ROBOTSOFTHEFUTUREARECOMING,AREYOUREADY?

Astudyinvestigatingconsumers’acceptanceofrobotics

DEBOER,WILMA

ÅSTRÖM,JENNY-MARIA

SchoolofBusiness,Society&EngineeringCourse:MasterThesisinBusinessAdministrationCoursecode:EFO704Credits:15

Supervisor:CeciliaLindhCoassessor:UlfAnderssonDate:2017/06/05

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AcknowledgementsWritingthemasterthesishasbeenaninterestingjourneyforus,includingbothupsanddowns(mostlyups)!Wewouldliketothankourfamilyandfriendsforsupportingus,aswellasourgreatsupervisorCeciliaLindhforalwaysbeingsosupportiveandpositiveduringthewholeprocess.WilmawouldliketothankeveryonebackintheNetherlandswhosupportedherduringthemasterprocess.Jenny-MariawouldliketogivespecialthankstohersuperawesomefriendJenniferforcontinuallyremindingaboutwhatreallymatters!

WilmadeBoerJenny-MariaÅström

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Abstract

Date: 2017/06/05

Level: MasterthesisinBusinessAdministration,15credits

Institution: SchoolofBusiness,SocietyandEngineering,MälardalenUniversity

Authors: WilmadeBoer Jenny-MariaÅström

(91/10/29) (87/06/30)

Title: Robotsofthefuturearecoming,areyouready?

Tutor: CeciliaLindh

Keywords: Robotics,technologyacceptance,futurerobots,trust,innovation,UTAUT

Researchquestions: - How can consumer acceptance of robots be studied as an international

phenomenon? -Whataretheimplicationsofrobotacceptanceforconsumers?

Purpose: Thepurposeofthisstudyistoinvestigatetheacceptance-levelinternationally,becauseofthefutureincreaseofnewtechnologyintheformofrobotics.

Method: Aquantitativeresearchmethodwasconductedinthisstudy.Thedatacollectionwasdonebyasurvey,vianon-probabilitysampling.

Conclusion: Thefindingsofthisstudyshowthattrust,anxietyandpersonalinnovativenessinfluencetheacceptanceofrobotsinternationally,whilesocialinfluencedoesnotaffecttheacceptanceofthenewtechnologyinthecontextofrobotics.

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TableofContentListofAbbreviations...............................................................................................................................IX

ListofTables...........................................................................................................................................IX

ListofFigures.........................................................................................................................................IX

1.Introduction........................................................................................................................................1

1.1ProblemStatement.......................................................................................................................2

1.2Purpose.........................................................................................................................................2

1.3ResearchQuestions.......................................................................................................................2

2.LiteratureReview................................................................................................................................3

2.1TechnologyAcceptance.................................................................................................................3

2.1.1TheoryofReasonedAction(TRA)...........................................................................................3

2.1.2TheoryofPlannedBehaviour(TPB)........................................................................................3

2.1.3TechnologyAcceptanceModel(TAM)...................................................................................3

2.1.4InnovationDiffusionTheory(IDT)..........................................................................................4

2.1.5CombinedTAMandTPB(C-TAM-TPB)...................................................................................4

2.1.6ModelofPCUtilization(MPCU).............................................................................................4

2.1.7MotivationalModel(MM)......................................................................................................4

2.1.8SocialCognitiveTheory(SCT).................................................................................................5

2.1.9UnifiedTheoryofAcceptanceandUseofTechnology(UTAUT)............................................5

2.2VariableswithintheUTAUT-model...............................................................................................5

2.2.1Performanceexpectancy........................................................................................................5

2.2.2Effortexpectancy...................................................................................................................6

2.2.3Socialinfluence.......................................................................................................................6

2.2.4Behaviouralintention.............................................................................................................7

2.3ExternalVariables..........................................................................................................................7

2.3.1Trust.......................................................................................................................................7

2.3.2Anxiety....................................................................................................................................8

2.3.3Personalinnovativeness.........................................................................................................8

2.4ConceptualModel.........................................................................................................................9

3.Methodology.....................................................................................................................................10

3.1Chosentheoreticalframework....................................................................................................10

3.2Researchstrategy&design.........................................................................................................10

3.3Datacollection.............................................................................................................................10

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3.3.1Survey...................................................................................................................................10

3.3.2Sample..................................................................................................................................11

3.3.3Descriptionofdata...............................................................................................................11

3.4Operationalisation.......................................................................................................................12

3.5Reliabilityandvalidity.................................................................................................................13

3.5.1Reliability..............................................................................................................................13

3.5.2Validity..................................................................................................................................14

3.6Analysisofdata...........................................................................................................................14

3.6.1Limitationsoftheanalysis....................................................................................................15

4.Analysis..............................................................................................................................................16

4.1Externalvariablesinfluencingperformanceexpectancy.............................................................16

4.2Externalvariablesinfluencingeffortexpectancy........................................................................17

4.3Variablesinfluencingbehaviouralintention...............................................................................18

4.4Discussion....................................................................................................................................20

5.Conclusion.........................................................................................................................................23

5.1Futureresearchandlimitations..................................................................................................24

Referencelist...........................................................................................................................................X

Appendix..............................................................................................................................................XV

A.Operationalisation.......................................................................................................................XV

B.Descriptivestatistics......................................................................................................................XX

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ListofAbbreviationsTRA TheoryofReasonedActionTPB TheoryofPlannedBehaviourTAM TechnologyAcceptanceModelIDT InnovationDiffusionTheoryC-TAM-TPB CombinedTechnologyAcceptanceModelandTheoryofPlannedBehaviourMPCU ModelofPCUtilizationMM MotivationalModelSCT SocialCognitiveTheoryUTAUT UnifiedTheoryofAcceptanceandUseofTechnologySEM StructuralEquationsModelling

ListofTablesTable1-Countryoforiginspreadofthesample..................................................................................12Table2-Agespreadofthesample.......................................................................................................12Table3-Genderspreadofthesample.................................................................................................12Table4-Cronbach´sAlfa......................................................................................................................13Table5-Correlationanalysis................................................................................................................14Table6-Test1regressionanalysis.......................................................................................................16Table7-Test2regressionanalysis.......................................................................................................17Table8-Test3regressionanalysis.......................................................................................................18Table9-Overviewhypotheses.............................................................................................................19

ListofFiguresFigure1-Conceptualmodel...................................................................................................................9Figure2-Conceptualmodelshowingsupportedandnotsupportedhypotheses...............................19

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1.IntroductionAlmost100yearsagorobotswereintroducedtotheworld.AccordingtoSicilianoandKhatib(2016),theword“robot”isderivedfromtheSlavlanguage,whichmeans‘subordinatelabour’.ThetermwascoinedbythesciencefictionauthorKarelČapek,whointhe1920’sdevelopedthewordforatheatrepiece,wheremechanicalbeings,calledrobots,wereabouttotakeovertheworld(ibid).Sincethenrobotshavebeenusedinsciencefiction.Thefirstpracticaluseofarobotwasindustrialandhasbeenused since the 1960’s for production at manufacturing companies, also called the “robot-basedautomationrevolution”(Boesl&Liepert).Theserobotswerefastandefficient,butalsothreatening.Inordertoguaranteethesafetyoftheemployees,therobotswereoperatingwithincages.Thesecondrobotrevolutioniscurrentlybreakingthroughthisseparation,betweenemployeesandrobots,withsensitiveandsaferobot-basedautomationsolutions(ibid).Eventhoughthesecondrevolutionisnotestimatedyet,anewdimension isalreadypredicted, thus leading theserobots tobecomemobile.Theywillbeabletomovetotheobjectinsteadoftheotherwayaround.Thelastpredictedrevolutionisthatrobotswillbeabletoperceivetheirsurroundingsandmaybeeventounderstandthem(ibid).Thesefourrevolutionsareoverlappingandthenewrobotswillcomplementtheoldrobots.Sincetherearealotofdifferentvariationsandusagesoftherobot,theworldissaturatedwithseveraldefinitions.TheInternationalFederationofRoboticshascreatedtwoexplanations,dividedintoservicerobotsandindustrialrobots,whichtogether,accordingtoWilson(2015)providethedefinition‘Artificiallycreatedsystemdesigned,built,andimplementedtoperformtasksorservicesforpeople.’

The market segments where robotics is used range frommanufacturing, agriculture, transport &logisticstocivil,commercial,healthcareandconsumerdomains(TPRE,2015).Durakbasa,Bauer,Bas&Kräuter(2016)statethatthebusinessesthatwishtosurviveintoday'scompetitivemarketneedtomanagetheexpectationsoftheconsumers.Manufacturingbusinessesmustincorporatenewwaysofconstructing products to meet the increased levels of demand from both industrial and privateconsumers.Itisenvisionedthatautomationbasedsystemswillcreatefunctionsthatarecollaborative,autonomousandself-organisingamongothers(ibid).Durakbasaetal.(2016)statethatthegrowthanddevelopmentwithintheindustrialenvironmentisanongoingprocess.

The development of technology appears in the whole society, where challenges are being faced.AccordingtoresearcherPeterSiljerud,53percentofthe jobs inSwedenwillbereplacedbydigitaltechnologywithin20 years (Johansson, 2017).Robotswill beused for repetitive tasks that canbeautomated, within all industries. The Swedish foundation for strategic research states, thatapproximately90percentofthepeopleworking intheservice industrysuchascashiers,aswellaspeopleassistingtherestaurants,willbereplacedbyrobots(OttoUurisman,2016).ProfessorDanicaKragicJensfelt,whoisinvolvedinresearchofroboticsatKTH,believesthatrobotsmayevenbebetteratperformingsomeofthetasksthathumansperceiveasboring,hardanddangerous(Svenberg,2017).Arobotthatisprogrammedtoexecuteacertaintask,willmakethesametaskintheexactsamewayeverytime,withoutgettingbored.However,Prof.Jensfeltdoesnotbelievethattherobotswill“takeover”jobs.Instead,theywillworkside-by-sidewithhumans,enablingpeopletofocusontaskswherecreativityandsocialskillsareneeded(ibid).Whatisstillunknown,intheopinionofProf.Jensfelt,ishowpeoplewillreacttothenewtechnology.

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Inadditiontotheexpectedincreaseofrobotswithintheworkingenvironment,isalsotheuseofrobotswithinthehomeenvironment.Humansareexpectedtoworkandlivelonger,thusneedingsupportbothwithin theworking environment, in their homes andwhen becoming older (Boesl& Liepert,2016). However, current research show that people seem considerably cautious to accept socialbehavioursofrobotsintheirhomes(deGraaf,Allouch&vanDijk,2017).Previousstudieshaveshownthatanxiety,trustandpersonalinnovativenesscanaffecttheacceptanceofnewtechnology,suchasrobotics (Bröhl,Nelles,Brandl,Mertens&Schlick,2016;deGraafetal.,2017).Anxiouspeoplefeelconcernedaboutpossibleobstaclesthatmayoccurinthefuture(Sarason,1984),whiletruststressesthewillingnesstotakerisks(Mayer,Davis,&Schoorman,1995).Personalinnovativeness,ontheotherhand,involvesthewillingnessofanindividualtotryoutnewtechnology(Agarwal&Prasad,1998).

1.1ProblemstatementKaivo-oja,Roth,&Westerlund(2016)statethatthedevelopmentofhumanisationandrobotisationare influencing thehuman future.Aspresented in thepreviousparagraphs, the fieldof robotics isgrowingtremendously,butthere isstilluncertainty inthesociety. It is impossibletoanticipatethefutureindetail,however,itispredictedthatnewmarketswillarisebecauseofthenewtechnologicaldemand(Boesl&Liepert,2016).AccordingtotheTPRE(2015),therearedifferentbarriersarisinginseveralindustries,whichhavetobeovercomeinordertohaveasuccessfulintegrationofroboticsinsociety.Kaivo-ojaet al., (2016) states that therewill be regionaldifferenceswhen it comes to theusability of technical solutions. Our future depends on how well we use technologies and takeadvantageofinnovationsandtechnicalopportunities.Forinnovationstoevolve,thedemandforthenewtechnologyisasimportantasthecreationofit(Boesl&Liepert,2016).Becauseoftheincreaseofroboticsusedintheeverydayenvironment,deGraafetal., (2017)researchedtheacceptanceofdomesticsocialrobots intheNetherlands.Basedonthestudy, it issuggestedthatan internationalstudyofrobotacceptancewouldgiveamoregeneralviewonopinionsoffuturerobotics.Inaddition,Boesl&Liepert(2016)statethatdifferenttechnologicalneedsareexpectedtorisesincetheglobalpopulationwillexceed10billionbytheyear2060.Incombinationwiththegrowthoftheroboticfield,aninternationaldatasetcancontributetothecurrentlyavailableresearch.Sinceroboticsisaglobalphenomenon,thestudyshouldnotbelimitedtoonespecificcountry.

1.2PurposeThepurposeofthisstudyistoinvestigatetheacceptance-levelinternationally,becauseofthefutureincreaseofnewtechnologyintheformofrobotics.

1.3Researchquestions• Howcanconsumeracceptanceofrobotsbestudiedasaninternationalphenomenon?• Whataretheimplicationsofrobotacceptanceforconsumers?

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2.LiteratureReviewInthischapter,aliteraturereviewoftechnologyacceptanceispresented.First,thedevelopmentoftheUTAUTmodeland its variablesarepresented, followedbyanexplanationof theexternal variablesanxiety, personal innovativeness and trust which according to previous research, can affect theacceptanceoftechnology.

2.1TechnologyAcceptanceOye,Iahad&Ab-Rahim(2014)statedthattechnologyisonlyvaluablewhenitisacceptedandused.Henceanunderstandingoftechnologyacceptanceisimportantasacceptanceisthekeytothesuccessorfailureofanewtechnology(Dillon&Morris,1996).Technologyacceptancecanbeexplainedasthefunctionofuserinvolvementintechnologyuse(Samaradiwakara&Gunawardena,2014).Thereisahighcuriosityamongresearcherstounderstandhowuserswillrespondtonewtechnologiesandhowthis can be improved (Dillon & Morris, 1996). Therefore, different kinds of models have beendevelopedtoexplain,predictandcontrolthetechnologyacceptancephenomena(Burch,2003).

2.1.1TheoryofReasonedAction(TRA)ThefirstmodelwhichwasacknowledgedistheTheoryofReasonedAction(TRA)developedbyFishbeinandAjzen(1967).Themodelhasoriginallybeenusedintheresearchfieldofsocialpsychology.Itisdevelopedtopredictthevoluntarybehaviourofhumanbeingsandhelpstoexplainthephysiologicalelements.AccordingtoAjzen(1985),thenameofthetheory,reasonedaction,derivesfromthefactthatpeoplebehaveinasensiblewaybykeepinginternalandexternalinformationavailableintheirmindwhendecidingwhetherornot to takeaction. Themodelmakesa clear connectionbetweenattitude and behavioural intention. Behavioural intention is based on two components; attitudetoward the behaviour and the subjective norm (ibid). The first component is described as theindividual’s positive or negative evaluation of performing the behaviour, while the second oneaddressestheperson’sperceptionofthesocialpressureputonhim/hertoperformornottoperformthebehaviourinquestion.

2.1.2TheoryofPlannedBehaviour(TPB)The Theory of Planned Behaviour, developed by Ajzen (1985), is a response to the TRAmodel. Alimitation of the TRAmodel is that it does not take the irrational and unconscious decisions intoconsideration. Therefore, the TPBmodel has been developed, including the component perceivedbehaviouralcontroltoexplainbehaviour.AccordingtoAjzen(1985),thecomponentcomplementsthetwocomponentsfromtheTRAmodel;attitudetowardthebehaviourandsubjectivenorm.Perceivedbehaviouralcontrolexplains‘people’sperceptionoftheeaseordifficultyofperformingthebehaviourofinterest’(ibid).

2.1.3TechnologyAcceptanceModel(TAM)TheTechnologyAcceptanceModel(TAM)isdevelopedbyDavis(1986)andoriginatesfromtheTRAmodel.Thismodel isknownfor thetwotechnicalcomponentsperceivedusefulnessandperceivedease of use, that influence the user’s intent to use a new information system. According toDavis(1986),perceivedusefulnessexplains‘thedegreetowhichanindividualbelievesthatusingaparticularsystemwouldenhancehisorherjobperformance’,whileperceivedeaseofuseisdescribedas‘thedegreetowhichan individualbelievesthatusingaparticularsystemwouldbefreeofphysicaland

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mentaleffort’(ibid).TheTAMmodelisnowadaysusedasabasicmodelwhereseveralextensionshavebeendeveloped,forexample,TAM2andTAM3.

2.1.4InnovationDiffusionTheory(IDT)Theeaseofuse fromtheTAMmodelalsoappeared inearlier research in the InnovationDiffusionTheory,developedbyRogers(2010).Aninnovationcanbedefinedassomethingthatisperceivedasnewbyanindividualorasocialsystem.Diffusion,ontheotherhand,isexplainedas‘theprocessbywhichaninnovationiscommunicatedthroughcertainchannelsovertimeamongthemembersofasocialsystem’(ibid).Thistheoryshowstheinnovation-decisionstagesfromtheinventiontothewideuseofanewtechnology,aswellasthedifferencesamongcategoriesofadopters(DillonandMorris,1996).AccordingtoRogers(2010),peoplewillgothroughfivestagesbeforetheyacceptaninnovation;knowledge, persuasion, decision, implementation and confirmation. In addition to the five stages,therearefivecharacteristicsoftechnologywhichalsoinfluencetheacceptance;relativeadvantage,compatibility, complexity, trialability and observability (ibid). Finally, there are also five differentadopter categories: innovators, early adopters, earlymajority, latemajority and laggards (Rogers,2010;Lee,Hsieh&Hsu,2011).Insummary,theIDTmodeldescribeshowuserscreatebeliefsaboutcharacteristicsofaninnovation,whichisthebasisforiftheinnovationisadoptedorrejectedbytheuser(Agarwal,2000).

2.1.5CombinedTAMandTPB(C-TAM-TPB)Asthenameimplies,combinedTAMandTPCB,isthemodelcombiningthetwomodels‘TechnologyAcceptanceModel’and‘TheoryofPlannedBehaviour’.Taylor&Todd(1995)createdittocoverupthelimitationsoftheTAMmodel.TheTAMmodellacksthecomponentssocietyandcontrolwhichhaveaproveneffectonactualbehaviour.TheTPBmodelcoversthetwocomponents,thusintegratedintothe C-TAM-TPB model. The model is suitable for understanding users’ behaviours of using newtechnologies (ibid). Another noticeable characteristic is that the model distinguishes betweenexperiencedandinexperiencedusers(Jen,Lu&Liu,2009).

2.1.6ModelofPCUtilization(MPCU)TheModelofPCUtilizationisbasedontheTheoryofHumanBehaviourcreatedbyTriandis(1977).ThebothmodelsaresimilartotheTheoryofReasonedAction.TheMPCUmodeldiffersinthewaythatthemodelmakesadistinctionbetweencognitiveandaffectiveelementsofattitudes.Behaviouralintention has a central role and consist out of attitude, social norms, habits and the expectedconsequenceofthebehaviour(ibid).Accordingtothemodeltherearesixcomponentswhichinfluencebehaviouralintention,namelyjob-fit,complexity,long-termconsequences,affecttowardsuse,socialfactorsandfacilitatingconditions(Thompson,Higgins&Howell,1991).

2.1.7MotivationalModel(MM)The Motivational Model has been developed by Davis, Bagozzi & Warshaw (1992), based onpsychological aspects. With the MM model, the motivation for behaviour can be explored byaddressing intrinsic and extrinsicmotivations (Davis et al., 1992). According to Venkatesh,Morris,Davis&Davis,(2003)variablessuchascomplexity,effectofuseandsocialfactors,derivedfromtheModelofPCofUtilization,canbeusedtopredictbehaviouratanindividuallevel.

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2.1.8SocialCognitiveTheory(SCT)Thelastmodel,whichcontributestotheUTAUTmodel,istheSocialCognitiveTheory(Bandura,1986).Themodelhasbeenusedinthesociologicalfield,focusingonself-efficiencywhichisdefinedas‘thejudgmentofone'sabilitytouseatechnologytoaccomplishaparticularjobortask’(Bandura1986;Compeau&Higgins,1995).Individualsdothisbytakingarolemodelwithintheirsocialenvironmentasanexampleandtrytoreproducetheactionsofthismodelastheirownbehaviour.Bandura(1986)describesthereproductionoftheobservedbehaviourasaninterplaybetweenpersonal,behaviouralandenvironmentalelements.

2.1.9UnifiedTheoryofAcceptanceandUseofTechnology(UTAUT)Toharmonisetheabovepresentedliteratureinthecontextoftechnologyacceptance,anewmodelhasbeendevelopedbyVenkatesh,Morris,Davis&Davis(2003).ThemodelunifiesthedifferencesandsimilaritiesofalltheeightpreviouspresentedmodelsandiscalledtheUnifiedTheoryofAcceptanceandUse of Technology (UTAUT). Venkatesh et al. (2003) integrated themost significant elementsacrosstheseeightmodelsintotheUTAUTmodel.Itaimstounderstandtheacceptancelevelandusageof a new technology. According toWilliams, Rana& Dwivedi (2015) research has shown that theindependentvariablesofthemodelexplain70percentofthevarianceofbehaviouralintention.Thisexceedsalleightmodelsthatexplain17to53percentofthevarianceofbehaviouralintention(ibid).

According to Venkatesh et al. (2003), the UTAUT model exists out of three main components;performance expectancy, effort expectancy and social influence that have a direct influence onbehaviouralintention.Thelastmaincomponent,facilitatingconditions,influencesusagebehaviour.Themaincomponents,whichareaffectingbehavioural intention,areinfluencedbythemoderatingfactors,age,experience,genderandvoluntarinessofuse(ibid).

TheUTAUTmodelisthemaininspirationfortheconceptualmodelofthisresearchsinceitincludesthemostsignificantelementsoftheeightmodelsandhasthebiggestpredictivepowerforbehaviouralintention(Williamsetal.,2015).Behaviouralintentionhasacentralroleinthisresearchandthereforetheindependentvariablesthathaveadirectinfluenceonbehaviouralintentionhavebeenusedintheconceptualmodel,namelyperformanceexpectancy,effortexpectancyandsocialinfluence.InadditiontotheindependentvariablesoftheUTAUTmodel,externalvariablesconfirmedbypreviousresearch,havebeenaddedtocompletetheconceptualmodelforthisresearch.

2.2VariableswithintheUTAUT-model2.2.1PerformanceexpectancyPerformanceExpectancyisdefinedbyVenkateshetal.(2003)as‘thedegreetowhichanindividualbelievesthatusingthesystemwillhelphimorhertoattaingainsinjobperformance.’Theperformanceexpectancy of new technology has been studied in previous research,within different technologycontextsandcountries.Carlsson,Carlsson,Hyvonen,Puhakainen&Walden(2006),havestudiedtheadoptionofmobiledevicesinFinland,Kijsanayotin,Pannarunothai&Speedie(2009)haveinvestigatedthe information technology adoption in Thailand,whileAbuShanab, Pearson& Setterstrom (2010)haveresearchedaboutInternetbankinginJordan.

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Performance expectancy has a direct influence on behavioural intention and is the result of fivevariablesoutoftheeightacceptancemodels,namelyperceivedusefulness(TAM),extrinsicmotivation(MM), job-fit (MPCU), relative advantage (IDT) and outcome expectations (SCT) (Venkatesh et al,2003).When researching the user adoption ofmobile banking, findings showed that performanceexpectancy had a significant effect on user adoption behaviour, which can be described as thebehaviouralintention(Zhou,Lu&Wang,2010).ThesamewasfoundbyYu(2012)whenresearchingaboutwhatfactorsthatcouldaffectindividualstousemobilebanking.Regardingtheacceptanceofrobotics,Heerink,Kröse,Evers&Wielinga(2010)foundthatperceivedusefulnessaffectedintentiontousewhenstudyingtheacceptanceofassistivesocialrobotsbyelderlyusers.

Basedonpreviousresearchpresentedabove,thefollowinghypothesisisdeveloped:• H1:Performanceexpectancyhasapositiveinfluenceonbehaviouralintentionwithinthe

acceptanceofrobotics.

2.2.2EffortexpectancyVenkateshetal.(2003)defineeffortexpectancyas‘thedegreeofeaseassociatedwiththeuseofthesystem’,whichhasadirect influenceonbehavioural intention.ThisvariableoftheUTAUTmodel isalsobasedonseveralvariablesfrompreviousacceptancemodels,namelyperceivedeaseofuse(TAM),complexity (MPCU)andeaseofuse(IDT)(ibid).Theeffortexpectancyofnewtechnologyhasbeenstudiedinseveraltechnologycontextsandcountries,byseveralresearchers.AbuShahabetal.,(2010)haveresearchedtheInternetbankinginJordan,Carlssonetal.,(2006)havestudiedtheadoptionofmobileservicesinFinland,Foon&Fah(2011)haveinvestigatedtheInternetbankingadoptioninKualaLumpur,whileXu&Gupta(2009)havestudiedtheacceptanceoflocation-basedservices.

The influenceof effort expectancy onbehavioural intention has been strengthenedbyVenkatesh,Thong&Xu(2012)whenresearchingtheacceptanceofmobileInternetusage.Findingsalsoshowedthat perceived ease of use affected intention to use, when Heerink et al., (2010) researched theacceptanceofassistivesocialrobotsbyelderlyusers.

Basedonpreviousresearchpresentedabove,thefollowinghypothesisisdeveloped:• H2:Effortexpectancyhasapositiveinfluenceonbehaviouralintentionwithintheacceptance

ofrobotics.

2.2.3SocialinfluenceThelastvariableoftheoriginalUTAUTmodelwhichhasadirectconnectiontobehaviouralintentionissocialinfluence.IthasbeendefinedbyVenkateshetal.(2003)as‘thedegreetowhichanindividualperceivesthatimportantothersbelieveheorsheshouldusethenewsystem.’Thisvariableisbasedonthethreevariables,subjectivenorm(TRA,TAM,TPBandC-TAM-TPB),socialfactors(MPCU)andimage (IDT) (ibid). Social influence has been researched in new technology contexts, such as theInternetbankingadoptioninKualaLumpur(Foon&Fah,2011).

Whenresearchingtheuseradoptionofmobilebanking,findingsshowedthatsocialinfluencehadasignificanteffectonuseradoptionbehaviour,whichcanbedescribedasbehaviouralintention(Zhouetal.,2010).ThesamewasfoundbyYu(2012)whenresearchingaboutwhatfactorsthatcouldaffectindividualstousemobilebanking.

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Basedonpreviousresearchpresentedabove,thefollowinghypothesisisdeveloped:• H3:Socialinfluencehasapositiveinfluenceonbehaviouralintentionwithintheacceptanceof

robotics.

2.2.4BehaviouralintentionBehavioural intention has a positive effect on the usage of technology, being directly affected byperformance expectancy, effort expectancy and social influence (Venkatesh et al., 2003). Thebehaviouralintentionregardingnewtechnologyhasbeenstudiedindifferenttechnologycontextsandcountries,byseveralresearchers.AbuShahabetal.,(2010)haveresearchedtheInternetbankinginJordan, Foon & Fah (2011) have investigated the Internet banking adoption in Kuala Lumpur,Sambavisan,Wemyss,&Rose(2010)studiedtheintentiontouseelectronicprocurementsystemsinMalaysia,Kijsanajotinetal.,(2009)haveresearchedtheinformationtechnologyadoptioninThailand,whileXu&Gupta(2009)havestudiedtheacceptanceoflocation-basedservicesinSingapore.

AccordingtoZeithaml,Berry&Parasuraman(1996),customerbehaviouralintentionsmayleadtoanactualpurchaseofaservice,whileMittal,Kumar&Tsiros(1999)measureitasacustomer's’intentiontorecommendaproductorservice.

2.3Externalvariables2.3.1TrustMayeretal.,(1995)definetrustas‘thewillingnessofapartytobevulnerabletotheactionsofanotherpartybasedontheexpectationthattheotherwillperformaparticularactionimportanttothetrustor,irrespectiveoftheabilitytomonitororcontrolthatotherparty’(ibid.,pp712).Inotherwords,trustisawillingnesstotakerisks.Morgan&Hunt(1994)statethatcommitmentandtrustarekeyfactorsinrelationshipmarketing, creating co-operative behaviour thatwill lead to success. It is a dominantelementofrelationshipcommitment,thusleadingtothewillingnesstorelyonanotherparty.Morgan&Hunt(1994)foundthatcooperationisdirectlyinfluencedbybothcommitmentandtrust.

The trust of new technology has been studied in several technology contexts and countries.Sambavisanetal. (2010) studied the intention touseelectronicprocurement systems inMalaysia,whileWeiss,Bernhaupt,Tscheligi,Wollherr,Kuhnlenz&Buss(2008)haveresearchedtheacceptanceofhuman-robotinteractionincludingthevariableoftrustintheirresearch.

AbuShanabetal.,(2010)foundthattrustaffectsbehaviouralintentionwhenstudyingtheacceptanceofInternetbankinginJordan.Thefindingswereassumedtodependonthepotentialriskoflosingornothavingaccesstotherespondent’sfunds.WhenresearchingabouttheadoptionofInternetbankinginMalaysia,Foon&Fah(2011)foundthattrustcorrelatedwithandinfluencedbehaviouralintention.Thesameresultwasfoundwhenstudyingtheacceptanceofassistivesocialrobotsbyolderadults,whereacorrelationwasfoundbetweentrustandintentiontouse(Heerinketal.,2010).

DeGraafetal., (2017)realised,whenstudyingtheacceptanceofsocialrobots,thathavingasocialrobotinahomeenvironmentcouldincreasethefeelingoftrustiftheparticipantsofthestudybelievedthat theyhadskills to interactwith the robot. Inaddition,Torta,Werner, Johnson, Juola,Cuijpers,Bazzani&Bregman(2014)foundthatelderlywerewillingtotrustasmallsociallyassistiverobotwhen

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theysawthebenefitofusingit.Thetrustcoulddependonthefactthattherobotwasperceivedas“niceandsafe”duetoitssmallsize(ibid).

Basedonpreviousresearchpresentedabove,thefollowinghypothesisisdeveloped:• H4:Trusthasapositiveinfluenceonbehaviouralintentionwithintheacceptanceofrobotics.

2.3.2Anxiety‘Anxietyappearsfromthefearofbeingdeprivedofexpectedsatisfactionalthoughthere isnoclearevidence that the satisfaction will be deprived’ (Spielberger, 2013, pp 47). Anxious people feelconcerned about possible obstacles that may occur in the future. The worry can be both real orimagined,whichcanaffecttheirperformancenegatively(Sarason,1984).

Trusthasbeenusedas a variablewhen investigating acceptanceof several different kindsofnewtechnology. AbuShanab et al. (2010) have researched the acceptance level of Internet banking inJordan,addinganxietytotheUTAUTmodel.Thecorrelationswerenon-significant,whichmaydependonthefactthattherespondentswerenotfamiliarwiththetechnologyatthattime,thusnotbeingawareoftheriskswhichcouldhavecausedanxiety(ibid).Carlsson,etal.,(2006),ontheotherhand,found that anxiety influenced performance expectancywhen investigating the acceptance level ofmobiledevicesandservices inFinland.ThesamewasalsofoundbyHeerinketal. (2010)whentheacceptance of assistive social agents by elderly userswas studied, showing a correlation betweenanxietyandperceivedusefulness.

Whenresearchingtheacceptancelevelregardingcooperationbetweenrobotsandhumans,Bröhletal.,(2016)createdaresearchmodelbasedonliteratureandbycollaboratingwithpeoplewithintherobotic industry. Themodelwasdeveloped in aworkshop involving robotmanufacturers, usersofindustrialrobotsandemployersworkingwithrobots.FindingsshowthatanxietyaffectsperceivedeaseofuseintheTAMmodel,thusaffectingeffortexpectancyintheUTAUTmodel.

Basedonpreviousresearchpresentedabove,thefollowinghypothesesaredeveloped:• H5:Anxietyhasapositiveinfluenceonperformanceexpectancywithintheacceptanceof

robotics.• H6:Anxietyhasapositiveinfluenceoneffortexpectancywithintheacceptanceofrobotics.

2.3.3PersonalinnovativenessPersonal innovativeness has been studied in innovation diffusion research, showing that activeinformation seeking and less reliance on others opinions are usual characteristics of innovators(Rogers,1995).Inaddition,Agarwal&Prasad(1998)definepersonalinnovativenessasthewillingnessofanindividualtotryoutnewtechnology.Theystatethatpeoplearecharacterisedas“innovative”iftheyareearlyadoptersofinnovation.

The personal innovativeness regarding new technology has been studied in different technologycontextsandcountries.AbuShahabetal.,(2010)haveresearchedtheInternetbankinginJordan,whileXu&Gupta(2009)havestudiedtheacceptanceoflocation-basedservicesinSingapore,bothincludingtheaspectofpersonalinnovativenessintheirresearch.

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Hur, Yoo & Chung (2012) explored how consumer innovativeness influences the intention to buyrobotsusedathome.ThedatawascollectedinSouthKorea,wheretherobotshadrapidlypenetratedthemarketplace.Thefindingsshowedthatpeoplewhowere“highlyinnovative”weremoreaffectedbytheemotionalvalueoftherobot,whilepeoplewhowerelessinnovativefocusedonthefunctionalvalues(ibid).

Whenstudyinghowpersonalinnovativenessaffecttheadoptionofwirelessinternetservices,Lu,Yao&Yu(2005)foundthatbothperceivedusefulnessandperceivedeaseofusewereaffected.Inaddition,deGraafetal.(2017)found,whenstudyingsocialroboticsandhuman-robotinteraction,thatpeoplewhoseethemselvesasmoreinnovativeexpectthatusingasocialrobotinhis/herhome,wouldbemoreenjoyable,safe,expensiveandeasiertouse.

Basedonpreviousresearchpresentedabove,thefollowinghypothesesaredeveloped:• H7:Personalinnovativenesshasapositiveinfluenceonperformanceexpectancywithinthe

acceptanceofrobotics.• H8:Personalinnovativenesshasapositiveinfluenceoneffortexpectancywithinthe

acceptanceofrobotics.

2.4ConceptualmodelBasedontheliteraturereview,aconceptualmodelofthehypothesesispresentedinFigure1.Thehypothesesfollowtheflowofthedescriptivetextandargumentsofchapter2.2and2.3.

Figure1-Conceptualmodel

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3.MethodologyThis chapterwill present themethodologywithwhich the research questionswere answered. Thepurposeofthestudywastoinvestigatetheacceptancelevelofroboticssinceitisaglobalphenomenon.Below,theapproach,strategyanddesignoftheresearchprocessarepresented.

3.1ChosentheoreticalframeworkThetheorychosenforthisstudyistheUnifiedTheoryofAcceptanceandUseofTechnologydevelopedby Venkatesh et al., (2003), along with the external variables trust, anxiety and personalinnovativeness.TheUTAUTmodelhasbeenusedinpreviousresearchinseveraldifferenttechnologycontexts, which is why we chose it to be a basis for our conceptualmodel. In addition, previousresearchhasattachedexternalvariablestothemodel,whichinspiredustousethemaswell,whendevelopingtheconceptualmodel.

3.2Researchstrategy&designThescientificmethodwasdeductive,thustestingatheory(Bryman&Bell,2015),namelytheUTAUTmodel in an international context.Aquantitative research strategywas chosen,whichemphasisesquantificationinthecollectionandanalysisofdata(ibid).Inthisresearch,quantitativeanalysiswasused to discover correlations between different variables (Christensen, Andersson, Carlsson &Haglund,1998).Theresearchquestionswereansweredbyacreatedsurveywhichwasbasedonthechosentheoreticalframework.

Duringthe10-weeklongresearchprocess,wewerepartofagroupofonesupervisorandsixstudents,havingtheroboticsincommonasacontextinourstudies.Everythesisprojectwaswritteninpairs,creatingthreedifferentprojectswithinthecontextofrobotics.Thegrouphadseveralmeetings,oneincludingavisittoABBinVästerås,togivethestudentssomeinspirationaboutthetopic.Furthermore,asurveywascreatedtogetherwithinthegroup,withquestionssuitedforallthreeprojects.Itwassentbyallmembersofthegrouptorespondents,globally.

3.3Datacollection3.3.1SurveyPrimarydatawascollectedthroughasurveysentinternationally.ItwascreatedonanonlineplatformcalledSunetSurvey,towhichalinkwasconnected.Thedatacollectionperiodwasbetween7thand26thofApril2017,duringwhichthelinkwassenttorespondentsonsocialmediaandviae-mail.Theuseofself-completionsurveyswaschosenbecausetheyare,incomparisontostructuredinterviews,easierfortherespondentstoanswersincetheycanchoosewhentoanswerit(Bryman&Bell,2015;Björklund&Paulsson,2012).Ontheotherhand,weareawareofthenegativeaspects,suchastherespondentsnotbeingabletoaskfurtherquestions iftheydonotunderstandsomestatementsormightgetbored,thusstopansweringandquittingthesurvey(ibid).Toavoidthis,thestatementswereaskedasclearlyaspossibleafterbeingrevisedseveraltimes.

Forthisstudy,35itemswerecreatedandusedinthesurveyinordertocollectrelevantdata.Theitemsconsistedofstatementsbasedonthevariablesof theconceptualmodel;performanceexpectancy,effortexpectancy,socialinfluence,anxiety,trust,personalinnovativenessandbehaviouralintention.Theansweroptionsconsistedofa7degree-Likertscale,whichwasusedinorderfortherespondent

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toeasilyanswerhis/heropinionrangingfrom“Totallydisagree”to“Completelyagree”(Bryman&Bell,2015).Theoption“Don’tknow”/Doesn’tapply”wasadded,thuscreatingachancefortherespondentnot tobe forcedtoanswer if she/hedidnotunderstandthequestion.Additionally, threepersonalfactualquestionswereused,torevealtheage,genderandoriginoftherespondents.

Toavoiddatacollectionerror,whichmayarisebypoorquestionwording,thestatementsinthesurveywereformulatedasclearaspossible(Trost,2007).Weareawareofthefactthattherespondentswerenotfamiliarwithscientificalexpressionsusedwhenpresentingthetheoreticalframework.Therefore,statements were formulated in a mundane manner within the context of robotics, although stillmeasuringwhatthisstudy intendedtomeasure. Inaddition, itwastaken intoconsiderationthatasurvey should not be longer than needed to avoid the respondent from not finishing it (ibid). Toincreasetheresponserate,thelayoutofthesurveywastakenintoconsideration.AsBryman&Bell(2015)states,thelayoutofthesurveyshouldbeclearandunderstandable,includinganexplanationofthestudy.Inthebeginningofthesurvey,greetingsandashortexplanationofwhatitwasusedfor,werepresented.Additionally,therespondentswereinstructedtokeeptheirown“view”ofroboticsintheirmindwhen filling it in, in order to get their personal perception of robotics. The layoutwasneutral,usingneutralcolourswithasconsistentansweroptionsaspossible,makingiteasytoanswer.Itwascreatedinawaythatitcouldbeviewedononepage,therespondentnotneedingtoclickfurthertoseveralpagesinordertocompleteit.

3.3.2SampleNon-probability sampling was used since the survey was sent to respondents known by theresearchers, suchas friends, family, colleagues and classmates. In thisway, somemembersof thepopulation were more likely to be selected, thus affecting the fact that the findings can not begeneralised,butmayprovideabasisforfurtherresearch(Bryman&Bell,2015).Therespondentswereencouraged to share it further onwith people they knewwould be able to answer the survey, toincreasetheresponserateasmuchaspossible.Remindersweresentcontinuouslyduringtheperiodofdatacollection.Intotal,thesurveywassentto886peopleandansweredby514respondents.Theresponseratewasthus58percent,whichisconsideredtobeacceptable(Bryman&Bell,2015).

3.3.3DescriptionofdataTherespondentswereaskedtostatetheirageandgenderinthebeginningofthesurvey,tobeabletopresentthedemographicvarianceofthesample.Therangeofagewasdividedinto6categorieswith the additional option “Don’t want to say”. The options for stating the gender was “male”,“female”and“Prefernot tostatemygender”.Whenstating thecountryoforigin, the respondentcouldfillinthenameofthecountrybyhim/herself,beingtheonlyopen-endedquestioninthesurvey.AdocumentpresentingdescriptivestatisticsofeveryconstructcanbefoundinappendixB.

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Table1presentstherespondents’countryoforigin.42percentoftherespondentswerefromSwedenwhile21percentwerefromtheNetherlands.Manycountrieswererepresentedbythesampleofthisstudy,however,therestofthecountrieswererepresentedbylessthan5percentoftherespondents.0,6 percent did not state their country of origin in the survey. It is relevant tomention that thequestions askedwerenotmandatory. Inotherwords, the respondent could continue to fill in thesurvey,althoughshe/hedidnotanswersomeofthequestions.

Age Percentage18-26 59,1%27-36 20,8%47-56 8,0%37-46 7,8%57-66 3,3%67≥ 0,8%Don'twanttosay 0,2%

Table2-Agespreadofthesample(%)

Table2presentstheagecategoriesoftherespondents.59percentofthesampleare18-26yearsold,while21percentare27-36yearsold.Theageoftherestofthesamplevariedandwasspreadintherestoftheagecategories.

Gender PercentageMale 52,7%Female 44,7%

Table3-Genderspreadofthesample(0,6%unknown)

Table3showsthat55percentoftherespondentsaremaleand45percentarefemale.Noneoftherespondentschosetonotstatetheirgenderbut0,6percentofthesampledidnotcrossanyoftheoptionsconcerninggenderinthesurvey.

3.4OperationalisationTheoperationalisationof the researchquestions is presented in appendixA togetherwith a shortdescriptionofthechosentheoriesaswellasareferencetotheresearchfromwhichthestatements

Country PercentageSweden 42,2%TheNetherlands 20,6%Germany 4,9%Morocco 3,5%Spain 2,7%India 2,1%UnitedStates,CzechRepublic,France,Bulgaria,China,Finland,Italy,Luxembourg,Malaysia

>1<2%

Portugal,GreatBritain,Poland,Turkey,Austria,Bangladesh,Cyprus,Iran,Latvia,Mexico,Montenegro,Palestine,SouthAfrica,Australia,Belgium,Burundi,Denmark,Ecuador,Greece,Honduras,Jordan,Lithuania,Mauritius,Pakistan,SierraLeone,Somalia,Switzerland,Thailand,Ukraine

>0,1%<1%

Table1-Countryoforiginspreadofthesample(0,6%unknown)

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wereinspired.Questions1,2and3arepersonalfactualquestionsusedtodescribetherespondents’whohaveansweredthesurvey,byage,countryoforiginandgender.Ageandgenderareaskedtoseeifsomeagegroupsorgendersaremorepresentthanothersinthesample,whilecountryoforiginisneededtoshowtheinternationalaspectofthisresearch.Therestofthequestionsareaskedinordertodescribetherespondentsviewonthevariablesbasedonthetheoreticalframework;performanceexpectancy, effort expectancy, social influence, anxiety, trust, personal innovativeness andbehaviouralintention.

The variables aremeasuredwith statements created based on the characteristics of the theories,creatingconstructs(Churchill1979;Wisniewski,1994).Inthisresearch,thesevenconstructsconsistofavariedamountofstatementseach,dependingonwhatkindofstatementspreviousresearchhaveusedtomeasurethesametheoreticalconcepts.Thestatementsusedinsurveysofpreviousresearch,whereacceptanceoftechnologyhasbeeninvestigated,havebeenusedasinspirationforthisstudywhencreatingtheconstructs.Thestatementsofeveryconstructareintendedtocovergeneral,work-andpersonal lifeperspective, inordertogettherespondenttogivehis/hersopinionbasedontheeverydayexperienceoflife.

3.5ReliabilityandvalidityBelow,thereliabilityandvalidityofthisstudy,arediscussed.Reliabilityconcernstherepeatabilityofthestudywhilevalidityreferstothecredibilityofthefindings(Neale&Liebert,1973;Churchill,1979).

3.5.1ReliabilityReliabilityconcernsifthesameresultcanbeobtainedwhenconductinganewsimilarstudy.Awayoflowering threatsof reliability is touse severalquestionsbasedoneachvariableof the conceptualmodel (Churchill, 1979; Trost 2007). This was consideredwhen creating the items for the survey.Several statementswereused tomeasureone constructbasedon the variablesof the conceptualmodelandinspiredbystatementsusedinpreviousresearchwheretechnologyacceptancehasbeenstudied,indifferentcontexts.

Theinternalreliabilityofeveryconstructismeasuredtoseethatalloftheitemsmeasurethesamephenomena.BycalculatingtheCronbach’sAlpha,thedegreeofinternalreliabilityofeveryconstructcanbepresented.TheCronbach’sAlfameasurementisasensitivemethodofmeasuringtheinternalreliabilitysinceitisdependentonthenumberofitemsincludedintheconstruct.Analphaof0,7orhigherisconsideredtobeanacceptablelevel,althoughsomeresearchersstatethatlowernumbersmaybeacceptableaswell(Nunnally,1978).

Construct NofItems Cronbach’sAlfaPerformanceExpectancy 7 ,843EffortExpectancy 6 ,768SocialInfluence 6 ,714Anxiety 6 ,628Trust 4 ,695PersonalInnovativeness 2 ,801BehaviouralIntention 4 ,847

Table4-Cronbach´sAlfa

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Table4showsthatalloftheconstructshadanalphaover0,7,excepttheconstructsofanxietyandtrust, showing values closelyunder0,7.AsNunnally (1978)describes, some researchers accept analphalowerthan0,7,whichiswhytheconstructsofanxietyandtrustareconsideredreliableinthisresearch.

3.5.2ValidityValidityconcernsthecorrectnessofthestudybythedegreeofhowsuccessfullythemeasuresoftheresearchdomeasurewhatitisaimedtomeasure(Neale&Liebert,1973).Toincreasethevalidityofthesurvey,thestatementsweretranslatedintoalanguageusedinpeople's“everydaylife”,avoidingscientificwordsusedintheliteraturewhichcouldhaveledtoamisinterpretationofthestatements(Björklund & Paulsson, 2012; Trost, 2007). In this way, the respondents could understand thestatements,thusanswerthesurveyinawaythatwouldproduceaccuratesurveyresults.

Acorrelationanalysiswasdonetoshowthattheconstructsweresuitablefortheconceptualmodel,thusshowingconvergentvalidity.AccordingtoMalhotra(1999),acorrelationcanbedescribedasastatisticsummarisingofthestrengthofassociationbetweentwovariables.Thestatisticalsignificanceshowsthelevelofriskthatistakenwhenstatingthatthereisarelationshipbetweentwovariables.Thehighestlevelofriskwithinsocialscienceisusually“p<0,05”(Pallant,2010).

(1) (2) (3) (4) (5) (6) (7)(1) PerformanceExpectancy - (2) EffortExpectancy ,595** - (3) SocialInfluence ,631** ,490** - (4) Anxiety ,505** ,478** ,492** - (5) Trust ,468** ,435** ,466** ,636** - (6) PersonalInnovativeness ,240** ,453** ,288** ,286** ,250** - (7) BehaviouralIntention ,528** ,600** ,447** ,492** ,377** ,310** -

Table5-Correlationanalysis(**accordingtoSPSSthecorrelationissignificantatthe0.01level(2-tailed))

Table5showsthestrengthsofthecorrelationsbetweenalloftheconstructs.Thenumbersinboldarethecorrelationsrelevanttotheconceptualmodelofthisstudy.Previousresearchhasvalidatedtheitemsinthetable,buttobecertaintheycanworktogetherinonemodel,theirdegreeofseparation(i.e.thatthecorrelationsarenottooclosetothevalue1sothattheymightmeasurethesamething)istestedwithcorrelationanalysis.AscanbeseeninTable5,allthecorrelationsaresignificantbutnocorrelationistoocloseto1sotheycanbedrawninonemodelwherenottwoconstructsmeasurethesamething.Theconstructshavingstrongcorrelationsbetweeneachotherareperformanceexpectancyandanxiety,effortexpectancyandbehaviouralintentionaswellasperformanceexpectancyandbehaviouralintention.Mediumstrongcorrelationsarefoundbetweenanxietyandeffortexpectancy,personalinnovativenessandeffortexpectancy,behaviouralintentionandsocialinfluenceaswellasbetweenbehaviouralintentionandtrust.Thecorrelationbetweenpersonalinnovativenessandperformanceexpectancyisconsideredweak.Overall,Table5showsthattherearesignificantcorrelationsbetweentheconstructsrelevantforthisstudy.

3.6AnalysisofdataThe program SPSSwas used to analyse the collected data. A correlation analysis, a calculation ofCronbach’sAlphaandtheregressionanalysesweredonetogettothefindingsofthispaper.Sincethe

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constructs measured were ordinally scaled, Spearman’s rho was used as the tool with which thecorrelationanalysiswasdone(Sekaran,1992).

Amultiple linearregressionanalysishasbeengeneratedsincethecorrelationmatrix (Table5)onlyshowedsignificantcorrelations.Thelinearregressionanalysishelpstounderstandthedirectionoftherelationbetweenthedependentandindependentvariable.Thefocusintheanalysisisonafewaspectsof the regression analysis. The first aspect is the adjusted R² which indicates to what extent theindependentvariable canpredict thedependentvariable inmultiple regressions. In this study, theadjustedR²isanalysedsinceitkeepsthenumberofindependentvariablesinmind.The(adjusted)R²isalwaysbetween0and1andthecloserto1itis,themorethedependentvariableisexplainedbytheindependentvariables.Tofindoutifthehypothesesaresignificant,thep-valuefromtheANOVAtable and individual p-values are analysed. The p-value should be lower than 0,05 in order to besignificant (Pallant, 2010). According to Voeten & van den Bercken (2003), the t-value should beanalysed inadditiontothep-valuessincethesetwogohand inhand.Thet-value is thecalculateddifferenceshowninunitsofthestandarderror,thusmeasuringthesizeofthedifferencerelativetothevariation inthesampledata.Thet-valueshouldbehigherthan2,0.Thehigherthet-value,themoreitproofsthatthereisnosignificantdifference.Thelastelementoftheregressionanalysisistheβ-value. The β-value showswhich independent variable is influencing the dependent variable themost. Since the β-value is a standardised coefficient, the outcome of different variables can becompared(ibid).

3.6.1LimitationsoftheanalysisItisworthtomentionthatthechosenwayoftestingtheconceptualmodelofthisstudy,islimited.Thethirdregressionanalysisdoesnotconsidertheinfluenceofanxietyandpersonalinnovativenesson performance expectancy and effort expectancy. Themodel is therefore not tested as awhole.Instead,itistestedseparatelybyseveralregressionanalyses.Acorrectwayoftestingamodelwhichisdrawnas thisonewouldbebyusingmultivariatemethodssuchasperforminga two-stage leastsquaresorthree-stageleastsquaresregressionanalysisorbySEM(structuralequationsmodelling).Thisisreturnedtointhesuggestionsforfurtherresearch.

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4.AnalysisInthischapter, theregressionanalysesarepresentedtotestthecreatedhypotheses, followedbyadiscussionwherethefindingsareconnectedtotheliteraturereview.Paragraph4.1and4.2showthetests affecting performance expectancy and effort expectancy and paragraph 4.3 shows the directinfluenceonbehaviouralintention.4.1Externalvariablesinfluencingperformanceexpectancy

AdjustedR² Sig.(p)(ANOVAtable)Test1 ,290 ,000

IndependentVariable

DependentVariable β t p Hypothesis

Anxiety Performanceexpectancy ,491 9,449 ,000 Supported

Personalinnovativeness

Performanceexpectancy ,126 2,425 ,016 Supported

Table6-Test1regressionanalysis

Thefirstregressionanalysiswhichhasbeendone(Table6)isanxietyandpersonalinnovativenessinaconstructwiththedependentvariableperformanceexpectancy.TheadjustedR²=0,290inthistest,whichmeansthatthelinearregressionexplains29percentofthevarianceinthedata.

Thep-valueofthetotalconstructis0,000whichislowerthan0,05.Thustheconstructinthismodelcansignificantlypredictperformanceexpectancy.Thep-valueinTable6indicateswhichindependentvariablehasasignificantinfluenceonperformanceexpectancy.Anxietyhasasignificantinfluencewith0,000onperformanceexpectancy.Eventhoughpersonalinnovativenessdoesnothaveasignificancelevelof100%,with98,4percent (p=0,016), itstillcanbesaidthatthatthe independentvariable,personal innovativeness, significantly predicts the dependent variable, performance expectancy,because0,016<0,05.Thet-valuesarebothabove2sothereisnosignificantdifference.

Theβ-valuesofthefactorsconveythedirectionsandthestrengthofthedirectionsoftherelationshipbetweentheindependentvariableandthedependentvariable,performanceexpectancy,withinthemodel.Theβ-valueforanxietyis0,491,whichmeansthatthisvariablehasastrongerpositiveinfluenceonperformanceexpectancythanpersonalinnovativenesswithaβ-valueof0,126.

Basedontheregressionanalysis,thefollowinghypothesesaresupported:• H5:Anxietyhasapositiveinfluenceonperformanceexpectancywithintheacceptanceof

robotics.• H7:Personalinnovativenesshasapositiveinfluenceonperformanceexpectancywithinthe

acceptanceofrobotics.

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4.2Externalvariablesinfluencingeffortexpectancy

AdjustedR² Sig.(p)(ANOVAtable)Test2 ,374 ,000

IndependentVariable

DependentVariable β t p Hypothesis

Anxiety Effortexpectancy ,363 7,461 ,000 Supported

Personalinnovativeness

Effortexpectancy ,403 8,284 ,000 Supported

Table7-Test2regressionanalysis

Thesecondregressionanalysiswhichhasbeendone(Table7)isanxietyandpersonalinnovativenessinaconstructwiththedependentvariableeffortexpectancy.TheadjustedR²=0,374whichmeansthat the independent variables anxiety and personal innovativeness can predict the dependentvariableeffortexpectancywith37,4percent.

Thep-valueofthetotalconstructis0,000whichislowerthan0,05.Thustheconstructinthismodelcansignificantlypredicteffortexpectancy.Thetwoindependentvariablesbothshowasignificantp-valueof0,000.Basedonthisitcanbesaidthatthedependentvariablecanbepredictedbypersonalinnovativenessandanxiety.Thet-values,7,461and8,284,arebothabove2sotherenosignificantdifference.

Sincetheconstructissignificantandthetwoindependentvariablesseparatelyaswell,theβ-valuecanbeanalysed.Inthisregressionanalysistest,thereisnotthatmuchofadifferencebetweenthetwoindependentvariables,anxiety(β=0,363)andpersonalinnovativeness(β=0,403).Thisimpliesthatpersonalinnovativenesshasaslightlystrongerinfluenceoneffortexpectancy,butthisdifferenceisalmostnotnoteworthy.

Basedontheregressionanalysis,thefollowinghypothesesaresupported:• H6:Anxietyhasapositiveinfluenceoneffortexpectancywithintheacceptanceofrobotics• H8:Personalinnovativenesshasapositiveinfluenceoneffortexpectancywithinthe

acceptanceofrobotics

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4.3Variablesinfluencingbehaviouralintention

AdjustedR² Sig.(p)(ANOVAtable)Test3 ,541 ,000

IndependentVariable

DependentVariable β t p Hypothesis

Performanceexpectancy

Behaviouralintention ,270 3.601 ,000 Supported

Effortexpectancy

Behaviouralintention ,371 5,941 ,000 Supported

Socialinfluence

Behaviouralintention ,109 1,728 ,085 Notsupported

Trust Behaviouralintention ,129 2,280 ,024 Supported

Table8-Test3regressionanalysis

Thethirdregressionanalysiswhichhasbeentested(Table8)istrust,performanceexpectancy,effortexpectancyandsocialinfluenceinaconstructwiththedependentvariablebehaviouralintention.TheadjustedR²presentstowhatextentthedependentvariablecanbepredictedbythefourindependentvariables.ThereisquiteahighadjustedR²inthistest,namely0,541.Thismeansthat54percentofthevariationofbehaviouralintentioncanbeexplainedbythedependentvariables.

Thelasttesthasap-valueof0,000whichmeansthatthetestofthisconstructasawholeissignificant.Theinterestingpartistheindividualp-valuesoftheindependentvariables.Eventhoughtheconstructasawholeissignificant,notalloftheindividualp-valuesaresignificant.Performanceexpectancyandeffortexpectancyhaveasignificancelevelof100percent(p=0,000).Trusthasasignificancelevelof97,6percent(p=0,024)whichishigherthan95percentandcanthereforebeinterpretedassignificant.Bothconstructshaveat-valuehigherthan2,thusnothavingasignificantdifference.Thelastvariableissocialinfluencewithasignificancelevelof91,5percent(p=0,085),whichislowerthan95percent,thusnotsignificant.Thet-valueis1,728,whichislowerthan2,thusshowingasignificantdifference.Therefore,thehypothesisstatingthatsocialinfluenceaffectsbehaviouralintention,isnotsupported.

SincehypothesisH3hasbeen rejected, it isnot relevant toanalyse theβ-valueof thishypothesis.Comparingtheβ-valuesfromtheotherindependentvariableswitheachother,itcanbesaidthateffortexpectancyhasthebiggestinfluenceonbehaviouralintentionwithaβ-valueof0,371.Theβ-valueofperformanceexpectancyis0,270andtheβ-valueoftrustis0,129whichhasthelowestinfluenceonbehaviouralintention.

Basedontheregressionanalysis,thefollowinghypothesesaresupported:• H1:Performanceexpectancyhasapositiveinfluenceonbehaviouralintentionwithinthe

acceptanceofrobotics• H2:Effortexpectancyhasapositiveinfluenceonbehaviouralintentionwithintheacceptance

ofrobotics• H4:Trusthasapositiveinfluenceonbehaviouralintentionwithintheacceptanceofrobotics

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Basedontheregressionanalysis,thefollowinghypothesisisnotsupported:• H3:Socialinfluencehasapositiveinfluenceonbehaviouralintentionwithintheacceptanceof

robotics

Hypothesis RegressionAnalysisTest

H1:Performanceexpectancyhasapositiveinfluenceonbehaviouralintentionwithintheacceptanceofrobotics Supported

H2:Effortexpectancyhasapositiveinfluenceonbehaviouralintentionwithintheacceptanceofrobotics Supported

H3:Socialinfluencehasapositiveinfluenceonbehaviouralintentionwithintheacceptanceofrobotics Notsupported

H4:Trusthasapositiveinfluenceonbehaviouralintentionwithintheacceptanceofrobotics Supported

H5:Anxietyhasapositiveinfluenceonperformanceexpectancywithintheacceptanceofrobotics Supported

H6:Anxietyhasapositiveinfluenceoneffortexpectancywithintheacceptanceofrobotics. Supported

H7:Personalinnovativenesshasapositiveinfluenceonperformanceexpectancywithintheacceptanceofrobotics Supported

H8:Personalinnovativenesshasapositiveinfluenceoneffortexpectancywithintheacceptanceofrobotics Supported

Table9-Overviewhypotheses

Figure2-Conceptualmodelshowingsupportedandnotsupportedhypotheses

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4.4DiscussionBelow,adiscussionofthefindingsofthispaper,arepresented.Theanalysisoftheconceptualmodelshowedthatallhypothesesofthemodelweresupported,exceptone(Table9,Figure2).HypothesisH3statingthatsocial influenceaffectsbehavioural intentionwasnotsupportedwhenbeingtestedwiththevariablesfromtheUTAUTmodelandtheexternalvariabletrust.Thediscussionwillconsiderallthefindingsoftheregressionanalyses,basedontheliteraturereviewpresentedinthispaper.

As stated in the conceptual model, performance expectancy and effort expectancy have a directinfluenceonbehavioural intention, in accordancewith theUTAUTmodel (Venkateshet al., 2003).Behaviouralintentionhasapositiveeffectonusagetechnologyandmayleadtoanactualpurchaseorrecommendationofaproductorservicetoothers(Zeithamletal.,1996;Mittaletal.,1999).Inotherwords,therespondentsofthisstudy,inthecontextofrobotics,feelthattheusefulnessandeaseofuseoftherobotwillaffecttheirintentiontobuyorusearobot.

Performance expectancy is described as the degree towhich an individual believes that using thetechnologicalsystemwillbeusefulforhimorher(Venkateshetal.,2003).Heerinketal.,(2010)foundthatperceivedusefulnessinfluencedtheintentiontousewhenstudyingtheacceptanceofassistivesocialrobotsusedbyelderly.Sinceperceivedusefulnessisaprecursortoperformanceexpectancy,takenfromtheTAM-model(Davis,1986),thefindingsofthisresearchseemtobeinaccordancewiththementionedresearch.Therespondentsofthisstudyseemtoagreewiththefactthatusefulnessoftherobotaffectstheintentiontouseorbuyone.UnliketheresearchmadebyHeerinketal.,(2010),therespondentsofthisresearchareofvariousages,consideringtheirownperceptionofwhatrobotsare.

Effortexpectancyisdescribedasthedegreeofeaseassociatedwiththetechnologicalsystemandhasadirectinfluenceonbehaviouralintention,accordingtotheUTAUTmodel(Venkateshetal.,2003).TheprecursorofthevariableisperceivedeaseofusefromtheTAM-model(Davis,1986).Asregardingthe influence of effort expectancy on behavioural intention,Heerink et al., (2010) also found thatperceivedeaseofuse influencedtheintentiontousewhenresearchingtheacceptanceofassistivesocialrobotsusedbyelders.Asmentionedabove,therespondentsofthisstudyhavethesameviewasthefindingsshownbyHeerinketal.,(2010)althoughtheageoftherespondentsisvarying,notonlyfocusing on elders. Additionally, the respondents of this study see robotics depending on theirperceptionandbeliefofwhatroboticswouldbeintheireverydaylife.Thesampleofthisstudyfindthattheeaseofuseisaffectingtheintentiontobuyorusearobot.

IntheUTAUTmodel,thesocialinfluenceisaffectingbehaviouralintention,togetherwithperformanceexpectancyandeffortexpectancy(Venkateshetal.,2003).Sincetheexternalvariabletrustisaffectingbehaviouralintentionaswell(Heerinketal.,2010),thefourvariablesweretestedtogether.Itwasthusassumed,whencreatingtheconceptualmodel,thatsocialinfluenceaffectingbehaviouralintention,wouldbeoneofthefindingsofthisresearch.Interestingly,thefindingsshowthattherespondentsofthisstudyarenotinfluencedbytheirenvironmentwhenintendingtobuyorusearobot.Thesocialinfluence did not affect behavioural intention, when being tested together with performanceexpectancy,effortexpectancyandtrust,thusnotbeingapartoftheconceptualmodelinspiredbytheUTAUTmodel,developedbyVenkatesh(2003).

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Anxiety,trustandpersonalinnovativenessarevariablesthatarenotpartoftheoriginalUTAUTmodel,buthavebeenusedasexternalpartsofthemodelinpreviousresearch.Thehypothesescreatedforthe conceptualmodelof this study, show that anxiety andpersonal innovativeness influencebothperformance expectancy and effort expectancy when tested together, while trust influencesbehaviouralintentionwhenbeingtestedtogetherwiththeinitialvariablesoftheUTAUTmodel.

Anxietyappearsfromthefearofbeingdeprivedofexpectedsatisfactionandcancreateafeelingofconcernaboutpossibleobstaclesthatmayoccurinone'sfuture(Spielberger,2013;Sarason,1984).Heerink et al., (2010) found a correlation between anxiety and perceived usefulness (Davis, 1986)amongeldersusingassistiverobots,thusshowingaconnectionbetweenthenegativeemotionandthe usefulness of the assistive robot. The findings of this study show that anxiety influencesperformanceexpectancy,inotherwords,thefeelingsofconcernmayaffecttheintentiontobuyorusearobot,accordingtothesampleinthisresearch.

Bröhletal.,(2016)foundthatanxietyinfluencetheperceivedeaseofuse(Davis,1986),thusaffectingtheeffortexpectancyoftheUTAUTmodel.Bröhletal.,(2016)haddevelopedamodelincollaborationwiththeroboticindustry,whenstudyingtheacceptancelevelofemployersandemployeesregardingcooperation between robots and humans. The findings of thementioned study are similar to thefindings of this research, showing that anxiety influence effort expectancy. In other words, theinternationalsampleofthisstudydoesfeelthatanxietyinfluencesthedegreetowhichtherobotiseasytouse.

Trustisdefinedasthewillingnesstotakerisksandthewillingnesstorelyonanotherparty,thusbeingvulnerabletotheactionsofothers(Morgan&Hunt,1994;Mayeretal.,1995).DeGraafetal.,(2017)realised, when studying the acceptance of social robots, that having a social robot in a homeenvironmentcouldincreasethefeelingoftrustiftheparticipantsofthestudybelievedthattheyhadskillstointeractwiththerobot.Inaddition,Tortaetal.(2014)foundthatelderlywerewillingtotrustasmallsociallyassistiverobotwhentheysawthebenefitofusingit.Thefindingsofthisstudyshow,thattrustinfluencesbehaviouralintention.Inotherwords,thewillingnesstotakerisksinfluencestheintentiontobuyorusearobot,amongtheinternationalsampleofthisstudy.AsimilarfindingwaspresentedbyHeerinketal.,(2010),whofoundacorrelationbetweentrustandtheintentiontouseanassistivesocialrobotamongolderadults.Asmentionedbefore,therespondentsofthisstudyhavetheirownperceptionofrobotics,whichmightinfluencehowtheyacceptthenewtechnology.

Agarwal&Prasad(1998)definepersonalinnovativenessasthewillingnessofanindividualtotryoutnewtechnologies,statingthatpeoplearecharacterisedas“innovative”iftheyareearlyadoptersofinnovation. The innovative people are usually seeking information actively, relying less on othersopinions (Rogers, 1995). The findings of this study show that personal innovativeness influencesperformanceexpectancy.Inotherwords,thewillingnesstotryoutnewtechnologiesdoesaffecttheusefulnessoftherobot,perceivedbythesample.Thefindingspresentedhavebeenfoundinotherresearchofnewtechnologyaswell,suchas intheadoptionofwireless internetservices (Luetal.,2005).

In the findingsof this study, personal innovativeness influenceeffort expectancy, aswell. Inotherwords,thewillingnesstotryoutnewtechnologyinfluencesthedegreeofhoweasytherobotistouse.Interestingly,deGraafetal.(2017)found,whenstudyingsocialroboticsandhuman-robotinteraction,

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thatpeoplewhoseethemselvesasmoreinnovativeexpectthatusingasocialrobotinhis/herhome,wouldbeeasiertouse.

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5.ConclusionThepurposeofthisstudywastoinvestigatetheacceptancelevelofroboticsinternationally,becauseofthefutureincreaseofnewtechnologyintheformofrobotics.Theimplicationsofrobotacceptanceforconsumershavebeenstudiedbyconductingaquantitativeresearch.Themaininspirationfortheconceptual model has been the UTAUT model, which has been used in previous literature whenresearchingacceptanceofnewtechnology.Additionally,externalvariableshavebeenincludedtosuitthecontextofrobotics.Thevariableshavebeenaddedbasedonpreviousresearch,wheretheyhavebeenconfirmedtoinfluencethevariablesoftheUTAUTmodel.

As stated in the conceptual model, this study shows that performance expectancy and effortexpectancyhaveadirect influenceonbehavioural intention, inaccordancewiththeUTAUTmodel.Additionally, the UTAUT model states that social influence is affecting behavioural intention(Venkateshetal.,2003).Itwasthusassumed,whencreatingtheconceptualmodel,thatthiswouldbeoneofthefindingsofthisresearch.Interestingly,thefindingsshowthatsocialinfluencedoesnotaffectbehavioural intentionwhenbeing testedtogetherwithperformanceexpectancy,effortexpectancyandtheexternalvariabletrust.EventhoughsocialinfluenceispartoftheoriginalUTAUTmodel,basedonthesampleusedinthisstudyandthecontextofrobotacceptanceinternationally,theinfluenceofpeople in the respondents surrounding does not affect the intention to buy or use a robot. Thisimplicatesthattheappearanceofsocial influence intheconceptualmodel isnotsupportedbythefindingsofthisstudy.

Trust,anxietyandpersonalinnovativenessarenotpartoftheUTAUTmodel,butareusedinpreviousresearchasexternalvariableswhenresearchingtechnologyacceptanceindifferentcontexts.Asstatedin the conceptualmodelof this study, the findings show that anxiety andpersonal innovativenessinfluence both performance and effort expectancy. Trust has been tested as a direct influence onbehavioural intention along with effort expectancy, performance expectancy and social influence.Interestingly,wheresocial influencedidnotshowadirectinfluenceonbehaviouralintentionintheconceptualmodel,trustdid.Hence,thewillingnesstotakerisks,withinthe internationalsampleofthis study, influences the intention to buy/use a robot. The proposed external variable of trust isconfirmedtofitintheconceptualmodelaswellasanxietyandpersonalinnovativeness.

Robotshavebeenapartofsciencefictionforalongperiodoftime,creatingallkindsofvisionsforhowtheycouldbeadoptedintherealworld.Inthenearfuture,theuseofroboticswillincreaseglobally,bothwithinthehomesofpeopleandintheirworkenvironment.Accordingtotheinternationalsampleofthisstudy,theemotionsofanxiety,trustandthewillingnesstotrynewtechnologyareaffectingtheacceptance of robots. However, the influence of family, friends and colleagues seem to show theopposite,notinfluencingtheacceptanceofthenewtechnologyinthecontextofrobotics.

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5.1FutureresearchandlimitationsThemostobviousavenue forcontinued researchwouldbe to test themodelwithother statisticalmethodstoprovidetestingofthemodelasawhole.Suchamethodmeansevaluatingthepathsaspartofonemodelandthatwouldshedmorelightnotonlyontherelevanceofthehypothesis,butalsotoshowifthereareintermediatevariablesimportantforthedependentvariableofbehaviouralintention. Since the regression analyses show interesting results (concerning confirmation ofconfirmations),testingthemodelasawholemightshowdifferentresultsandthiswillgiveabetterunderstanding.

Thefindingsofthisstudycannotbegeneralisedsincedatawascollectedbyanon-probabilitysampling.Hence, it would be interesting to conduct research with probability sampling based on the sametheoreticalframeworkusedinthispaper,inordertogetmoreaccuratefindings.

TheinternationaldatacollectionshowedthatthemajorityofrespondentswerefromtheNetherlandsandSweden.Forfutureresearch,amorein-depthstudycouldbedoneinthesecountriesbasedonthefindingsofthisresearch.Theconceptualmodelcouldalsobetestedtocomparetheacceptanceofroboticsbetweencountries.

Themajorityoftherespondentswerebetween18to36yearsold,whichcanbeseenasalimitationsinceitdoesnotrepresentanevenspreadoftheoldergeneration.Itwouldthereforebeinterestingtoresearchtheacceptanceofdifferentagegroupsinmoredetail.

Additionally,itisnotedthattheconstructrepresentingpersonalinnovativeness,wasonlymeasuredwithtwoitems.Thiscouldaddalimitationforthisstudy,whichiswhypersonalinnovativenesswithinthecontextofrobotics,couldbemeasuredwithmoreitemsinfutureresearch.Thepathsbetweentheconstructsofthemodelcouldalsobefurtherexplored,preferablywithastructuralmodel.

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ReferencelistAbuShanab,E.,Pearson,J.M.,&Setterstrom,A.J.(2010).Internetbankingandcustomers'acceptance

inJordan:theunifiedmodel'sperspective.CommunicationsoftheAssociationforInformationSystems,26(1),23.

Agarwal,R.,&Prasad,J.(1998).Aconceptualandoperationaldefinitionofpersonalinnovativenessinthedomainofinformationtechnology.InformationSystemsResearch,9(2),204–215.

Agarwal, R. (2000), Individual Acceptance of Information Technologies. Educational TechnologyResearchandDevelopment,40,90-102.

Ajzen,I.(1985).Fromintentionstoactions:Atheoryofplannedbehavior.InActioncontrol(pp.11-39).SpringerBerlinHeidelberg.

Bandura,A.(1986).Theexplanatoryandpredictivescopeofself-efficacytheory.Journalofsocialandclinicalpsychology,4(3),359-373.

BjörklundA.,&PaulssonU.(2012).Seminarieboken(2:auppl.).Lund.StudentlitteraturAB.

Boesl,D.B.,& Liepert,B. (2016,October). 4RoboticRevolutions-ProposingaholisticphasemodeldescribingfuturedisruptionsintheevolutionofroboticsandautomationandtheriseofanewGeneration ‘R’of Robotic Natives. In Intelligent Robots and Systems (IROS), 2016 IEEE/RSJInternationalConferenceon(pp.1262-1267).IEEE.

Bröhl,C.,Nelles,J.,Brandl,C.,Mertens,A.,&Schlick,C.M.(2016,July).TAMReloaded:ATechnologyAcceptance Model for Human-Robot Cooperation in Production Systems. In InternationalConferenceonHuman-ComputerInteraction(pp.97-103).SpringerInternationalPublishing.

Bryman,A.,&Bell,E.(2015).BusinessResearchMethods.Oxford:OxfordUniversityPress.

Burch,T.K.(2003).Demographyinanewkey:Atheoryofpopulationtheory.DemographicResearch,9,263-284.

Carlsson, C., Carlsson, J., Hyvonen, K., Puhakainen, J. andWalden, P. (2006), “Adoption ofmobiledevices/services – searching for answers with the UTAUT”, Proceedings of the 39th HawaiiInternationalConferenceonSystemSciences,pp.1-10.

Christensen,L.,Andersson,N.,Carlsson,C.&Haglund,L.(1998)Marknadsundersökning-enhandbok,Lund,Studentlitteratur.

ChurchillJr.,G.A.(1979)AParadigmforDevelopingBetterMeasuresofMarketingConstructs.JournalofMarketingResearch,16,64-73.

Compeau,D.R.andHiggins,C.A.,(1995),ComputerSelf-Efficacy:DevelopmentofaMeasureandInitialTest,MISQuarterly,June:189-211

Craig, J. J. (2005). Introductiontorobotics:mechanicsandcontrol (Vol.3,pp.48-70).UpperSaddleRiver:PearsonPrenticeHall.

XI

DavisJr,F.D.(1986).Atechnologyacceptancemodelforempiricallytestingnewend-userinformationsystems:Theoryandresults(Doctoraldissertation,MassachusettsInstituteofTechnology).

Davis,F.D.,Bagozzi,R.P.,&Warshaw,P.R.(1992).Extrinsicandintrinsicmotivationtousecomputersintheworkplace1.Journalofappliedsocialpsychology,22(14),1111-1132.

DeGraaf,M.M.,BenAllouch,S.,&vanDijk,J.A.(2017).WhyWouldIUseThisinMyHome?:AModelofDomesticSocialRobotAcceptance.Human–ComputerInteraction,(just-accepted).

Dillon, A., &Morris, M. G. (1996). User acceptance of new information technology: theories andmodels. In Annual review of information science and technology. Medford, NJ: InformationToday.

Durakbasa,N.M., Bauer, J.M., Bas,G.,& Kräuter, L. (2016). Novel Trends in theDevelopment ofAutonomation and Robotics in Metrology for Production in the Future. IFAC-PapersOnLine,49(29),6-11.

Foon,Y.S.&Fah,B.C.Y.(2011),“InternetbankingadoptioninKualaLumpur:anapplicationofUTAUTmodel”,InternationalJournalofBusinessandManagement,Vol.6No.4,pp.161-167.

Heerink,M.,Kröse,B.,Evers,V.,&Wielinga,B.(2010).Assessingacceptanceofassistivesocialagenttechnologybyolderadults:thealmeremodel.Internationaljournalofsocialrobotics,2(4),361-375.

Hur,W.M.,Yoo,J.J.,&Chung,T.L.(2012).Theconsumptionvaluesandconsumerinnovativenessonconvergenceproducts.IndustrialManagement&DataSystems,112(5),688-706.

Jen,W.,Lu,T.,&Liu,P.T.(2009).Anintegratedanalysisoftechnologyacceptancebehaviourmodels:Comparisonofthreemajormodels.MISReview,15(1),89-121.

Johansson,Therese.(2017,January).MänniskavsMaskin.Shortcut.(1)18-19.

Kaivo-oja, J., Roth, S., & Westerlund, L. (2016). Futures of robotics. Human work in digitaltransformation.InternationalJournalofTechnologyManagement(Forthcoming).

Kijsanayotin,B.,Pannarunothai, S.,&Speedie, S.M. (2009).Factors influencinghealth informationtechnology adoption in Thailand's community health centers: Applying the UTAUT model.Internationaljournalofmedicalinformatics,78(6),404-416.

Lee, Y. H., Hsieh, Y. C., &Hsu, C. N. (2011). Adding innovation diffusion theory to the technologyacceptancemodel: Supporting employees' intentions to use e-learning systems. EducationalTechnology&Society,14(4),124-137.

Lu,J.,Yao,J.E.,&Yu,C.S.(2005).Personalinnovativeness,socialinfluencesandadoptionofwirelessInternet services viamobile technology.The Journal of Strategic Information Systems,14(3),245-268.

XII

Malhotra,N.K.(1999)MarketingResearch,anAppliedOrientation,NewJersey,Prenticehall,Inc.

Mayer, R. C.,Davis, J.H.,& Schoorman, F.D. (1995).An integrativemodel of organizational trust.Academyofmanagementreview,20(3),709-734.

Mittal,V., Kumar,P.,&Tsiros,M. (1999).Attribute-levelperformance, satisfaction, andbehavioralintentionsovertime:aconsumption-systemapproach.TheJournalofMarketing,88-101.

Morgan, R.M.,&Hunt, S. D. (1994). The commitment-trust theory of relationshipmarketing.Thejournalofmarketing,20-38.

Neale,J.M.&Liebert,R.M.(1973)ScienceandBehaviour-AnIntroductiontoMethodsofResearch,EnglewoodCliffs,NewJersey,PrenticeHallInternational,Inc.

Nunnally,J.(1978)PsychometricTheory,NewYork,McGraw-Hill.

OttoUrisman,Alexandra.(2016,September11).Branschernadärrobotartaröver.DagensNyheter.Retrieved 2017-03-18 from: http://www.dn.se/ekonomi/jobb-karriar/branscherna-dar-robotar-tar-over/

Oye, N. D., Iahad, N. A., & Rahim, N. A. (2014). The history of UTAUT model and its impact on ICT acceptance and usage by academicians. Education and Information Technologies, 19(1), 251-270.

PallantJ.(2010).SPSSSurvivalManual-AstepbystepguidetodataanalysisusingSPSS.(4thedition).McGrawHillEducation.England.

Rogers,E.M.(1995).Diffusionofinnovations(4thed.).NewYork:FreePress.

Rogers,E.M.(2010).Diffusionofinnovations.SimonandSchuster.

Samaradiwakara, G. D. M. N., & Gunawardena, C. G. (2014). Comparison of existing technologyacceptance theories and models to suggest a well improved theory/model. InternationalTechnicalSciencesJournal,21.

Sambasivan,M.,Wemyss,G.P.andRose,R.C. (2010),“UseracceptanceofaG2Bsystem:acaseofelectronicprocurementsysteminMalaysia”,InternetResearch,Vol.20No.2,pp.169-187.

Sarason, I. G. (1984). Stress, anxiety, and cognitive interference: reactions to tests. Journal ofpersonalityandsocialpsychology,46(4),929.

Sekaran,U.(1992)ResearchMethodsforBusiness,ASkillBuildingApproach,JohnWileyandSons,Inc.

Siciliano,B.,&Khatib,O.(Eds.).(2016).Springerhandbookofrobotics.Springer.

Spielberger,C.D.(Ed.).(2013).Anxietyandbehavior.AcademicPress.definition

Svenberg,Josefin.(2017,januari).Robotarskataöverjobbsomvityckerärtråkiga.Shortcut.(1).28-36.

XIII

Taylor, S.,&Todd,P.A. (1995).Understanding information technologyusage:A testof competingmodels.Informationsystemsresearch,6(2),144-176.

The Partnership for Robotics in Europe. (2015). Robotics 2020Multi-Annual Roadmap. Retrieved:2017-04-05from:https://www.eu-robotics.net/sparc/newsroom/index.html

Thompson,R.L.,Higgins,C.A.,&Howell,J.M.(1991).Personalcomputing:towardaconceptualmodelofutilization.MISquarterly,125-143.

Torta,E.,Werner,F.,Johnson,D.O.,Juola,J.F.,Cuijpers,R.H.,Bazzani,M.,...&Bregman,J.(2014).Evaluationofasmallsocially-assistivehumanoidrobotinintelligenthomesforthecareoftheelderly.JournalofIntelligent&RoboticSystems,76(1),57.

Triandis,H.C.(1977).InterpersonalBehaviour.Monterey,C.A:Brook/Cole.

TrostJ.(2007).Enkätboken(3:euppl.).StudentlitteraturAB.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of informationtechnology:Towardaunifiedview.MISquarterly,425-478.

Venkatesh,V.,Thong,J.Y.,&Xu,X.(2012).Consumeracceptanceanduseofinformationtechnology:extendingtheunifiedtheoryofacceptanceanduseoftechnology.

Voeten,M.J.,&vandenBercken,J.H.(2003).Lineaireregressieanalyse:Introductieopmeervoudigeregressieenverwanteprocedures:Handboek.Groningen:StenfertKroese.

Weiss, A., Bernhaupt, R., Tscheligi, M., Wollherr, D., Kuhnlenz, K., & Buss, M. (2008, August). Amethodologicalvariationforacceptanceevaluationofhuman-robotinteractioninpublicplaces.In Robot and Human Interactive Communication, 2008. RO-MAN 2008. The 17th IEEEInternationalSymposiumon(pp.713-718).IEEE.

Williams,M.D., Rana,N. P.,&Dwivedi, Y. K. (2015). Theunified theoryof acceptance anduseoftechnology(UTAUT):aliteraturereview.JournalofEnterpriseInformationManagement,28(3),443-488.

Wilson,H.J.,(2015,April).WhatIsaRobot,Anyway?InHarvardBusinessReview.Retrieved:2017-04-05from:https://hbr.org/2015/04/what-is-a-robot-anyway

Wisniewski,M.(1994)QuantitativeMethodsforDecisionMakers,London,PitmanPublishing.

Xu,H.andGupta,S.(2009),“Theeffectsofprivacyconcernsandpersonalinnovativenessonpotentialandexperiencedcustomers’adoptionoflocation-basedservices”,ElectronMarkets,Vol.19Nos2/3,pp.137-149.

Yu,C.S. (2012).Factorsaffecting individuals toadoptmobilebanking:EmpiricalevidencefromtheUTAUTmodel.JournalofElectronicCommerceResearch,13(2),104.

Zeithaml,V.A.,Berry,L.L.,&Parasuraman,A.(1996).Thebehavioralconsequencesofservicequality.theJournalofMarketing,31-46.

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Zhou,T.,Lu,Y.,&Wang,B.(2010).IntegratingTTFandUTAUTtoexplainmobilebankinguseradoption.Computersinhumanbehavior,26(4),760-767.

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AppendixA.OperationalisationPersonalfactualquestions

Questionregardingage

Q1:Howoldareyou?

Operationalisation:Agewasaskedinordertoreceivethedemographicspreadofthesample.Furthermore,respondentsunder18yearsoldwouldhavebeenexcludedfromtheanalysis,sinceincludingunderagedrespondentsinthisstudywouldbeconsideredunethical.

Questionregardingcountryoforigin

Q2:Whatcountryareyoufrom?

Operationalisation:Thecountryoforiginwasaskedinordertoseethespreadofnationalitiesinthesample,becauseoftheinternationalcontextofthestudy.

Questionregardinggender

Q3:Whatisyourgender?

Operationalisation:Genderwasaskedinordertopresentthespreadofmales,femalesandthosewhowishednottostatetheirgender.

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Statementsoftheconstructs

Statementsperformanceexpectancy

Q9:1:Robotsareusefulinmyworkenvironment

Q9:2:Atwork,arobotwouldincreasetheproductivity

Q9:3:Icouldsavemuchtimeatworkbyusingarobot

Q9:4:Iwouldaccomplishtasksathomemorequicklywitharobot

Q9:5:Robotsareusefulinmypersonallife

Q9:6:Havingarobotcouldsavetimeforme,inmypersonallife

Q12:4:Robotsshouldreplacehumanlabourifthisismoreeffective/productive

Operationalisation:Performanceexpectancydescribestheusefulnessofanewtechnology(Venkateshetal.,2003).Thestatementsusedformeasuringperformanceexpectancyare inspiredbythestatementscreatedbyCarlssonetal.,(2006);Kijsanajotinetal.,(2009)andAbuShanabetal.,(2010)whohaveresearchedtechnologyacceptanceofmobiledevices,informationtechnologyandinternetbanking.

Statementseffortexpectancy

Q7:1:Ilikelearningaboutnewtechnologies

Q13:1:Iexpectmyinteractionwitharobottobeclearandunderstandable

Q13:2:Ilearntooperatenewtechnologieseasily

Q13:3:Iamskilfulenoughtousearobot

Q13:4:Usingarobotinmyworkenvironmentwouldbeeasy

Q13:5:Ibelieverobotsareeasytouseinmypersonallife

Operationalisation:Effortexpectancydescribesthedegreetowhichanewtechnology iseasytouse(Venkateshetal.,2003).ThestatementsusedformeasuringeffortexpectancyareinspiredbythestatementscreatedbyAbuShanabetal.,(2010);Carlssonetal.,(2006);Foon&Fah(2009)andKijsanajotinetal.,(2009)whohaveresearchedtechnologyacceptanceregardinginternetbanking,adoptionofmobiledevices,informationtechnologyadoptionandnewtechnologyforlocation-basedservices.

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Statementsregardingsocialinfluence

Q11:1:Myworkingenvironmentsupporttheuseofrobots

Q11:2:Usingarobotatworkwouldindicatemehavingahigherstatusthanthosewhodonot

Q11:3:Myfriends/familysupporttheuseofrobots

Q11:4:Usingarobotduringmypersonallifewouldindicatemehavingahigherstatusthanthosewhodonot

Q11:5:Itakeandappreciateadvicefromfriendsandfamily

Q11:6:Itakeandappreciateadvicefrommyco-workers

Operationalisation:Social influence isdescribedas ‘thedegree towhichan individualperceives that importantothersbelieve he or she should use the new system.’ (Venkatesh et al., 2003) The statements used formeasuring social influence are inspired by the statements created by Foon & Fah (2011) whenresearchingaboutInternetbankingadoption.

Statementsregardingbehaviouralintention

Q14:1:Iwillbuyarobot

Q14:4:IpredictthatIwilluserobotsatmywork,within20years

Q14:10:IpredictthatIwilluserobotsinmypersonallife,within20years

Q14:12:IbelieveIwillhavearobotinmyhomewithin20years

Operationalisation:Behavioural intentionhasapositiveeffectontheusageoftechnology(Venkateshetal.,2003)andmay lead to a purchase of a product or service (Zeithaml et al., 1996). The statements used formeasuringbehaviouralintentionareinspiredbythestatementscreatedbyAbuShanabetal.,(2010);Foon&Fah(2009);Sambavisanetal.(2010)Kijsanajotinetal.,(2009)andXu&Gupta(2009)whohaveresearched technology acceptance regarding internet banking, electronic procurement systems,informationtechnologyadoptionandnewtechnologyforlocation-basedservices.

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Statementsregardinganxiety

Q10:1:Ithinkrobotsaresaferthanhumans

Q10:3:Robotsareaformoftechnologythatrequirescarefulmanagement

Q10:4:Iwouldfeelsafeworkingclosetoarobot

Q12:10:Robotswillcreateabetterfuture

Q14:7:Ifeartherobotswilltakeovermanythingsinsociety

Q14:13:Ilikethepossibilityofhavingmorerobotsinsocietyinthefuture

Operationalisation:Anxietyappearsfromthefearofbeingdeprivedofexpectedsatisfactionandbythefeelingofbeingconcernedaboutpossibleobstaclesthatmayoccurinthefuture(Spielberger,2013;Sarason,1984)ThestatementsusedformeasuringanxietyareinspiredbythestatementscreatedbyAbuShanabetal.,(2010)andCarlssonetal.,(2006)whenresearchingabouttheacceptanceofInternetbankingandtheadoptionofmobiledevices.

Statementsregardingtrust

Q10:2:Ifindproductsmadebyrobotsreliable

Q10:5:Robotsarereliable

Q12:7:Iwouldtrusttheadviceofarobot

Q12:8:Robotswillkeephumaninterestsinmind

Operationalisation:Trust isawillingnesstotakerisksandthewillingnesstorelyonanotherparty.(Mayeretal.,1995;Morgan&Hunt,1994)Thestatementsusedformeasuringtrustareinspiredbythestatementscreatedby Sambavisan et al., (2010) andWeiss et al., (2008) when researching about the acceptance ofInternetbankingandtheacceptanceofhuman-robotinteraction.

Statementsregardingpersonalinnovativeness

Q7:5:Amongmyco-workers,Iamusuallythefirsttotryoutnewtechnologies

Q7:6:Amongmyfriends/family,Iamusuallythefirsttotryoutnewtechnologies

Operationalisation:Personal innovativeness is defined as the willingness of an individual to try out new technology(Agarwal&Prasad,1998).Thestatementsusedformeasuringpersonalinnovativenessareinspiredby

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thestatementscreatedbyAbuShanabetal.,(2009)andXu&Gupta(2009)whenresearchingabouttheacceptanceofInternetbankingandtheadoptionoflocation-basedservices.

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B.Descriptivestatistics Mean Standard

DeviationCoefficientofVariation

AgeQ1:Howoldareyou? 1,8 1,2 65,8%

GenderQ3:Whatisyourgender? 1,5 0,5 32,1%

PerformanceexpectancyQ9:1:Robotsareusefulinmyworkenvironment 4,3 1,8 40,9%Q9:2:Atwork,arobotwouldincreasetheproductivity 3,8 2,0 51,9%Q9:3:Icouldsavemuchtimeatworkbyusingarobot 3,8 1,9 50,0%Q9:4:Iwouldaccomplishtasksathomemorequicklywitharobot 4,9 1,6 32,1%Q9:5:Robotsareusefulinmypersonallife 4,2 1,7 41,3%Q9:6:Havingarobotcouldsavetimeforme,inmypersonallife 4,8 1,6 33,5%Q12:4:Robotsshouldreplacehumanlabourifthisismoreeffective/productive

4,3 1,7 40,4%

EffortexpectancyQ7:1:Ilikelearningaboutnewtechnologies 5,6 1,4 24,1%Q13:1:Iexpectmyinteractionwitharobottobeclearandunderstandable

5,0 1,3 26,5%

Q13:2:Ilearntooperatenewtechnologieseasily 5,4 1,3 23,6%Q13:3:Iamskilfulenoughtousearobot 5,2 1,4 26,6%Q13:4:Usingarobotinmyworkenvironmentwouldbeeasy 4,2 1,6 38,9%Q13:5:Ibelieverobotsareeasytouseinmypersonallife 4,8 1,4 29,1%

SocialinfluenceQ11:1:Myworkingenvironmentsupporttheuseofrobots 4,0 1,7 42,4%Q11:2:Usingarobotatworkwouldindicatemehavingahigherstatusthanthosewhodonot

3,2 1,6 50,1%

Q11:3:Myfriends/familysupporttheuseofrobots. 4,1 1,4 35,5%Q11:4:Usingarobotduringmypersonallifewouldindicatemehavingahigherstatusthanthosewhodonot.

3,5 1,7 49,1%

Q11:5:Itakeandappreciateadvicefromfriendsandfamily 5,6 1,2 20,8%Q11:6:Itakeandappreciateadvicefrommyco-workers 5,4 1,2 21,4%

BehaviouralintentionQ14:1:Iwillbuyarobot 5,0 1,6 31,0%Q14:4:IpredictthatIwilluserobotsatmywork,within20years 5,0 1,7 33,6%Q14:10:IpredictthatIwilluserobotsinmypersonallife,within20years

5,2 1,6 30,0%

Q14:12:IbelieveIwillhavearobotinmyhomewithin20years 5,2 1,6 30,6%Anxiety

Q10:1:Ithinkrobotsaresaferthanhumans 3,9 1,5 38,1%Q10:3:Robotsareaformoftechnologythatrequirescarefulmanagement

5,6 1,3 22,6%

Q10:4:Iwouldfeelsafeworkingclosetoarobot 4,5 1,4 31,1%Q12:10:Robotswillcreateabetterfuture 4,6 1,4 29,9%Q14:7:Ifeartherobotswilltakeovermanythingsinsociety 4,6 1,7 36,3%Q14:13:Ilikethepossibilityofhavingmorerobotsinsocietyinthefuture

4,7 1,6 33,6%

TrustQ10:2:Ifindproductsmadebyrobotsreliable 4,7 1,2 26,4%Q10:5:Robotsarereliable 4,8 1,2 26,1%Q12:7:Iwouldtrusttheadviceofarobot 3,8 1,5 40,0%Q12:8:Robotswillkeephumaninterestsinmind 3,8 1,5 39,6%

PersonalinnovativenessQ7:5:Amongmyco-workers,Iamusuallythefirsttotryoutnewtechnologies

3,8 1,7 44,2%

Q7:6:Amongmyfriends/family,Iamusuallythefirsttotryoutnewtechnologies

4,2 1,8 41,8%