机器人服务质量与消费者接受度之间的关系

摘要IIIITheRelationshipbetweenRobots’ServiceQualityandConsumerAcceptanceintheContextofHospitality:intheContextofHospitalityTheintegrationofservicerobotswithinthehospitalityindustryisrapidlytransformingthewayhotelsoperateandengagewiththeircustomers.Astechnologicaladvancementscontinuetoreshapeservicedelivery,understandingconsumerperceptionsandbehavioralintentionstowardtheseinnovationsbecomescrucial.Part1Introduction1.1ResearchBackgroundTheInternationalFederationofRobotics(IFR)categorizesrobotsintotwomaintypes:industrialrobotsandservicerobots(Tussyadiah,2020).Aservicerobotisdefinedasarobotperformsusefultasksforhumansorequipmentforpersonalorprofessionaluse(InternationalFederationofRobotics,2025).Asroboticscontinuestobeintegratedintovariousprofessionalsectorssuchashealthcare,logistics,andconstruction,theIFRpredictsthattheglobalmarketwillmaintainacompoundannualgrowthrate(CAGR)ofmorethan20%through2030(IFR,2024).ThisgrowthisfurthersupportedbyresearchfromStatista(2024),whichshowsthatbothrevenueandmarketvolumeareonanupwardtrajectory,asshowninFigures1and2.Figure1:RevenueofGlobalServiceRoboticsMarketSource:Statista,2024Figure2:VolumeofGlobalServiceRoboticsMarketSource:Statista,2024Astechnologycontinuestoadvance,particularlyinareassuchasartificialintelligence,machinelearning,andsensortechnology,thecapabilitiesofservicerobotsarebecomingincreasinglysophisticated(Oosthuizen,2022).Servicerobotscanengageinmoresophisticatedserviceinteractionsandhavemoreprominentvisibilityinsidetheservicescapeduetomobility(Tussyadiah,2020;Tsengetal.,2016).Anthropomorphicandinteractiondesignmakesservicerobotswithasocialcomponent(Christouetal.,2020;Wangetal.,2021).Thesedevelopmentsunderscorethegrowingintegrationofservicerobotsintovarioussectors,particularlythoseinvolvingprofessionaltasksanddailyserviceactivities(Belancheetal.,2021).Continualtechnologicaladvancesandtherisingpopularityoflow-contactservicesduetothepandemichaveledtoanincreasedapplicationofroboticsinvarioussectorsofthehospitalityandtourismindustry(Bowen&Morosan,2018;Seyitoğlu&Ivanov,2021;Agarwaletal.,2024).Inthetourismandhospitalityindustries,servicerobotshavegainedsignificantresearchinterest,beingutilizedindiversecontextssuchasrestaurants(Molinilloetal.,2023;Maetal.,2023),hotels(Choietal.,2020;Kuoetal.,2017),andtravelagencies(Ivanovetal.,2019).Servicerobotsinhotelsperformvarioustaskstoenhanceefficiencyandguestexperience.Theyassistwithcheck-in/check-out,provideinformation,deliverroomservice,transportluggage(Murphyetal.,2017;Belancheetal.,2020).Giventhetraditionallyhighlevelofhumaninteractioninthesesectors,theintegrationofrobotssignifiesatransformativephase,withprojectionssuggestingthatby2030,servicerobotsmayconstitutearound25%oftheworkforceinthehotelindustry(Bowen&Morosan,2018).Asthehospitalityindustryfacesincreasingcompetition,hotelsareincreasinglyturningtoservicerobotstoreducelaborcosts(Collier&Kimes,2013;Ivanovetal.,2020),improveoperationalefficiency(Ivanovetal.,2018),andadapttoevolvingconsumerexpectations(Collins,2020).Integratingrobotsintotheservicedeliveryprocesscanoffercompetitiveadvantages(Kervenoaeletal.,2020;Chenetal.,2021).1.2SignificanceofResearchHotelsarefocusingonserviceevolutionandexcellenceinintegratingrobotsintotheservicedeliveryprocess(Kervenoaeletal.,2020;Chenetal.,2021),aimingtoenhancecustomerwillingnesstoadoptroboticservicesandimproveinteractiveexperiences(Park,2020;Goeletal.,2022)Theoretically,thisstudyextendspreviousstudiesexaminingcustomerattitudestowardsservicerobots.Itwillidentifythespecificdimensionsofservicequalityandelucidatethemechanismsofguestperceptionandadoption.Therebyitprovidesvaluableinsightsforexploringtheinfluencingvariablesofemployingserviceroboticsintourismbasedondemand-side.Understandingtheinfluenceofperceivedservicequalitydeliveredbyhotelrobotsonconsumers'intentionsisessentialforthewiderTHEindustry.Improvedservicequalitywillencouragecustomerstoembraceroboticservices(Goeletal.,2022).Byidentifyingwhichaspectsofservicequalitymostimpactconsumerdecision-making,theresearchcanhelphotelmanagersoptimizeservicedesign,enhancecompetitiveness,andultimatelyimprovetheoverallguestexperience(Ivanovetal.,2019;Luetal.,2019).Furthermore,thestudyprovidesempiricalsupporttoguidestrategicdecisionsinarapidlyevolvingmarket,withpracticalimplicationsforindustrystakeholderswhointendtouserobotsintheirservices.1.3ResearchQuestionandObjectivesThisstudyaimstoinvestigatetheimpactofhotelservicerobotservicequalityonconsumerintentiontouseroboticservicesinthehospitalitysector,providinginsightsintohowservicerobotscaneffectivelymeetconsumerneeds.Thiswillbecarriedthroughspecificresearchobjectives:A)Investigatehowtheservicequalityofhotelservicerobots,basedontheSERVPERFmodel,influencesconsumers'intentiontouseroboticservices.B)ExaminethespecificeffectsofeachSERVPERFdimension(reliability,responsiveness,assurance,empathy,andtangibles)onconsumers'intentiontoadoptservicerobotsinthehospitalitysector,usingquantitativemetricstomeasureintention.C)Toidentifybarrierstoacceptanceandfactorsthatenhanceconsumertrustandsatisfactioninroboticservices

withinthehospitalitycontext.Formulatestrategicrecommendationsforhotelmanagersandawiderindustrystakeholdersonhowtoenhanceservicequalityinroboticservicesandeffectivelypromotetheiradoptionamongconsumers.1.4ConceptualFrameworkBasedonSERVPERFmodel,Thisstudywillexplorevariousdimensionsofservicequalityandtheircorrelationwithconsumerattitudesandbehaviors,providingvaluableinsightsintohowthesefactorsaffecttheemploymentofservicerobots.Thisresearchfirstintroducestheresearchbackground,significance,andobjectives.Inthesecondpart,thetheoryisexplained,andhypothesesareformulatedfortheresearchquestions.Thethirdpartoutlinesthequantitativeanalysismethodandthequestionnairedesign.Inthefourthpart,thedifferentialimpactofeachSERVPERFdimensiononconsumerbehaviorisanalyzed,andthehypothesesaretestedthroughdescriptiveanalysisandlinearregressionanalysis.Basedontheresultsofthepreviousanalysis,thefifthpartproposesmarketingsuggestions.Thesixthpartconcludestheresearch,presentsitslimitations,andprovidesrecommendationsforfutureresearch.Part2LiteratureReview2.1Co-occurrenceAnalysisFrom2002to2025,theresearchonservicerobotliteraturehaveimproved,reflectingagrowinginterestandsignificance.Specificallyinhospitalitycontext,thesepapersweresummarizedaccordingtotheresearchpurposeandthemes,whichresultedinthreemaintopicareas,servicerobotacceptance,tourist–robotinteractionandroboticserviceperformance(seefigure3).Figure3:Co-occurrenceAnalysisSource:self-madeTable1MatrixAnalysis--ServiceRobotAcceptanceSource:Linetal.,2020;Abou-Shouketal.,2021Table2MatrixAnalysis--Tourist-RobotInteractionSource:Choietal.,2020;Tung&Au,2018Table3MatrixAnalysis--RoboticServicePerformanceSource:Liu&Hung,2019;S.Choietal.,2020b2.2CustomerAcceptanceofTechnologyUseracceptancehasemergedasaprominenttheme,asresearchersexploreusers'attitudes,behavioralintentions,andactualusagebehaviorsregardingservicerobots.Thesestudiesinvestigatedthevaryingfactorsthataffecttouristwillingnesstouseorinteractwithaservicerobotbasedontechnologyacceptancetheories.Venkateshetal.’s(2003)

UnifiedTheoryofAcceptanceandUseofTechnology(UTAUT)

standsasaseminalcontribution,synthesizingconstructssuchasperformanceexpectancy,effortexpectancy,andsocialinfluenceintoacohesivemodelforpredictingbehavioralintentions.Itsstrengthliesinits

generalizabilityacrossdiversetechnologicalcontexts,offeringarobustbaselineforsubsequentadaptationsinroboticsresearch.However,UTAUT’srelianceon

cross-sectionaldata

limitsitscapacitytocapturedynamicshiftsinuserattitudesovertime,acriticalgapinfast-evolvinghospitalityenvironmentswhereconsumerexpectationsrapidlyadapttotechnologicaladvancements(Linetal.,2020).BuildingonUTAUT,latermodelslikethe

ServiceRobotAcceptanceModel(sRAM)

(Wirtzetal.,2018)and

SociallyInteractiveRobotAcceptanceModel(SIRAM)

(Shin&Choo,2011)introducednoveldimensionssuchassocialpresenceandemotionalengagement,addressingtheuniquehuman-robotinteractiondynamicsoverlookedbyearlierframeworks.Theseextensionsdemonstrate

theoreticalinnovation,particularlyincontextualizingacceptancewithinservice-orientedsettings.Yet,theirheavyrelianceon

lab-basedexperiments

(e.g.,simulatedhotelcheck-inscenarios)raisesconcernsaboutecologicalvalidity,asartificialenvironmentsmaynotfullyreplicatethecomplexitiesofreal-worldserviceencounters(Tung&Au,2018).Empiricalapplicationsofthesetheoriesfurtherhighlighttheirstrengthsandlimitations.Forexample,Abou-Shouketal.(2021)successfullyappliedthe

TechnologyAcceptanceModel(TAM)

toroboticadoptioninEgyptiantourism,reaffirmingtheuniversalrelevanceof

perceivedeaseofuse(PEU)

and

perceivedusefulness(PU)

ascoredriversofacceptance.TheirworkexemplifiesTAM’s

practicaladaptability

acrossculturalcontexts.Nevertheless,theirexclusivefocusonEgyptiantouristslimitsinsightsintohowindividualistculturalvaluesmightmoderatePEU/PUeffects(Choietal.,2020).Linetal.’s(2020)

ArtificiallyIntelligentDeviceUseAcceptance(AIDUA)

frameworkrepresentsanotherstrideforward,integrating

hedonicmotivation

and

anthropomorphism

toexplainhospitalitycustomers’intentions.ByemphasizingemotionstowardAIdevices,AIDUAcapturestheaffectivedimensionsofhuman-robotinteractions,astrengththatalignswithdeKervenoaeletal.’s(2020)findingsonempathyasacriticalmediator.Anthropomorphismisalsothemainresearchareainrobotacceptance,Chung-EnYu(2020)andBelancheetal.(2020)discoverthepublic’sgeneralnegativeperceptionsofhumanlikerobotsbasedonUncannyvalleytheory.

AsurveydidbyBaltacietal(2024)indicatedthatattitudetowardsservicerobotsmediatedtherelationshipbetweenanthropomorphism,technologicalreadinessandintentiontouse.Participatingtouristsanthropomorphicallyfocusedonthephysicalappearanceoftherobotsratherthantheirfunctionsandinternalstructures.2.3Human–robotInteractionHuman–robotinteractionisanimportantfeatureofservicerobotsandthesuccessfuladoptionofservicerobotsishighlydependentoneffectivehuman–robotinteractions.Murphyetal.(2017)andTungandLaw(2017)establishedthefoundationalpremisethateffectiveHRIispivotalforsuccessfulroboticintegration,emphasizingfunctionalitiessuchastaskefficiencyandusersatisfaction.However,theirearlyframeworkspredominantlyfocusedontechnicalperformancemetrics(e.g.,responsetime,accuracy),overlookingthesocio-emotionaldimensionsthatshapeuserperceptionsinhospitalitycontexts.Subsequentresearchexpandedthisperspectivebyincorporatingculturalandexperientialfactors.Forinstance,Y.Choietal.(2020)conducteda

cross-culturalanalysis

ofhotelguests’interactionswithservicerobots,revealingthatJapaneseguestsexhibitedstrongeremotionalengagementduringHRI,whereasnon-Japaneseguestsprioritizedfunctionalreliability.Whilethisstudyunderscoresthe

roleofculturalcontext

inshapinginteractionpreferences,its

exclusiverelianceonself-reporteddata

riskssocialdesirabilitybias,asparticipantsmayoverstatepositivereactionstoconformtoculturalnorms(Podsakoffetal.,2003).TungandAu(2018)advancedthefieldbyapplyingthe

USUSevaluationframework

toHRI,identifyingfivedimensionsoftourist–robotinteraction:usability,socialacceptance,userexperience,societalimpact,andsustainability.Theirworkprovidesa

holisticassessmenttool,yetitstheoreticalbreadthcompromisesgranularity.Forexample,the“socialacceptance”dimensionconflatesculturalnormsandindividualattitudes,obscuringhowspecificfactors(e.g.,trustinautomation)independentlyinfluenceadoption(Fuentes-Moraledaetal.,2020).Similarly,Fuentes-Moraledaetal.(2020)proposedathree-dimensionalmodel(functional,social-emotional,andrelationalelements)toexplainHRIacceptance.Whilethismodel

addressesmulti-facetedinteractiondynamics,itsoperationalizationof“relationalelements”remainsabstract,lackingempiricalvalidationinreal-worldservicerecoveryscenarios(Hoetal.,2020).2.4PerceivedServiceQualityServicequalityreferstocustomers'perceptionsandexperienceswithaservice(Asubontengetal.,1996;Wisniewski,1996).Itisamultifacetedconcept,determinedbyconsumers'expectationsbeforereceivingtheserviceandtheirevaluationoftheexperienceaftertheserviceisdelivered(Grönroos,1990).Researchonservicequalityhasprimarilyfocusedonmeasuringcustomers'opinionsregardingtheperformanceoftheservicedelivered(Athanassopoulosetal.,2001;Bloemeretal.,1999).Grönroos(1984)madeanearlydistinctionbetweentechnicalquality,whichisoutcome-focused(e.g.,broadbandspeed),andfunctionalquality,whichisprocess-focused(e.g.,customerserviceinteractions).Heemphasizedthatbothdimensionscollectivelyshapecustomerperceptions.Thisdualisticframeworklaidthegroundworkforsubsequentmodels,notablySERVQUAL(Parasuramanetal.,1988),whichoperationalizedservicequalitythroughfivedimensions:tangibles,reliability,responsiveness,assurance,andempathy.SERVQUAL’sfocusontheexpectation-perceptiongapofferedanuancedunderstandingofcustomersatisfactionbutfacedcriticismforthesubjectivityinvolvedinmeasuringexpectations(Cronin&Taylor,1992).Inresponse,CroninandTaylor(1992)proposedtheSERVPERFmodel,whichemphasizedperformanceratherthanexpectations.AccordingtotheSERVPERFmodel,servicequalityshouldbeevaluatedsolelybasedoncustomers'perceptionsoftheactualperformanceoftheservice,withoutconsideringtheirpriorexpectations.Byremovingtheneedtomeasureexpectations,SERVPERFprovidedamorestraightforwardandobjectivewaytoassessservicequality.TheSERVPERFmodeliswidelyrecognizedasaneffectivetoolformeasuringservicequality(Akdere,2020;Sohail&Hasan,2021).Thismodelsupportstheperformance-basedapproachbyprovidingclearerinsightsintocustomersatisfaction,asitdirectlylinksserviceoutcomeswithcustomerperceptions.However,SERVPERFisnotwithoutitslimitations.Whileitremovesthesubjectivityofmeasuringexpectations,itmayfailtocapturethecomplexitiesofcustomerperceptions,particularlyincontextswherepriorexpectationsarehighlyrelevanttoserviceevaluations(Zeithaml,1988).Whenmeasuringservicequalityofservicerobotsinhospitality,currentliteratureusuallyfocusesonassessingservicerobotperformanceinrelationtocustomertransactionalvalues,withanemphasisoncomparisonswithhumanemployees.(Chan&Tung,2019;S.Choietal.,2019;Prentice,Lopes,&Wang,2020;Prentice&Nguyen,2020).Forinstance,Ho,Tojib,andTsarenko(2020)foundthatwhileservicerobotscanprovidesomelevelofsupport,theyarelesseffectivethanhumanstaffinmitigatingnegativefeelings,highlightingthecontinuedimportanceofhumaninteractioninhospitalityrecoverysituations.Similarly,Fanetal.(2020)demonstratedthattechnologyanthropomorphismcaneffectivelyalleviatecustomerdissatisfactionfollowingservicefailures.Athree-factormodelofservicequalitywasdevelopedbyintegratingSERVQUALandSERVPERFintoahierarchicalstructurewiththedimensionsofinteractionquality,outcomequality,andphysicalserviceenvironment(Brady&Cronin,2001).ThismodelisusedbyChoietal.(2020)tounderstandthefutureuseofservicerobotsandtheireffectonservicequalityfromtheperspectiveofhotelmanagers,whichalsocontributestocomparingservicequalitybetweenservicerobotsandhumanstaff.2.5ReviewofExistingLiteratureandHypothesisDevelopmentAsartificialintelligencecontinuestoevolve,theintelligenceofservicerobotswillfurtherimprove,presentingnewopportunitiesandchallengesforthehotelindustry.Severalgapshavebeenidentifiedwithintheliterature.Critically,existingresearchprimarilyfocusesonthephysicalattributesofservicerobotsthemselvesandtheelementsoftechnologyacceptancemodelstoinvestigateguests’willingnesstouseorinteractwithaservicerobot.However,therelationshipbetweenuserperceivedvalueintherobot-humaninteractionservicedeliveryprocesswithcustomeracceptanceinthehospitalitycontextremainsunderexplored.Particularly,thereisalackofin-depthinvestigationintowhichspecificaspectsofservicerobotattributesresonatemostwithconsumersanddrivetheirintentionstoutilizetheseservices.Therefore,hypothesisofthisresearchareproposedbasedonSERVPERFmodel:H1:ThereliabilitycharacteristicsoftheservicerobotispositivelyrelatedtotheCustomer’swillingnesstouse.H2:ThetangiblecharacteristicsoftheservicerobotispositivelyrelatedtotheCustomer’swillingnesstouse.H3:TheresponsivenesscharacteristicsoftheservicerobotispositivelyrelatedtotheCustomer’swillingnesstouse.H4:TheassurancecharacteristicsoftheservicerobotispositivelyrelatedtotheCustomer’swillingnesstouse.H5:TheempathycharacteristicsoftheservicerobotispositivelyrelatedtotheCustomer’swillingnesstouse.Figure4:ResearchmodelSource:self-madeThisresearchaimstofillthesegapsbyempiricallyinvestigatingtheperceivedservicequalityofhotelservicerobotsandexamininghowtheseperceptionsaffectconsumers'intentionstouseroboticservices.Byfocusingontheinterplaybetweenservicequalityandconsumerperceptionsinthehospitalitycontext,thisstudywillprovidevaluableinsightsintothefactorsthatdriveservicerobotadoption,therebyenhancingthecurrentunderstandingofhowtoeffectivelyintegrateroboticsintohospitalityservices.Part3Methodology3.1ResearchDesignandSamplingInthisresearch,thepurposeistoinvestigatetheinfluenceofservicequalityonconsumerintentionstoutilizehotelservicerobots.Specifically,theresearchaimstoanalyzethedifferentialimpactofeachSERVPERFdimensiononconsumerbehavior.Toanswertheaims,thisresearchwilluseanobjectivistandpositivistresearchparadigmtoformulatehypothesesandtestthemtoprovecausalrelationshipsbetweenvariablesthroughaninductiveapproach,andaquantitativeanalysismethodwillbeusedtocollectdata.Thequantitativeapproachwillallowstatisticalevaluationofthedatatotherelationshipsbetweenservicequalityandconsumerbehavioralintentions,therebyyieldingclearandquantifiableresults.ThetotalpopulationisChineseconsumers,andasampleof300consumersaged18-64whousetheInternetwillbeselected.Systematicsamplingwillbeconductedfordifferentgenders,ages,incomes,etc.,andforpeoplewithvaryinguserexperiences.Anadditional10%ofquestionnaireswillbedistributed,consideringcasessuchasinvalidorincompletequestionnaires,resultinginatotalof340expectedquestionnaires.3.2MeasurementInstrumentsandDataAnalysisMethodologyThequestionnairewasdesignedtoinvestigatetheimpactofservicerobotqualityonconsumers’utilizationintentionsinthehospitalityindustry.Drawingonpriorliterature,constructssuchasservicequalitydimensions(reliability,responsiveness,assurance,empathy,tangibles),behavioralintentions,andtrustbarrierswereoperationalizedintomeasurableitems.Thequestionnairecomprisedfivesections:A)DemographicInformation:Includinggender,age,educationlevel,annualincome,travelfrequency,andpriorexperiencewithhotelservicerobots(Q1–Q7).B)ServiceQualityEvaluation:A5-pointLikertscale(1=StronglyDisagree,5=StronglyAgree)wasadoptedtoassessperceptionsofrobotservicequalityacross13items(Q1–Q13),referencingSERVQUALdimensionsandstudiesontechnologyacceptance.C)BehavioralIntentions:FiveLikert-scalequestionsmeasuredwillingnesstoadoptrobotservices(Q1–Q5).D)AcceptanceBarriersandTrustEnhancement:Single-andmultiple-choicequestionsexploredconcerns(e.g.,privacy,safety)andfactorsinfluencingtrust(Q1–Q3).E)AdditionalComments:Anopen-endedquestionallowedqualitativefeedback.ThequestionnairewasdistributedonlineviaWeChatandREDplatformstargetingChinesetravelersandhotelcustomers.DatacollectionoccurredbetweenJanuaryandApril2025.Outof330responsesreceived,300validquestionnaireswereretainedafterexcludingincompletesubmissions,repetitivepatterns,andentrieswithcompletiontimesunder1minute.SPSS26willbeutilizedtoconductamultiplelinearregressionanalysis,whichisappropriateforevaluatingtheinfluenceofmultipleindependentvariables(SERVPERFdimensions)onasingledependentvariable(consumerintention).Thismethodprovidesinsightsintotherelativesignificanceofeachservicequalitydimensionandquantifiestheircontributionstoconsumerintentions.Thefollowinganalysisstepswillbetaken:A)Reliabilityandvalidityanalysis:Toassessreliability,Cronbach’sαwillbeutilized,andtoevaluatethesuitabilityofthedataforexploratoryfactoranalysis,KMOandBartlett’stestwillbeconductedtoanalyzevalidity.B)Descriptiveanalysis:Toanalyzedemographicinformationanddifferencesindemographicvariablestounderstandthecharacteristicsofconsumers.C)Correlationanalysis:TostudycorrelationbetweenvariablesbyPearsoncorrelationcoefficient.D)Regressionanalysis:TotestthehypothesisofwhetherthereisapositiverelationshipBetweenservicequalitydimensionsandconsumerintention,andalsotodeterminetheinfluencefactors.Part4Results4.1AnalysisofReliabilityandValidity4.1.1ReliabilityTestTable4CronbachReliabilityAnalysisDimensionNameTotalItem-TotalCorrelationAlphaifItemDeletedDimensionReliabilityOverallReliability0.75Q10.6-0.750.874Q20.6-0.735Q30.584-0.735Q40.584-0.83Q50.7330.7190.83Q60.6530.798Q70.6830.7690.859Q80.770.7690.859Q90.7260.809Q100.7090.8240.843Q110.7140.7860.843Q120.7060.786Q130.7210.7730.892Q140.7830.8580.892Q150.6970.878Q160.7350.869Q170.7840.858Q180.6840.881Inthissurvey,theCronbach'sAlphacoefficientsforalldimensionswerehigherthan0.7,indicatingthattheitemsinthequestionnairedemonstrategoodconsistencywhenmeasuringthesameconcept.4.1.2.ExploreFactorAnalysisTable5KMOandBartlett'sTestKMO0.831Bartlett'sTestofSphericityApproximateChi-Square2577.632df153p

0.000Table6VarianceExplainedFactorNumberEigenvalueVarianceExplained(%)VarianceExplained(%)(Rotated)EigenvalueVarianceExplained(%)Cumulative(%)EigenvalueVarianceExplained(%)Cumulative(%)EigenvalueVarianceExplained(%)Cumulative(%)15.82532.36232.3625.82532.36232.3623.54019.66619.66622.11511.74844.1102.11511.74844.1102.35113.06032.72631.7339.62853.7391.7339.62853.7392.32512.91645.64241.5808.77862.5171.5808.77862.5172.26512.58158.22351.3067.25769.7741.3067.25769.7741.5918.83967.06261.0926.06875.8421.0926.06875.8421.5808.78075.84270.5362.98078.82280.4952.74981.57190.4882.71084.280100.4252.36486.644110.3892.16088.804120.3762.08890.892130.3341.85492.746140.3211.78394.529150.2971.65296.181160.2471.37297.553170.2321.28798.840180.2091.160100.000TheresultsshowthattheKMOvalueforthissurveyis0.831,whichmeetstherequirementsforexploratoryfactoranalysis.Usingthemaximumvariancemethod,thisstudyextracted6commonfactorswitheigenvaluesgreaterthan1,andthecumulativeexplainedvariancereached75.842%,indicatingahighexplanatorypowerofthedataforthequestionnaire.ThestudyemployedHarman'ssingle-factortesttocheckforcommonmethodbias.Ifthevarianceexplainedbythefirstfactorexceeds50%,itsuggeststhepresenceofsignificantcommonmethodbiasinthedata.Anunrotatedexploratoryfactoranalysiswasconducted,andthevarianceexplainedbythesinglefactorwas32.362%,whichdidnotexceedthe50%thresholdfortesting.Therefore,itcanbeconcludedthatthereisnosignificantcommonmethodbiasinthesurveydata.4.2DescriptiveAnalysis4.2.1DemographicProfileDescriptivestatisticsofthecollecteddatawereanalyzedusingSPSS26.0,focusingondemographicvariables.Theresultsarepresentedbelow:Table7DemographicProfileVariableCategoryFrequencyPercentage(%)CumulativePercentage(%)GenderMale13645.3345.33Female13545.0090.33Other299.67100.00Age18-24years8628.6728.6725-34years11939.6768.3335-44years5618.6787.00≥45years3913.00100.00EducationHighschoolorbelow7023.3323.33Bachelor’sdegree17959.6783.00Master’sorabove5117.00100.00AnnualIncome<¥500011538.3338.33¥5000-1000008729.0067.33¥100000-2000006521.6789.00≥¥2000003311.00100.00TravelFrequencyRarely9030.0030.00Occasionally12341.0071.00Frequently8729.00100.00RobotUsageExperiencesYes12140.3340.33No17959.67100.00Basedontheresultsofthefrequencyanalysis,thedistributionofgendershowsthat45.33%ofthesampleismale,45.00%isfemale,and9.67%identifiesasothergenders,indicatingarelativelybalancedgenderrepresentationinthesample.Regardingage,thegroupaged25-34yearsconstitutes39.67%,whilethe18-24yearsgroupaccountsfor28.67%.Theproportionsofotheragegroupsarerelativelysmaller,withthegroupaged45yearsandabovecomprising13.00%.Intermsofthehighestlevelofeducation,59.67%ofrespondentsholdabachelor'sdegree,followedby23.33%withahighschooleducationorlower,and17.00%haveamaster'sdegreeorhigher.C