ISSN 1822-7996 (PRINT), ISSN 2335-8742 (ONLINE)TAIKOMOJI EKONOMIKA:SISTEMINIAI TYRIMAI: 2020.14/1https://doi.org/10.7220/AESR.2335.8742.2020.14.1

SamantaGRAUŽINIENĖ –Bachelor’sDegreeinFinance,VytautasMagnusUniversity,FacultyEconomicsandManagement.Address:S.Daukantostr.28,LT-44246Kaunas,Lithuania;e-mailsamanta.grauziniene@stud.vdu.ltDovilėKUIZINIENĖ –adoctoralstudentatVytautasMagnusUniversity,FacultyofInformatics,mas-ter’sdegrees inFinanceandComputing,anassistantatVytautasMagnusUniversity,Facultyof Infor-maticsandFacultyEconomicsandManagement.Address:Vileikosstr.8,LT-44404Kaunas,Lithuania;e-maildovile.kuiziniene@vdu.lt

Research on Factors Identification in FinTech Acceptance: Lithuania Context

ThepaperaimsaretoidentifyfactorsthathavetheinfluenceonFinTechservicesacceptanceinLithuania.Inordertocollectthequestionnairedata,severalmodelsandanalyseswereused:technologyacceptancemodel(TAM),structuralequationmodeling,exploratoryandconfirmatoryfactoranalysis,pathanalysis,andvisu-alization.Thestudyresultsstatethatperceivedusefulnessandtrustinserviceshaveastatisticallysignificanteffectonconsumers’attitudestowardsfinancialtechnologies.Keywords: confirmatoryfactoranalysis,exploratoryfactoranalysis,financialtechnologies,FinTech,Struc-turalEquationModeling,technologyacceptancemodel.

Straipsnyjesiekiamaatskleistiveiksnius, lemiančiusfinansiniųpaslaugųpriėmimąLietuvoje.Tyrimometuanketiniamsduomenimspritaikytastechnologijųpriėmimomodelis(TAM),atliekamasstruktūriniųlygčiųmodeliavimas,tiriamojiirpatvirtinančiojifaktorinėanalizė,keliųanalizėirvizualizavimas.Tyrimometunu-statyta,kadsuvokiamasnaudingumasirvartotojųpasitikėjimasdarostatistiškaireikšmingąįtakąvartotojųpožiūriuiįfinansinestechnologijas.Reikšminiai žodžiai: finansinėstechnologijos,FinTech,patvirtinančiojifaktorinėanalizė,struktūriniųlygčiųmodeliavimas,technologijųpriėmimomodelis,tiriamojifaktorinėanalizė.

JEL Classifications: C12/C51/O30.

Samanta GRAUŽINIENĖ, Dovilė KUIZINIENĖ

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Introduction

FinTech is an abbreviation for financialtechnologiescommonlyusedfor‘anewfi-nancial industry that applies technologies to improve financial activities’ (Schuef-fel,2016).Financial technologiesarealsounderstood as technological innovations usedinthefinancialsectortosimplifyandaccelerate the performance of financialservices. Recently, financial technologies

havebeenoneofthemostwidelyanalyzedtopics in the context of Lithuania’s econo-micgrowthprospects.Thefinancialsectorhas many expectations from the Fintech: notonly the increaseof thebudget fromthepaid taxes but also the integrationofthe services into the daily life of Lithu-anians,contributingtothesmootherper-formanceofdailyfinancialservices.

The adaptation of the populationto technological innovations is often a

Samanta GRAUŽINIENĖ, Dovilė KUIZINIENĖ 42

difficult process caused bymany factors.SomeauthorstriedtodistinguishFinTechacceptance factors and assess their impact: Ryu(2018),Chuang,LiuandKao(2016),Hu, Ding, Li, Chen and yang (2019),Ramos(2016)andothers.Inordertoeval-uateFinTechacceptanceamong residentsare used: TechnologyAcceptanceModel,extended Technology AcceptanceModel,Theory of Reasoned Action model, andThe Unified Theory of Acceptance andUseofTechnologymodel.

Thefinancialtechnologysectoriscon-sumer-dependent, thus it is important for FinTech start-ups to identify the factorsthatleadtothemorefrequentuseoffinan-cial technology companies instead of reg-ularbankingservices.FinTechacceptanceresearchresultsallowustoadjustthecom-pany’s service delivery model or eliminate thefactorsthatcauseunfavorablefeelingsfor consumers and discourage them from furtherFinTechservicesusage.

The research object: identification of FinTechacceptancefactors.

The aim: to identify and evaluate the factors determining the acceptance of Fin-TechinLithuania.

The research methods: analysis of sci-entific literature, data collectionbyques-tionnaire, technology acceptance model (TAM), structural equation modeling(SEM), exploratory (EFA) and confirma-tory factor analysis (CFA), path analysis,comparison, and summary of the results.

The objectives of the article:1. To analyze and compare previous

research done on financial technologies acceptance.

2. Toidentifyandevaluatethefactorsof acceptance of Fintech in Lithuania.

Previous Research on FinTech Acceptance

Inordertoanalyzeconsumerbehaviorinthe process of technology acceptance, se-veral theoreticalmodels have been deve-loped.ThemostcommonlyusedtheorieswhenassessingtechnologyacceptanceareaTechnologyAcceptanceModel,aTheoryofReasonedAction,anUnifiedTheoryofAcceptanceandUseofTechnology,anIn-novationDiffusionModel,aTechnology-Organization-Environment model (Oli-veira&Martins,2016).‘Alotofresearchisdone according to the traditional models, and the rest usually link previous models oraddnewfeaturesinordertocreatemo-dels during research’ (Ribokas& Burins-kienė,2018).

Ryu(2018)conductedastudyinKoreaon the factors that determine consumers’ behavioral intention to use or not to useFinTech.Theauthorchose theTheoryofReasoned Action (TRA) model for thisstudy. TRAwas developed by Ajzen andFishbein in 1967 and considered to beone of the oldest theories of technology acceptance.Thistheoryassumesthatcon-sumersarethemaindecision-makerswhocontinually count and evaluate relevant behavioralbeliefswhenformulatingtheirattitudes towardbehavior (Li, 2013).TheauthorofthestudychosetoapplytheTRAmodeltoassesstheacceptanceofFinTechthroughperceivedconsumerbenefitsandrisks,whichwere split intomore factors:economic benefits, convenience, serviceprocess, financial risk, legal risk, security risk, operational risk.

Chuangetal.(2016)chosetheTechnol-ogyAcceptanceModel(TAM)toevaluatetheacceptanceofFinTechservicesamongthepopulationinChina.TheTAMmodelwasdevelopedbyDavisin1989basedon

ReseaRch on FactoRs IdentIFIcatIon In FIntech acceptance: LIthuanIa context 43

theTRAtheory.TAMevaluatesconsumerbehavior in termsof itsperceiveduseful-nessandeaseofuse.Itisproposedtocoveruserattitudesaswellasexternalvariablesinthemodel.TheTechnologyAcceptanceModel is one of the most successful theo-riesexaminingtheacceptanceofnewtech-nologies(Pabedinskaitė&Šliažaitė,2012).Theauthorsofthestudyincludednotonlyfactors of perceived usefulness and per-ceivedeaseofuse,butalsoconsumertrustinservicesandattitudestowardsFinTech.

Hu et  al. (2019) inChina applied theExtended Technology Acceptance Model(TAM2) to the bank’s consumer accept-ance of FinTech services. This modelwas additionally named ‘extended’ bythe authors because it is comprehensive,extending the applicability of the tradi-tionalTAMmodel,astheauthorsincludemore factors in the model that can encour-ageconsumerstoadoptnewtechnologies.Accordingto theauthors,extendedTAMcovers factors such as: government sup-port, perceived risk, consumer attitudes to FinTech, corporatebrandawareness,per-ceived ease of use, perceived usefulness, trust, and consumer innovation.

Ramos (2016) assessedFinTech adap-tation factors among young people in Portugal. The study was based upon theUnifiedTheoryofAcceptanceandUseofTechnology(UTAUT).Venkatesh,Morris,DavisandDavis(2003)developedaunifi-cationtheoryinwhichtheyintegratedthecomponents of eight technology accept-ance models and theories: TRA, TAM,the motivational model, TPB, combinedTAM-TPB, the model of PC utilization,innovation diffusion theory and social cognitive theory (Alomary & woollard,2015). However, this theory is criticizedfora largenumberof independentvaria-bles,whiletheauthorofthetheoryhimself

argues that this model is the most reli-ableinestimatingandpredictingtechnol-ogy adoption (Venkatesh et  al., 2003). Inthemeantime,Ramos(2016)inhisstudychose to include three factors in the design of the UTAUT model: perceived useful-ness, perceived ease of use, and financial literacy. A comparison of the models isgiveninTable1.

To be clear, all the models shown inTable 1 have their shortcomings. TRA isthe earliest generalmodel, which cannotbe adapted to assess the acceptance ofa particular technology. Momani and Jamous (2017) emphasize that themodelhasits’ownlimitationsduetothelackofadditionalvariables.

BasedontheTRAtheoryandtheTAMmodelhasbeendevelopedtopayattentionto the usefulness and easy use of technolo-gies.Althoughthismodelismoreaccuratethanthefirstone,itdoesnotavoidits’ownshortcomings. Ribokas and Burinskienė(2018) state: ‘TAM does not assess theimpact of unfulfilled consumer expecta-tionsontheconsumer’sfurtherbehavior’.TAMmodelisuseful,butmoreandmoreresearchers are trying to extend it, thus creatingevenmoredifficulties(Sharma&Mishra,2014).

The extended TAM2 model wasapplied in the study carried byHu et  al.(2019). In addition to the factors inher-entintheTAMmodel,suchasperceivedease of use and perceived usefulness, the authors included factors that are related to personal characteristics. The extendedTAM2model is usually considered to bemore complicated than the conventional one.

Theunified theory of acceptance anduse of technology is distinguished bythe fact that it integrates both TRA andTAM theories and six other theories of

Samanta GRAUŽINIENĖ, Dovilė KUIZINIENĖ 44

feasibility of new technologies and fore-castingoftheiradoption,theTAMmodelwas used in thiswork. Itwas decided touse a technology acceptance model iden-tical to themodel used byChuang et  al.(2016)inordertoachievecomparabilityofthe research.

Hypotheses of the Research

ThefactorscoveredbytheTAMmodelinthis research are perceived usefulness, ease of use, attitude, and behavioral intentionto use.

technology acceptance. As mentionedabove,thebiggestdrawbackoftheUTAUTmodel is a large number of independentvariables.Venkateshetal.(2003)statethatthe model is the most useful in assessing theprobabilityofnew technology imple-mentation. It is worth noting that thismodel incorporates a number of volatilefactors that may change according to dif-ferent circumstances, thus the intention to useorrejectFintechmaybetemporary.

GiventhenotionthatTRAisonlyapri-marygeneralmodelthatcannotbeappliedto assess a particular technology and that UTAUT is best suited for assessing the

Table 1Research on adaptation to financial technologies

Author Research Model Factors

Ryu(2018)‘whatmakesuserswillingorhesitanttouseFintech?’(Korea)

Theoryofreasonedaction(TRA)

1.Economicbenefit;2.Convenience;3. Service process;4. Financial risk;5. Legal risk;6. Security risk;7.Operationalrisk.

Chuanget al.(2016)

‘TheAdoptionofFintechService:TAMperspective.’(China)

Technologyacceptancemodel(TAM)

1.Trustinservices;2. Perceived usefulness;3. Perceived ease of use;4. User attitude.

Huet al.(2019)

‘AdoptionIntentionofFintech Services for BankUsers:AnEmpi-ricalExaminationwithanExtendedTechnologyAcceptanceModel.’(China)

Extended technology acceptance model (TAM2)

1. Government support;2. Perceived risk;3.Attitude;4.Companybrandawareness;5. Perceived ease of use;6. Perceived usefulness;7.Trust;8. User innovation.

Ramos(2016)

‘Accessingthedeter-minantsofbehavioralintention to adopt fin-tech services among the millennial generation.’ (Portugal)

Theunifiedtheoryofacceptance and use of technology(UTAUT)

1. Perceived usefulness;2. Perceived ease of use;3. Financial literacy.4. Demographic characteris-tics.

ReseaRch on FactoRs IdentIFIcatIon In FIntech acceptance: LIthuanIa context 45

Perceivedusefulness isdefinedas ‘theuser’s perceived conviction that the use of technology will increase the ability tobetter perform an action in a particularactivity (Pabedinskaitė&Šliažaitė, 2012).Perceived usefulness is an important crite-rioninassessingtheacceptanceofFinTechamongconsumers.ThisfactorcommonlyusedinTAM.TAM2andUTAUTmodelstherefore it is also included in the research TAM.

H1  – perceived usefulness has a sig-nificant positive effect on attitudes towards financial technologies.

Perceivedeaseofuseis‘thedesirethatthe chosen system should not requiremuch effort to perform a certain action’ (Pabedinskaitė&Šliažaitė,2012).Inotherwords, it is an easy-to-use financial ser-vices platform. The complex use of thefinancial technology platform can have a negative impact on service adoption, espe-ciallyamongolderpeople.Inprevioussci-entificstudies,easeofuseisdescribedasasimpleappdownload,easytolearnserviceperformance, and easy access to the nec-essary equipment. The perceived ease ofuse factoralso is included in theFinTechacceptance model in Lithuania.

H2  – perceived ease of use has a sig-nificant positive effect on attitudes towards financial technologies.

Anothercriterionthatiswidelyusedinthe FinTech acceptance research is trust.This factor is closely related toperceivedusefulnessandperceivedeaseofuse.Thetrustfactorisconsideredtobeaconsum-ers’belief thattheFinTechsector isrelia-ble,thattheirpersonalinformationissafeand all ordered services are performed correctly. ‘Many scholars have confirmedthat users’ trust of services plays an impor-tant role in adoption decision-making in the context of Fintech.’ (Hu et al., 2019),

therefore this factor is included in the research model in Lithuania.

H3  – trust in services has a significant positive effect on attitudes towards FinTech.

Attitude is commonly determinedby ‘perceived usefulness and ease of use’(Chuanget al.,2016).Thesefactorsdeter-mine consumer attitudes, which, accord-ing to previous research, have a significant impactonconsumerbehavioralintentiontousetechnology.SimilartoChuanget al.(2016) considerations, the attitude wasdetermined by perceived usefulness per-ceived easeof use, and trust factors.Theattitude factor is described as the beliefthatFinTechservicesareagoodidea,goodconsumers’ experience with FinTech ser-vices, and consumers’ interest inFinTechservices in general.

H4  – consumers‘ attitude towards Fin-Tech has a significant positive effect on their behavioral intention.

The factors included in themodel andtheir evaluation criteria are presented in Table2.

Methodology

The aim of the research: to identify and evaluate the factors determining the acceptanceofFinTechinLithuania.

The research data:datawascollectedby conducting an online survey. Thesamplesizewascalculatedusingacalcula-torwiththeformula(Kardelis,2002):

(1)where:n –asamplesize;∆ –themargin

of error; N –apopulationsize.

Samanta GRAUŽINIENĖ, Dovilė KUIZINIENĖ 46

According to the estimations, at least384respondentswereneededtoensurethequalityofthestudywith95%probability.

During the research, 416 respondents were interviewed. The questionnaire wasposted online from March 17, 2020, to April 17, 2020. It consistedof thedemo-graphic characteristics of the respondents, five factors (perceived usefulness, per-ceived ease of use, trust, attitude, behav-ioral intentiontouse) thatdeterminetheacceptanceofFinTech.Respondentsweresuggested to rate their opinion accord-ing to the five-point Likert scale in order

to express their consent to the statements presented (1  - agree; 2  - partially agree,3 –hasnoopinion,4 -partiallydisagree,5 -disagree).

The stages of the study: 1) SurveyofLithuanian residents (online); 2) Pro-cessing of collected data and exploratory factor analysis; 3) Analysis of confirma-toryfactorandstructuralequationsmodelpaths coefficients.

Research model and hypotheses. Theresearchmodelisbasedonthetechnologyacceptancemodel (Davis, 1989) and it isshowninFigure1.

Table 2

Factor evaluation criteria and hypotheses

Hypothesis Factor Criteria

H1 –perceiveduseful-ness has a significant positive effect on atti-tudestowardsfinancialtechnologies.

Perceived usefulness (PU)

PU1 -IcanuseFinTechservicesanywhereandanytime.PU2 -FinTechservicesmakelifemoreconvenient.PU3 -TheservicesofferedbyFinTechmeetmyneeds.PU4 -FinTechservicesareperformedquicklyandsavesmetime.

H2 –perceivedeaseofuse has a significant positive effect on atti-tudestowardsfinancialtechnologies.

Perceived ease of use (PEU)

PEU1 -UsingFinTechservicesisnotcomplicated.PEU2 -EasytohavetheequipmentneededtouseFinTechservices(mobilephone,tablet,computer,Internet).PEU3 -LearningtouseFinTechservicesiseasyanddoesnotrequiremuchtime.PEU4 -FinTechappiseasytodownloadandinstall.

H3 –trustinserviceshas a significant posi-tive effect on attitudes towardsFinTech.

Trust(TRU)

TRU1 -IbelievetheFinTechsectorisreliable.TRU2 -IbelievethatFinTechcompaniesprotectmypersonalinformation properly.TRU3 -Ibelievethatmyorderedservicesareperformedcorrectly.

H4 –consumers‘attitudetowardsFin-Techhasasignificantpositive effect on their behavioralintention.

Attitude(ATT)

ATT1 -IhavenonegativeexperiencewithFinTechservices.ATT2 -IaminterestedintheFinTechsectorandtheservicesit offers.ATT3 -IthinkofferingservicesinaFinTechwayisagoodidea.ATT4 -IlikeFinTechservices.

H4 –consumers‘attitudetowardsFin-Techhasasignificantpositive effect on their behavioralintention.

Behavioralintention(BI)

BI1 -ItendtouseFinTechservices.BI2 -IintendtouseFinTechservicesinthefuture.

ReseaRch on FactoRs IdentIFIcatIon In FIntech acceptance: LIthuanIa context 47

Research hypotheses: H1  – perceivedusefulness (PU)hasa significantpositiveeffectonattitudestowardsfinancialtech-nologies; H2 –perceivedeaseofuse(PEU)has a significant positive effect on attitudes towardsfinancialtechnologies; H3 –trustin services (TRU) has a significant posi-tive effect on attitudes towards FinTech; H4  – consumers‘ attitude (ATT) towardsFinTechhasasignificantpositiveeffectontheirbehaviouralintention(BI).

Research method.StructuralEquationModeling (SEM) consists of three parts:1)  Exploratory factor analysis (EFA); 2)Confirmatory factor analysis (CFA); 3)Path analysis.

The exploratory factor analysis responds to the correlation between thedata and divides the observed variablesinto groups that have a unifying factor (Čekanavičius & Murauskas, 2002). Thepurpose of the exploratory factor analysis

istocheckthesuitabilityofthelatentfac-tors used in the study and to perform cor-rectivefactorsifnecessary.TheMaximumLikelihood method is used to separate the factors. This method maximizes thelikelihood of excluding the most similar criteria.Promaxwasusedas the rotationscheme for non-orthogonal factors. Thisrotationschemeisconsideredtobemath-ematically simpler and more commonly used in the factor analysis. The purposeof the confirmatory factor analysis is to evaluate the parameters and validity of theresearchmodel.whenperformingthepath analysis, the coefficients and signifi-canceoftherelationshipbetweenthecon-sidered factors are determined.

Structuralequationmodeling isa sta-tistical method that seeks to elucidate the relationship between multiple variablesusing a variable variation matrix, multi-ple regression analysis, path analysis, and

Fig. 1. Research model

trust, attitude, behavioral intention to use) that determine the acceptance of FinTech. Respondents were suggested to rate their opinion according to the five-point Likert scale in order to express their consent to the statements presented (1 - agree; 2 - partially agree, 3 – has no opinion, 4 - partially disagree, 5 - disagree).

The stages of the study: 1) Survey of Lithuanian residents (online); 2) Processing of collected data and exploratory factor analysis; 3) Analysis of confirmatory factor and structural equations model paths coefficients.

Research model and hypotheses. The research model is based on the technology acceptance model (Davis, 1989) and it is shown in Figure 1.

Fig. 1. Research model

Research hypotheses: H1 – perceived usefulness (PU) has a significant positive

effect on attitudes towards financial technologies; H2 – perceived ease of use (PEU) has a significant positive effect on attitudes towards financial technologies; H3 – trust in services (TRU) has a significant positive effect on attitudes towards FinTech; H4 – consumers‘ attitude (ATT) towards FinTech has a significant positive effect on their behavioural intention (BI).

Research method. Structural Equation Modeling (SEM) consists of three parts: 1) Exploratory factor analysis (EFA); 2) Confirmatory factor analysis (CFA); 3) Path analysis.

The exploratory factor analysis responds to the correlation between the data and divides the observed variables into groups that have a unifying factor (Čekanavičius & Murauskas, 2002). The purpose of the exploratory factor analysis is to check the suitability of the latent factors used in the study and to perform corrective factors if necessary. The Maximum Likelihood method is used to separate the factors. This method maximizes the likelihood of excluding the most similar criteria. Promax was used as the rotation scheme for non-orthogonal factors. This rotation scheme is considered to be mathematically simpler and more commonly used in the factor analysis. The purpose of the confirmatory factor analysis is to evaluate the parameters and validity of the research model. When performing the path analysis, the coefficients and significance of the relationship between the considered factors are determined.

Structural equation modeling is a statistical method that seeks to elucidate the relationship between multiple variables using a variable variation matrix, multiple regression

Samanta GRAUŽINIENĖ, Dovilė KUIZINIENĖ 48

confirmatoryfactoranalysis.Thismethodcanexplainthecausalrelationshipbetweenindependentanddependentvariablesandiswidely used in the fields of behavioralscienceofindividuals(Huetal.,2019).

The minimum significance level atwhich the hypotheses are confirmed is0.05.

Limitations. The survey sample doesnot cover the entire Lithuanian popula-tion. The sample consists of 16.3% menand83.7%women.Itshouldbenotedthat50.2 % of survey respondents were per-sonsagedfrom16to24years.Thestudywasconductedontheinternet –thiscouldbethecauseofthelackofresponsesfromthe elderly and those without computerliteracy.

Results

Resultsarepresentedaccordingtothere-search methodology:

1)Dataanalyses;2)Modelconstructionandhypothesis

testing;3)ComparisonwiththeresultsofFin-

TechacceptancestudyinChina.In order to achieve the aim of the

research, 416 respondents were inter-viewedandtheresultsobtainedwerepro-cessed.ThedistributionofrespondentsbydemographicdataispresentedinTable3.

The age distribution of respond-ents was uneven. There were 83.7%were women and 16.3%men among therespondents. Half of all surveyed persons were 16-24 (50.2%), 29.3%  - 25–34 yearsold. According to the education criteria,the respondents were distributed as fol-lows: basic education  - 2.9%, secondaryeducation  – 18.5%, vocational educa-tion -6.5%,incompletehighereducation -23.8%, higher education  – 48.3%. Thus,it could be considered that the majorityof respondents have higher education.

Table 3

Distribution of respondents by demographic data (N = 416)

Variable Description Distribution Distribution (%)

GenderFemale 348 83.7

Male 68 16.3

Age

16–24 209 50.2

25–34 122 29.3

35–44 57 13.7

45–51 17 4.1

≥ 52 11 2.6

Education

Basic 12 2.9

Secondary 77 18.5

Vocational 27 6.5

Incompletehigher 99 23.8

Higher 201 48.3

Household income≤ 1000 eur. 227 54.6

> 1000 eur. 189 45.4

ReseaRch on FactoRs IdentIFIcatIon In FIntech acceptance: LIthuanIa context 49

Accordingtothereceivedmonthlyincome,the largest part of the respondents received 501 -1000Eur(32.45%)and1001 -2000Eur (30.29%). Combining householdsreceiving up to 1000 Eur and more than 1000Eur income,anevendistributionofrespondentswasobtained.Lessthan1000Eur receiving households accounted for 54.6% out of the sample, more  - 45.4%.Out of all respondents, 65.9% indicatedthattheyhadusedtheservicesofFinTech,31.5% -didnotuse,and2.6%ofrespond-entswerenotsurewhethertheyhadusedFinTechservicesorhadnot.

The reliability of the developed ques-tionnaire used in the study was checkedby calculating the coefficient Cronbach’salpha. The Cronbach’s alpha coefficientsof the factorsused in thestudyare:PU -0.940, PEU  - 0.925, ATT  - 0.790, TRU  -0.856, BI  - 0.897, their values are higherthan 0.7, that iswhy it is concluded thatthequestionnairewasdesigned correctly,

andtheanswerscanbeusedinthefollow-ing steps of the research.

During the exploratory factor analysis, 5 factorsweredistinguishedaccording tosimilar indicator variables. Adjustmentsare made to the resulting model matrix until a clean model matrix is obtained(Table 4). PEU2, PEU4, TRU3, ATT1indicator variables were removed duringadjustments.Thecorrectiondidnotaffectthenumberofevaluatinghypotheses.

Kaiser-Meyer-Olkin (KMO) test wasperformed during the exploratory factor analysis –theresultsoftheanalysisshowedthatthecollecteddatacorrelatewitheachotherandcanbeusedinthefactoranaly-sis (KMO  - 0.946, χ2 -5306.146, df -78,p-value -0.000).

Afterremovingthecorrelatingindica-torvariablesandmakingsurethatthedatawassuitableforfurtherinvestigation,con-firmatoryfactoranalysiswasperformedintheAMOSprogram.

Table 4 Model matrix

Perceived usefulness (H1)

Perceived ease of use (H2)

Trust (H3)

Attitude(H4)

Behavioral intention (H4)

PU1 0.771

PU2 0.955

PU3 0.830

PU4 0.834

PEU1 0.952

PEU3 0.640

TRU1 0.660

TRU2 0.999

ATT2 0.830

ATT3 0.854

ATT4 0.505

BI1 0.879

BI2 0.533

Samanta GRAUŽINIENĖ, Dovilė KUIZINIENĖ 50

In this analysis, the suitability of themodel is assessed using the most com-monly used indices: NFI, GFI, CFI,PCLOSE,RMSEA.Thevalueof χ² isnotappropriate for samples larger than 200, so theratioofχ²squaretodegreesoffreedom(df)isusedinstead.Thevaluesoftheindi-ces obtained, and their estimates used inpracticearepresentedinTable5.

The validity of the model helps toassess whether the model is suitable fortheevaluationoftheanalyzedproblemsornot.TheresultsofthemodelvalidityandreliabilityassessmentareshowninTable6.

Awangetal.(2015)separatethemodelvalidity into three parts: convergent valid-ity, construct validity, and discriminant validity. The AVE (Average Variance

Extracted)valueofallthefactorsisgreaterthan 0.5, so the convergent validity is sat-isfactory. The validity of the construct issatisfactory due to the values of the model fitindicesalreadyanalyzedinTable5.Thesquare root of the attitude factorAVE isless than the correlation between othervalues, so the discriminatory validity of thisfactorisquestionable.Nomoreappro-priate discriminant validity values wereobtainedduringmodeladjustments.GiventhatthesquarerootoftheapproachfactorAVEislessthanthemaximumcorrelationof0.004,itwasdecidednottoexcludethisfactor from the study.

The CR (Composite reliability) valueofallanalyzed factors isgreater than0.7,which means that the data reliability is

Table 5Confirmatory factor analysis model fit indices

Indices Value Threshold Conclusion

NFI 0,984 > 0,90 Valueisacceptable

CFI 0,994>0,95 –great >0,90 –good>0,80 –sometimespermissible

Valueisacceptable

GFI 0,970 > 0,95 Valueisacceptable

RMSEA 0,039<0,05 –good0,05 –0,10 –moderate>0,10 -bad

Valueisacceptable

PCLOSE 0,880 > 0,05 Valueisacceptable

χ²/df 1,645 < 3 Valueisacceptable

Table 6 Validity and reliability assessment

CR AVE PU ATT BI PEU TRU

PU 0.941 0.801 0.895

ATT 0.858 0.672 0.874 0.870

BI 0.900 0.818 0.865 0.849 0.904

PEU 0.920 0.852 0.865 0.768 0.795 0.923

TRU 0.861 0.756 0.687 0.752 0.722 0.679 0.870

ReseaRch on FactoRs IdentIFIcatIon In FIntech acceptance: LIthuanIa context 51

satisfactory.After reviewing and evaluat-ingallthecriteria, itwasdecidedtocon-sider the model applicable for furthermodeling of structural equations  - pathanalysis.

The structural equation model wasdrawn in the AMOS program and pathcoefficients were obtained, the results

showed that the path of perceived easeof use (PEU) and attitude (ATT) to Fin-Techisstatisticallyinsignificant(p -0.55;p > 0.05), therefore the hypothesis H2 is rejected.Forabettermodelfit,thispathisremoved.Anewmodelofstructuralequa-tionswasdeveloped(Figure2).

Fig.2. Structural equations model

Fig.2. Structural equations model

The reliability of the developed structural equation model was assessed by the previously mentioned model fit indices (Table 7). After evaluating the obtained model suitability parameters, it was concluded that the performed path analysis is reliable, and the obtained coefficients can be analyzed.

Table 7

Structural equations model fit indices

Indices Value Threshold Conclusion NFI 0,974 > 0,90 Value is acceptable

CFI 0,985 > 0,95 – great > 0,90 – good > 0,80 – sometimes permissible

Value is acceptable

GFI 0,951 > 0,95 Value is acceptable

RMSEA 0,048 < 0,05 – good 0,05 – 0,10 – moderate > 0,10 - bad

Value is acceptable

PCLOSE 0,150 > 0,05 Value is acceptable χ² / df 2,376 < 3 Value is acceptable

The results of the study show that perceived usefulness and trust explain 84% of the

population’s attitudes towards FinTech (R2 = 0.84). Moreover, the attitude evaluated on the basis of perceived usefulness, trust, and the individual three latent factors, explains 88% population's behavior intention to use FinTech (R2 = 0.88).

The conclusions of the hypotheses testing made out of the performed empirical analysis and the path coefficients are presented in Table 8.

Table 8

Hypothesis testing

Hypothesis Path Path coefficient C.R./ p-value Conclusion

H1 PU → ATT 0,70 9,95 / *** Hypothesis supported H2 PEU → ATT -0,04 -0,59/ 0,55 Hypothesis rejected

H1

H4

H3

Table 7Structural equations model fit indices

Indices Value Threshold Conclusion

NFI 0,974 > 0,90 Valueisacceptable

CFI 0,985>0,95 –great >0,90 –good>0,80 –sometimespermissible

Valueisacceptable

GFI 0,951 > 0,95 Valueisacceptable

RMSEA 0,048<0,05 –good0,05 –0,10 –moderate>0,10 -bad

Valueisacceptable

PCLOSE 0,150 > 0,05 Valueisacceptable

χ²/df 2,376 < 3 Valueisacceptable

Samanta GRAUŽINIENĖ, Dovilė KUIZINIENĖ 52

Thereliabilityof thedevelopedstruc-turalequationmodelwasassessedby thepreviously mentioned model fit indices (Table 7). After evaluating the obtainedmodel suitability parameters, it was con-cluded that the performed path analysis is reliable,andtheobtainedcoefficientscanbeanalyzed.

Theresultsofthestudyshowthatper-ceived usefulness and trust explain 84% of thepopulation’sattitudestowardsFinTech(R2=0.84).Moreover, theattitudeevalu-atedon thebasisofperceivedusefulness,trust, and the individual three latent fac-tors, explains 88% population’s behaviorintentiontouseFinTech(R2=0.88).

Theconclusionsofthehypothesestest-ing made out of the performed empirical analysis and the path coefficients are pre-sentedinTable8.

Perceived usefulness has a statistically significant effect on consumers’ approach to FinTech. The path coefficient of thesefactors is 0.70; p <0.05, therefore the per-ceived usefulness and attitude have a strong and statistically significant relation-ship. Hypothesis: H1  - perceived useful-ness(PU)hasasignificantpositiveeffectonattitudestowardsfinancialtechnologiesisconfirmed.Therefore,wecanconcludethattheperceivedbenefitsoftheservicesofferedbyFinTechformapositiveattitudetowardsfinancialtechnologies.

The impact of perceived ease of useon consumers’ attitudes towards finan-cial technologies is statistically insignifi-cant.Thesignificanceof thispath is0.55(higherthanthesignificancelevelusedinthestudy -p<0.05),thereforethehypoth-esis: H2 –perceivedeaseofuse(PEU)hasa significant positive effect on attitudes towardsfinancialtechnologiesisrejected.

TrustinFinTechisastatisticallysignifi-cant factor influencing consumer attitudes towardsthis innovation.Thevalueof thetrust and attitude path coefficient is 0.28, p <0.05. Thus, the hypothesis:H3  - trustin services (TRU) has a significant posi-tiveeffectonattitudestowardsFinTechisaccepted. Trust in financial technologiesinfluences attitudes towards them,whichmeans that if trust falls, the consumer atti-tudetowardsFinTechwillworsen.

The attitude of Lithuanian userstowards FinTech services is a statisti-cally significant factor that determines thebehavioralintentiontousethem.Thepath coefficient of these two factors is  -0.94, p <0.05. A high standardized pathcoefficient indicates that there is a strong and statistically significant relationship betweenattitudesandbehavioralintentiontouseFinTechservices.Thus,thehypoth-esis: H4  – consumers’ attitude (ATT)towardsFinTechhasasignificantpositiveeffectontheirbehavioralintention(BI)is

Table 8 Hypothesis testing

Hypothesis Path Path coefficient C.R./ p-value Conclusion

H1 PU→ATT 0,70 9,95 / *** Hypothesis supported

H2 PEU→ATT -0,04 -0,59/ 0,55 Hypothesis rejected

H3 TRU→ATT 0,28 6,54 / *** Hypothesis supported

H4 ATT→BI 0,94 20,34 / *** Hypothesis supported

Note: *** – p < 0,05.

ReseaRch on FactoRs IdentIFIcatIon In FIntech acceptance: LIthuanIa context 53

(2016)modelwhichwas used evaluatingthe financial technology acceptance in China.TheacceptanceofFinTechamongconsumers in Lithuania was comparedwiththeFinTechacceptance inChina. InChina the FinTech adoption rate is 87%(Ey,2019),thiscountrystandsoutforitsfrequentuseofFinTech.Lithuaniaarenotincluded in the countries listwhichhavehighestFinTechadoptionrates.Compari-son of the results of FinTech acceptancestudiesinLithuaniaandChina,willshowwhythisdifferenceoccurs.

In Lithuania and China, the studyis conducted with 95% probability. Therequiredsampleofthepopulationofboth,LithuaniaandChina,is -384.InthesurveyconductedinChina,440respondentswereinterviewed,inLithuania -416.AlthoughfewerpersonswereinterviewedinLithua-nia,therequiredsamplesizewasachieved.

Theresearchmodel inbothLithuaniaand China consists of five factors (per-ceived usefulness, perceived ease of use, trust in services, attitude, and behavioralintention to use). After the performedexploratory and confirmatory factor analyses, 11 latent factors remained in the Lithuanianmodel and 16 in theChinesemodel.

A comparison of the obtained pathcoefficientsandtheirsignificanceinbothstudiesispresentedinTable9.

Perceived usefulness in China has agreaterimpactonattitudestowardsfinan-cial technology services than in Lithuania. The statement is reflected by the higherpathcoefficientinChinathaninLithuania0.84 > 0.70. The perceived usefulness ofthe research has a statistically significant impact on attitudes towards innovativefinancial technology services.

Theimpactofperceivedeaseofuseonattitudes towards financial technologies

accepted.Themajority(89%)ofconsum-ers’ behavioral intention to use financialtechnologies is determined by their atti-tude towards this sector, the usefulness,and the trust of its services.

It should be noted that the influenceof perceived usefulness on the attitude of consumers is 2.5 times higher than the influence of trust: the path of perceived usefulness -0.70,trust -0.28.Asaresult,we can admit that the usefulness of theservices has a 2.5 times greater impact on consumerattitudes towardsFinTech thanconsumer trust in these services.

The research on factors identificationinFinTechacceptanceinLithuaniashowedthat Lithuanians tend to use financial tech-nologies more often due to their perceived usefulness, trust in the services, and gen-eral attitude towards the FinTech sector.Thestudyconfirmedthreehypotheses:

H1  - perceived usefulness (PU) hasa significant positive effect on attitudes towardsfinancialtechnologies.

H3 -trust inservices(TRU)hasasig-nificantpositiveeffectonattitudestowardsFinTech.

H4 -consumers‘attitude(ATT)towardsFinTechhasasignificantpositiveeffectontheirbehavioralintention(BI).

The perceived ease of use factor hasbeen identified as a statistically insignifi-cant factor determining the use of Fin-Tech in Lithuania. For Lithuanians, easyfinancial transactions do not give them the trust and desire to use financial technolo-gies. The hypothesis rejected during theresearch:

H2  - perceived ease of use (PEU) hasa significant positive effect on attitudes towardsfinancialtechnologies.

To have a broader and comparableview, the study was conducted using aresearchmodel identical toChuanget al.

Samanta GRAUŽINIENĖ, Dovilė KUIZINIENĖ 54

in Lithuania was assessed as statisticallyinsignificant,whileinChinatherelation-ship between these factors is statisticallysignificant. According to this statement,we can formulate a conclusion that thesimple and easy use of FinTech servicesfor Lithuanians does not affect the attitude towards these services, unlike personslivinginChina.

TheinfluenceoftrusttowardsFinTechis statistically significant in both studies,but inChina, it is stronger (0.42>0.28).The trust ordistrust ofChina citizens inservices has a stronger influence on the attitudetowardsthemthanLithuaniaciti-zens.Themorepeopletrustthesectorandarenotdisappointedwiththeservices,themorefavorabletheattitudeis.

The relationship between attitudestowardsfinancialtechnologiesandbehav-ioral intention to use them is statistically significant and the strongest among the analyzed paths in both Lithuanian andChinese studies. In Lithuania, comparedto China, the relationship between thesefactorsisstronger(0.94>0.86).Thus,theattitude defined as a factor consisting of three exogenous variables and three fac-torshasa strongeffecton thebehavioralintentiontousefinancialtechnologies.InLithuania, the attitude to financial tech-nologies has a stronger impact on people’s use of financial technology services than inChina.

AcomparisonofthemodelfitindicesofthemodeldevelopedinChinawiththemodel fit indices of the model conducted inLithuaniaispresentedinTable10.

A comparison of themodel’s fit indi-ces concludes that the fit of the study con-ducted in China is only satisfactory, butnot excellent.According to the thresholdof model fit indices used in the Lithuanian study, thevalues ofNFI,GFI,RMSEAintheChinesestudywouldbeunsatisfactory,butresearchersChuanget al.(2016)con-sidered the model indices satisfactory for further study due to the small difference betweentheobtainedvaluesandrequiredvalues.

Summarizing the results of the com-parative analysis of Lithuanian and Chi-nese financial technology acceptance sur-veys, thenumber of respondents in bothsurveyswassufficienttodrawconclusionsabout thecountries’populations.Acom-parisonoftheresearchresultsshowedthatthe factors promoting the acceptance of financial technologies in Lithuania and China are identical, except the factor ofperceived ease of use, which was identi-fied in the study as a statistically insignifi-cant factor determining the acceptance of financial technologies in Lithuania. Themodel fit indices of the study model in Lithuaniaarebetterthantheindicesofthestudymodel inChina, so it is concludedthat the results of the Lithuanian study are

Table 9 Comparison of path coefficients and their significance

Hypothesis Path Path coefficient in Lithuania

Path coefficient in China

C.R./ p-value(Lithuania)

C.R./ p-value(China)

H1 PU→ATT 0,70 0,84 9,95 / *** 4,66/***

H2 PEU→ATT -0,04 0,31 -0,59/ 0,55 2,15/***

H3 TRU→ATT 0,28 0,42 6,54 / *** 3,07/***

H4 ATT→BI 0,94 0,86 20,34 / *** 9,08/***

ReseaRch on FactoRs IdentIFIcatIon In FIntech acceptance: LIthuanIa context 55

more reliable than the results of Chuanget al.(2016)study.

Conclusions

The Fintech sector evaluation is one ofthe key factors for a country’s economic developmentduetoitsprofitability,com-petitiveness,andartificial intelligenceen-vironmentcreation.However,consumers’acceptance is one of the main aspects of Fintechgrowth.Evaluatingthesecircums-tances, it is crucial not only to identify but also accurately assess the factors de-termining the acceptance of FinTech inLithuania.

In this articleTechnologyAcceptanceModel  – TAM (Davis, 1989) is based onthe survey of residents) and Structuralequationmodeling –SEMwereused.Thefactors involved in this research are per-ceived usefulness, ease of use, attitude, and behavioral intention to use. InTAMandSEM modeling for each analyzed factorhypothesiswasformed.

when performing the study, a statis-tically significant model was developed,based on which three hypotheses wereconfirmed and one rejected (about ease

of use factor). Research has shown thatperceived ease of use has no influence on attitudestowardsfinancialtechnology.Onthe other hand, the influence of perceived usefulness on the attitude of consumers is 2.5 times higher than the influence of trust. According to this, it is concludedthat consumers’ behavioral intention touse financial technologies is more strongly drivenbytheperceivedusefulnessofser-vices than the trust factor in Lithuania. Thehighestpathcoefficient(0,94)offac-torsofconsumerattitudesandbehavioralintentiontouseFinTechservicesindicatesthat there is the strongest and the most sta-tistically significant relationship betweenattitudes and behavioral intention to useFinTechservices.

ThisresearchiscomparedwithChuanget  al. (2016) study inChina.The ‘easyofuse’ factor is the main difference of these researchers because in Lithuania case itwasstatisticallyinsignificantandinChinasignificant.Inbothstudies,the‘influenceoftheattitude’factorwasthelargest(0.86)andthe‘influenceoftheperceiveduseful-ness’ factor on attitudes is twice as largeas the ‘influenceof the trust’ factor.Thisresearch differs from the Chuang et  al.

Table 10Comparison of model fit indices

Indices Lithuanian model Chinese model Threshold

NFI 0,974 0,840 > 0,90

CFI 0,985 0,910>0,95 –great >0,90 –good>0,80 –sometimespermissible

GFI 0,951 0,820 > 0,95

RMSEA 0,048 0,070<0,05 –good0,05 –0,10 –moderate>0,10 -bad

PCLOSE 0,150 - > 0,05

χ²/df 2,376 1,960 < 3

Samanta GRAUŽINIENĖ, Dovilė KUIZINIENĖ 56

(2016)study –theLithuaniancaseshowedgreater model fit indicators results.

Thefutureresearchcouldbedevelopedusing an extended Technology Accept-ance Model, which would include morefactors in research, such as: consumer financial literacy, innovation, financial

risk, government support, service process, etc. According to the literature analysis,thesefactorsmaybeagoodstartingpointfor the model future development and better insight creation for Fintech sectorprogress.

References

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ThepapersubmittedApril29,2020PreparedforpublicationJune1,2020

ReseaRch on FactoRs IdentIFIcatIon In FIntech acceptance: LIthuanIa context 57

Samanta GRAUŽINIENĖ, Dovilė KUIZINIENĖ

FINANSINIŲ TECHNOLOGIJŲ PRIĖMIMO VEIKSNIŲ ĮTAKOS NUSTATYMO TYRIMAS LIETUVOJE S a n t r a u k a

Finansinės tehnologijos (santrumpa FinTech)  – taifinansinių paslaugų ir informacinių technologi-jų integracija, svarbine tik įmonės,bet irvalstybėsproduktyvumui,užimtumuibeikonkurencingumui.Įžvelgdamos finansinių technologijų inovatyvumąbeikuriamąvertę,pasauliovalstybėssiekiasudarytitinkamąmikroklimatąfinansiniųtechnologijųstar-tuoliamskurtis.Finansųsektorius,FinTechįsiliejusįLietuvosrinką,tikisinevienbiudžetopapilnėjimoišsumokėtųmokesčių,betirįsiliejimoįšaliesgyven-tojųkasdienįgyvenimą,prisidedantpriesklandesniokasdieniųfinansiniųpaslaugųvykdymo.Finansiniųtechnologijų sektorius priklausomas nuo vartoto-jų,dėl toFinTech startuoliams svarbu identifikuotiveiksnius,kuriesąlygojadažnesnįpasirinkimąnau-dotis finansinių technologijų įmonių paslaugomis.Šiame straipsnyje siekiama nustatyti ir įvertintiveiksnius, lemiančius finansinių technologijų priė-mimą Lietuvoje.

Mokslinėje literatūroje finansinių technologijųpriėmimui įvertinti dažniausiai naudojamas Da-vis  (1989) sudarytas technologijų priėmimomode-lis –TAM.Šismetodaspagrįstasgyventojųapklau-somisbei struktūrinių lygčiųmodeliavimu –SEM.TAM metodu tiriami veiksniai, darantys įtaką fi-nansinių technologijųpriėmimuiaratmetimui: su-vokiamas naudingumas, suvokiamas naudojimosi paprastumas, vartotojųpasitikėjimaspaslaugomis /suvokiama rizikabei vartotojųpožiūris, kuriais re-miantiskeliamoshipotezės.

Gyventojųapklausa, vykdyta internetu, atsklei-dė,kaddidesnįpolinkįnaudotisfinansinėmistech-nologijomis turi 25–34 m. amžiaus asmenys, gau-nantys didesnes namų ūkio pajamas (>1000 Eur),įgijęaukštąjįišsilavinimąaršiuometustudijuojantysvyrai.Lietuvosgyventojamstrūkstapasitikėjimofi-nansinių technologijų paslaugomis, nes, gyventojųnuomone, FinTech įmonės neužtikrina tinkamosvartotojųduomenųapsaugos.

Siekiant įvertinti iškeltas hipotezes, surinktiduomenys buvo apdoroti taikant tiriamąją bei pa-tvirtinančiąją faktorinę analizę, struktūrinių lygčiųmodelio kelių analizę. Tiriamosios faktorinės ana-lizės metu atlikti Bartlett’o sferiškumo ir Kaizerio-Mejerio-Olkinotestųrezultataiatskleidė,kadišskirti

veiksniaipaaiškina94,6proc.kintamųjųvertėsnuo-krypįnuovidurkio,irduomenysyratinkamifakto-rineianalizei.Atlikuspatvirtinančiąjąfaktorinęana-lizę,nustatyta,jogvisimodeliotinkamumoindeksai(NFI,GFI,CFI,PCLOSE,RMSEA,χ²/df)yra labaigeri,todėlmodelispuikiaitinkatolesneiSEManali-zei.Postruktūriniųlygčiųmodeliavimoatmesta H2 hipotezė, nes suvokiamas naudojimo paprastumasnėra statistiškai reikšmingas veiksnys tarp Lietu-vos gyventojų, darantis įtaką vartotojų požiūriui įfinansinių technologijų teikiamas paslaugas. Kitostyrimekeltoshipotezėsyrastatistiškaireikšmingos.Pastebėta, kad suvokiamo naudingumo įtaka var-totojų požiūriui 2,5 karto didesnė nei pasitikėjimoveiksnio įtaka, todėl daroma išvada, kad vartotojųpolinkį naudotis finansinėmis technologijomis sti-priauskatinasuvokiamaspaslaugųnaudingumasneipatikimumas.Vartotojųsusidarytopožiūrioirpolin-kionaudotisFinTechpaslaugomisveiksniųkelioko-eficientas(0,94)yradidžiausiasištyrimenagrinėtųryšių,–vadinasi,vartotojųpožiūrisdarolabaistipriąįtakąjųpolinkiuinaudotisarnesinaudotifinansinė-mis technologijomis.

LietuvojeatliktotyrimorezultatuspalyginussuChuang,LiuirKao(2016)tyrimorezultataispaste-bėta, kad iš finansinių technologijų priimtinumoveiksniųLietuvoje irKinijoje išsiskyrė tik suvokia-monaudojimopaprastumoveiksnys. Jis statistiškaireikšmingas Kinijoje, nors Lietuvoje nustatytas kaip statistiškai nereikšmingas. Kinijoje, kaip ir Lietuvoje, požiūrio veiksnio įtaka (kelio koeficientas) yra di-džiausia –0,86,osuvokiamonaudingumoveiksnioįtaka požiūriui du kartus didesnė nei pasitikėjimoveiksnio įtaka.Lyginant tinkamumo indeksus,pas-tebimasLietuvos tyrimomodeliopatikimumopra-našumas.

Tolesniuosetyrimuosetikslingafinansiniųtech-nologijų priėmimą analizuoti naudojant išplėstinįtechnologijųpriėmimomodelį.Šismodelissuteikiagalimybęįtyrimusįtrauktidaugiauveiksnių,būtent:vartotojų finansinį raštingumą, novatoriškumą, fi-nansinę riziką, valstybės paramą, paslaugų proce-są.RemiantisRyu (2018),Hu ir  kt. (2019),Ramos(2016)tyrimais,šieveiksniaigalidarytiįtakąvarto-tojųpožiūriuiįfinansinestechnologijas.