquias,R.F.,&Hwang,Y.(2016).Anempiricalstudyontrustinmobilebanking:Adevelopingcountryperspective.Computers in Human Behavior,54,453–461.doi:10.1016/j.chb.2015.08.039
Martins,C.,Oliveira,T.,&Popovič,A.(2014).UnderstandingtheInternetbankingadoption:Aunifiedtheoryof acceptance and use of technology and perceived risk application. International Journal of Information Management,34(1),1–13.doi:10.1016/j.ijinfomgt.2013.06.002
Mothobi,O.,&Grzybowski,L.(2017).InfrastructuredeficienciesandadoptionofmobilemoneyinSub-SaharanAfrica.Information Economics and Policy,40,71–79.doi:10.1016/j.infoecopol.2017.05.003
Namahoot,K.S.,&Laohavichien,T. (2018).Assessing the intentions touse internetbanking:Theroleofperceived risk and trust as mediating factors. International Journal of Bank Marketing, 36(2), 256–276.doi:10.1108/IJBM-11-2016-0159
Nofer, M. (2015). The value of social media for predicting stock returns: preconditions, instruments and performance analysis.Springer.doi:10.1007/978-3-658-09508-6
International Journal of E-Business ResearchVolume 15 • Issue 1 • January-March 2019
17
Omwansa,T.K.,Waema,T.M.,Chen,C.,&Sullivan,N.P.(2013).Themobilephoneasthetooltoredefinesavingsforthepoor:EvidencefromKenya.African Journal of Science, Technology.Innovation and Development,5(5),355–361.
Ortiz,J.,Chih,W.H.,&Tsai,F.S.(2018).Informationprivacy,consumeralienation,andlurkingbehaviorinsocialnetworkingsites.Computers in Human Behavior,80,143–157.doi:10.1016/j.chb.2017.11.005
Park,J.,Lennon,S.J.,&Stoel,L.(2005).On‐lineproductpresentation:Effectsonmood,perceivedrisk,andpurchaseintention.Psychology and Marketing,22(9),695–719.doi:10.1002/mar.20080
Pavlou, P. A., Liang, H., & Xue, Y. (2007). Understanding and mitigating uncertainty in online exchangerelationships: A principal-agent perspective. Management Information Systems Quarterly, 31(1), 105–136.doi:10.2307/25148783
PWC.(2016).2016GhanaBankingSurvey:Howtowininaneraofmobilemoney.Retrievedfromhttps://www.pwc.com/gh/en/assets/pdf/2016-banking-survey-report.pdf
Ratnasingam,P.,Gefen,D.,&Pavlou,P.A.(2005).Theroleoffacilitatingconditionsandinstitutionaltrustin electronic marketplaces. Journal of Electronic Commerce in Organizations, 3(3), 69–82. doi:10.4018/jeco.2005070105
Ringle,C.M.,Wende,S.,&Becker,J.M.(2015).SmartPLS3.Boenningstedt:SmartPLSGmbH.Retrievedfromhttp://www.smartpls.com
Rogers, E. M. (1995). Diffusion of Innovations: modifications of a model for telecommunications. In Die diffusion von innovationen in der telekommunikation(pp.25–38).Springer.doi:10.1007/978-3-642-79868-9_2
Safeena,R.,Kammani,A.,&Date,H.(2018).ExploratoryStudyofInternetBankingTechnologyAdoption.InTechnologyAdoptionandSocialIssues:Concepts,Methodologies,Tools,andApplications(pp.333-355).Hershey,PA:IGIGlobal.
Shaikh, A. A., Glavee-Geo, R., & Karjaluoto, H. (2018). How relevant are risk perceptions, effort, andperformanceexpectancyinmobilebankingadoption?International Journal of E-Business Research,14(2),39–60.doi:10.4018/IJEBR.2018040103
Song, J.,&Zahedi,F. (2002). A theoretical framework for theuseofweb infomediaries. InAMCIS 2002 Proceedings(p.308).
SumChau,V.,&Ngai,L.W.(2010).Theyouthmarketforinternetbankingservices:Perceptions,attitude,andbehavior.Journal of Services Marketing,24(1),42–60.doi:10.1108/08876041011017880
Taylor,S.,&Todd,P.A.(1995).Understandinginformationtechnologyusage:Atestofcompetingmodels.Information Systems Research,6(2),144–176.doi:10.1287/isre.6.2.144
Tchouassi,G.(2012).CanMobilePhonesReallyWorktoExtendBankingServicestotheUnbanked?EmpiricalLessonsfromSelectedSub-SaharanAfricaCountries.International Journal of Developmental Science,1(2),70–81.
Tobbin, P., & Kuwornu, J. K. (2011). Adoption of mobile money transfer technology: Structural equationmodelingapproach.European Journal of Business and Management,3(7),59–77.
Venkatesh,V.,Morris,M.G.,Davis,G.B.,&Davis,F.D.(2003).Useracceptanceofinformationtechnology:Towardaunifiedview.Management Information Systems Quarterly,27(3),425–478.doi:10.2307/30036540
Wu,Y.L.,Tao,Y.H.,&Yang,P.C.(2008).Theuseofunifiedtheoryofacceptanceanduseoftechnologytoconferthebehavioralmodelof3Gmobiletelecommunicationusers.Journal of Statistics and Management Systems,11(5),919–949.doi:10.1080/09720510.2008.10701351
Yang,Q.,Pang,C.,Liu,L.,Yen,D.C.,&Tarn,J.M.(2015).Exploringconsumerperceivedriskandtrustforonlinepayments:AnempiricalstudyinChina’syoungergeneration.Computers in Human Behavior,50,9–24.doi:10.1016/j.chb.2015.03.058
Yousafzai,S.Y.,Pallister,J.G.,&Foxall,G.R.(2003).Aproposedmodelofe-trustforelectronicbanking.Technovation,23(11),847–860.doi:10.1016/S0166-4972(03)00130-5
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APPENdIX A: MEASUREMENT INSTRUMENT
Table 5. Measurement instrument
Codes Measurements items and sources
Economicrisk(Yangetal.,2015;Featherman&Pavlou,2003)
ERK1 Iworrythatmobilemoneytransfercostsandothercostswillmakethetotalexpenditurehigherthanexpected.
ERK2 IworrythatifIwanttosendmoney,themobilemoneyvendorwillcollectthemoneybutwillnotsendit.
ERK3 Iworrythatmonetarylosscanbecausedbytheimproperactionsofmobilemoneyvendors.
Functionalrisk(Yangetal.,2015;Featherman&Pavlou,2003)
FRK1 Iworrythatthelimitedfunctionsofmobilemoneywillnotmeetmyrequirements.
FRK2 IworrythatIcan’tsendmoneythroughthemobilemoneysystembecauseofunstablenetworks.
FRK3 IworrythatIcan’tsendmoneythroughthemobilemoneysystembecauseofdelaysintransferringthemoney.
Securityrisk(Yangetal.,2015;Featherman&Pavlou,2003)
SRK1 Iworrythatthetransactioninformationwillbestolenbyathirdparty.
SRK2 Iworrythatmyaccountinformationwillbeusedbyathirdpartytoperformanillegaltransaction.
SRK3 IworrythatthemoneyIsendviamobilemoneycanbewithdrawnbyathirdparty.
SRK4 IworrythatthemoneyIsendthroughmobilemoneywillnotreachtheintendedrecipient.
Privacyrisk(Yangetal.,2015)
PRK1 Iworrythatmypersonalinformationmaybestolenduringatransactionbecauseofthecarelessnessofmobilemoneyvendorsorserviceproviders.
PRK2 Iworrythatmypersonalinformationcanbeimproperlycollectedandusedbymobilemoneyvendorsandmobilemoneyserviceproviders.
PRK3 Iworrythathackerscanstealmypersonalinformationfromthedatabaseofthemobilemoneyserviceprovider.
Timerisk(Yangetal.,2015)
TMK1 Iworrythatitwillcostmealotoftimetoperformmobilemoneytransactions.
TMK2 Iworrythatitwillcostmealotoftimetoaccessmobilemoneyservices.
TMK3 IworrythatitwillcostmealotoftimetoconfirmmyIDandotherdetailsformobilemoneytransactions.
Servicerisk(Yangetal.,2015;Featherman&Pavlou,2003)
SK1 IworrythatifIperformamobilemoneytransaction,itwillbedifficulttocommunicatewiththemobilemoneyserviceproviderorvendorwhenIneedhelp.
SK2 IworrythatifIperformamobilemoneytransaction,itwillbedifficulttogetthemobilemoneyserviceproviderorvendortorespondtomyqueries.
SK3 IworrythatifIperformamobilemoneytransaction,itwillbedifficulttoknowwheremobilemoneyvendorsarelocated.
Psychologicalrisk(Yangetal.,2015;Featherman&Pavlou,2003)
PK1 IworrythatIwillexperiencestressifthereisalossduringamobilemoneytransaction.
PK2 IworrythatIwillbeanxiousbeforethemobilemoneytransactionisconfirmedandreachestheintendedrecipient.
PK3 IworrythatIwillbeanxiousifthemoneycan’tbetransferredinatimelyway.
continued on following page
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Ibn Kailan is a PhD Candidate at the University of Ghana and a faculty at the University of Professional Studies, Accra. His research interested are in marketing, sustainability marketing, market orientation, mobile commerce, corporate social responsibility, and emerging market research.
Shaikh is a faculty at the Jyväskylä University School of Business and Economics.
Henry Boateng holds a PhD in Knowledge Management from the University of Technology, Sydney. His research experience covers knowledge management, branding, electronic business and commerce and internet application in marketing. Email: [email protected]
Robert E. Hinson is Professor in the University of Ghana Business School, Legon, Ghana and a Research Associate at the University of the Free State Business School in South Africa. Email contact: [email protected]
Codes Measurements items and sources
Socialrisk(Yangetal.,2015;Featherman&Pavlou,2003)
SKk1 IworrythatifIamcheatedwhenmakinganonlinepayment,myfriendsandfamilywillmakenegativecommentsaboutme.
SKk2 IworrythatifIdon’tusemobilemoneyservices,myfriendsandfamilywillthinkI’mnotfashionable.
SKk3 Iworrythatmyfriendsandfamilywilllaughatmeforusingmobilemoneyservices.
Economy-basedtrust(Chai&Kim,2010)
ET1 Usingmobilemoneyservicessavestime.
ET2 Usingmobilemoneyservicesavescost.
ET3 Itiseconomicaltousemobilemoneyservices.
Trustinserviceproviders(Chai&Kim,2010)
TP1 Itrustthatmobilemoneyserviceproviderswillsafeguardmypersonalinformation.
TP2 Itrustthatmobilemoneyserviceproviderswillsafeguardmymoney.
TP3 Itrustthatmobilemoneyagentswillnotrunawaywithmymoney.
Intentionstousemobilemoneyservices(Yangetal.,2015).
IUM1 Ingeneral,I’mwillingtousemobilemoneyservices.
IUM2 Ifpossible,Iwillcontinuetousemobilemoneyservices.
IUM3 Iwillencouragemyfamilyandfriendstousemobilemoneyservices.
Table 5.