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10.15240/ ul/001/2021-2-007
FINTECH SERVICES AND FACTORS
DETERMINING THE EXPECTED BENEFITS
OF USERS: EVIDENCE IN ROMANIA
FOR MILLENNIALS AND GENERATION Z
Oc a ian Dospinescu1, Nicole a Dospinescu2,
Daniela-Ta iana Agheo ghiesei3
1 Alexand u Ioan Cuza Uni e si y, Facul y o Economics and Business Adminis a ion, Depa men o Business
In o ma ion Sys ems, Romania, ORCID: 0000-0002-5403-8050, [email p o ec ed];
2 Alexand u Ioan Cuza Uni e si y, Facul y o Economics and Business Adminis a ion, Depa men o Managemen
and Ma ke ing, Romania, ORCID: 0000-0002-7097-7365, [email p o ec ed];
3 Alexand u Ioan Cuza Uni e si y, Facul y o Economics and Business Adminis a ion, Depa men o Managemen
and Ma ke ing, Romania, [email p o ec ed].
Abs ac : The pu pose o his esea ch is o de ine he le el o signi icance o a ious indica o s
ha in luence he deg ee o consume sa is ac ion ega ding he use o FinTech echnologies and
se ices. The mos impo an ac o s ha in luence he le el o sa is ac ion when using FinTech
se ices we e conside ed: com o and ease o use, legal egula ions, ease o accoun opening,
mobile paymen s ea u es, c owd unding op ions, in e na ional money ans e s ea u es, educed
cos s associa ed wi h ansac ions, pee - o-pee lending, insu ances op ions, online b oke age,
c yp ocoins op ions and exchange op ions. The s udy was conduc ed on a sample o 162
esponden s, pe sons belonging o he Millennials and Gene a ion Z gene a ions. The alues
o he indica o s o di e en ca ego ies o use s o FinTech se ices and di e en ca ego ies o
gene a ions can be de e mined based on he s a is ical es s pe o med and he esul s ob ained
om he eg ession analysis. The alues o he indica o s a e he basic elemen s o de e mining
he eg ession model ha will help he FinTech se ice endo s o make pe sonalized decisions o
each ca ego y o use s so ha he le el o cus ome sa is ac ion is maximized. The s udy ca ied
ou wi hin he p esen a icle is he i s o i s kind o Romania, because up o his momen in he
specialized li e a u e he e a e no such s udies o Eas e n Eu ope. The esea ch we conduc ed aims
o ill he gap exis ing in he li e a u e and esponds o he expec a ions and needs o s akeholde s
in he FinTechs’ business a ea. The esul s o he a icle a e ele an o bo h s akeholde s and he
scien i ic communi y ha is conce ned abou he impac o FinTech echnologies.
Keywo ds: FinTech, Millennials, Gene a ion Z, FinTech cus ome s sa is ac ion.
JEL Classi ica ion: M31, M37.
APA S yle Ci a ion: Dospinescu, O., Dospinescu, N., & Agheo ghiesei, D.-T. (2021). FinTech
Se ices and Fac o s De e mining he Expec ed Bene i s o Use s: E idence in Romania o
Millennials and Gene a ion Z. E&M Economics and Managemen , 24(2), 101–118. h ps://doi.
o g/10.15240/ ul/001/2021-2-007
In oduc ion
The pu pose o his a icle is o iden i y he
ac o s ha ha e a decisi e in luence on he
le el o sa is ac ion o use s o FinTech se ices
in Romania. To explain he a iables ha ha e
an e ec , we conduc ed his s udy bo h in he
en i e popula ion o FinTech use s, as well as
di e en ia ed in o wo dis inc gene a ions:
Millennials and Gene a ion Z. F om he poin
o iew o age anges, consume s in he
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Millennials ca ego y a e also known in he
special y li e a u e (Aichne & Shal oni, 2019)
as Gene a ion Y, ha a e people bo n be ween
he 1980s and he mid-1990s. Acco ding o
Buco e chi e al. (2019), Gene a ion Z is he
demog aphic coho ollowing Millennials and
is made up o hose bo n be ween he second
hal o he 1990s and ea ly 2000s. These wo
gene a ions ha e di e en beha io s, p io i ies
and p e e ences when i comes o he use o
in o ma ion echnologies.
FinTech is an eme ging end ha combines
in o ma ion echnology wi h inancial se ices
(Lee & Jae Shin, 2018), wi h he main ea u e
being s ong inno a ion in he inancial indus y.
Specialized s udies (Ryu, 2018) conside ha
FinTech does no de ine a single sec o o
ac i i y, bu co e s he en i e a ea o inancial
p oduc s and se ices ha we e adi ionally
o e ed by banking ins i u ions. A ne e al.
(2015) de ine FinTech as a ange o p oduc s
and se ices o e ed by non-banking ins i u ions
ha use echnology dis up i ely. O he same
opinion a e Gombe e al. (2018) who ha e
come o he conclusion ha he classic banks
a e unde a eal assaul om he inno a ions
ealized by he newly c ea ed companies ha
gene a e he concep o “FinTech Re olu ion”,
pu ing in he ocus o end consume s and
he desi e o con inuously imp o e hei
expe iences. Cus ome s a e al eady in e ac ing
wi h inancial obo s ha a e capable o
sa is ying complex demands and making he
igh decisions depending on he si ua ions in
he clien ’s po olio. Acco ding o Zhang and
Kedmey (2018), so wa e obo s can de ec
audulen beha io s, help es ablish c i e ia
and alues o insu ances, make beha io al
p edic ions o help consume s o inancial
se ices.
The FinTech e e escence is con i med
by he au ho s o he li e a u e (Kashyap e al.,
2016) who es ima e ha by 2020 FinTech will
each a ma ke sha e o 20% in he inancial
se ices indus y. P essed by he compe i ion
om FinTechs, banks a e hea ily in es ing
in digi alisa ion in an a emp o e ain hei
exis ing cus ome s.
On he o he hand, Ash a and Bio -Paque o
(2018) show ha FinTechs a e ying o ge
cus ome s in wo ways: 1) by “ ec ui ing” he
cus ome s o he classic banks and 2) by
accessing he pe sons who we e no in he
co e age a ea o banks (people who a e
geog aphically dis an om bank b anches o
pe sons who o a ious easons do no quali y
o become a bank cus ome ). These dis up i e
ac ions can lead o alliances be ween classic
banks as well as be ween banks and FinTech
challenge s in an a emp o p ese e hei
cus ome base. The adop ion a es o FinTechs
a y by coun y; hus, Zigu a (2019) shows ha
he e a e coun ies wi h e y high adop ion a es
(China – 69%, India – 52%, he Uni ed Kingdom
– 42%, B azil – 40%) and coun ies wi h lowe
adop ion a es (Belgium – 13%, Luxembou g –
13%, Japan – 14%, Canada – 18%).
Behind hese adop ion a es o FinTech he e
a e majo di e ences ega ding he a i ude o
gene a ions o consume s. As a esul , in his
esea ch we ocus on highligh ing he ac o s
and a iables ha in luence he sa is ac ion and
p e e ences o use s om he 2 echnologically
ep esen a i e gene a ions: Millennials and
Gene a ion Z.
The a icle has he ollowing chap e s:
In oduc ion, Li e a u e Re iew, Da a and
Me hodology, Resul s, Discussion and
Conclusions.
1. Li e a u e Re iew
Assa zadeh and Abe oumand (2018) s a ed
ha FinTech is a ph ase ha e e s o he
combina ion o inancial se ices and in e ne -
based echnologies, being an excellen
example o inno a ion and indus ial in eg a ion.
Specialized s udies (e.g. Wonglimpiya a , 2017)
show ha a p esen , he banking landscape
is cons an ly changing unde he in luence o
in o ma ion echnologies. In his con ex , bo h
banks and non-bank compe i o s a e adop ing
FinTech wi h he e y clea pu pose o being
close o cus ome s. This app oach alls
wi hin he heo e ical amewo k de eloped by
Da is (1989), ha is, in he TAM ( echnology
accep ance model) model acco ding o which
he main ac o s ha in luence he decision o
adop he new echnologies a e: 1) pe cei ed
ease o use and 2) pe cei ed use ulness.
Ryu (2018) demons a ed ha end
consume s’ in en ion o use FinTech depends
on wo ca ego ies o ac o s: 1) pe cei ed
bene i and 2) pe cei ed isk. Fac o s such as
economic bene i , con enience and seamless
ansac ion a e included in he pe cei ed
bene i ca ego y. The pe cei ed isks all in o
ou ca ego ies: inancial isk, legal isk, secu i y
isk and ope a ional isk. In his app oach Hu e
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al. (2019) show ha he demand o he use s o
he se ices o e ed by he FinTech companies
depends e y much on he popula i y o he
in e ne and he a ailabili y o sma mobile
de ices. Also, go e nmen suppo can
in luence he deg ee o accep ance o FinTech
se ices by end consume s, while pe cei ed
isk has a nega i e impac on us in adop ion
and use. In e ms o ease o use, esea ch
shows ha i has no signi ican in luence on he
decision o adop FinTech se ices. In he same
line o ideas, Dospinescu e al. (2019) show
ha he decision o adop banking p oduc s and
se ices di e s acco ding o he age gene a ion
o which he inal consume s belong.
F om an a chi ec u al poin o iew, Lee
and Jae Shin (2018) conside ha he FinTech
ecosys em is made up o i e majo componen s:
(1) FinTech s a ups, (2) echnology de elope s,
(3) go e nmen , (4) inancial cus ome s and
(5) adi ional inancial ins i u ions. These
componen s con ibu e symbio ically o he
inno a i e p ocess, s imula e he economy,
encou age compe i ion and gene a e he need
o collabo a ion in he inancial indus y. Wi hin
his complex ecosys em, FinTech s a ups
ha e a cen al place because hey a e he
mos en ep eneu ial componen , gene a ing
inno a ions ha con inuously imp o e he
inancial indus y. Schul e and Liu (2018)
belie e ha adi ional banks a e no ye ully
p epa ed o he challenges gene a ed by
new in o ma ion echnologies in he con ex in
which decisions will inc easingly be made by
in elligen machines in a eas such as c edi isk
analysis, da a managemen and insu ance.
Since he 2000s, he inancial-banking
sys em has di e si ied s ongly and clea
unc ionali ies ha e been in oduced unde he
FinTech end (Wonglimpiya a , 2017; Kim e
al., 2016): in e ne ca d, online banking, digi al
paymen s sys ems, digi al money (such as
PayPal, Google Walle , Apple Pay, AliPay, LINE
Pay, WePay, M-PESA Mobile money), pee -
o-pee paymen s ia mobile banking. Many
o hese new ea u es ha e been p omo ed
especially by non-banking companies, in an
a emp o di e en ia e hemsel es om he
adi ional banks. The FinTech end causes
ce ain sys emic cha ac e is ics o change o e
ime and unde he in luence o ma ke size;
as he ma ke g ows h ough collabo a ion
be ween FinTech pa ne s, he cha ac e is ics
o he inno a ion p ocess change subs an ially.
Acco ding o Lee and Jae Shin (2018), FinTechs
a e going h ough a a o able e a in e ms o
legal egula ions as many go e nmen s a e
ying o suppo hei de elopmen so ha he
na ional inancial indus ies do no lag behind
he global ones. I is expec ed, howe e , ha
i FinTechs a ec adi ional inancial ma ke s,
go e nmen s will impose mo e s ingen ules.
Jag iani and John (2018) show ha
au ho i ies a ound he wo ld a e ocusing on
consume in e es and s i ing o p o ec hem,
ying o s ike a balance be ween inancial
s abili y and he need o suppo c ea ion
o an en i onmen conduci e o FinTech
inno a ions. F om he pe spec i e o he
secu i y and con iden iali y o ansac ions,
many consume s a e conce ned abou he
legal egula ions subjec o he ac i i ies o
he FinTechs. Resea ch conduc ed by Buchak
e al. (2018) shows ha FinTechs all in o he
shadow-banking ca ego y due o he ac ha
hey ake o e he asks ela ed o lending
ac i i ies; o mo gage lending, he quan i a i e
model highligh ed ha egula ion ep esen s
abou 60% o shadow-banks g ow h, and he
echnological componen con ibu es abou
30%. Wo ldwide, he s a is ics on in es men s
in FinTechs show imp essi e amoun s a he
Region Amoun o in es men s ($ billion) Numbe o deals
Global 111.8 2,196
Ame icas 54.5 1,245
US 52.5 1,061
Eu ope 34.2 536
Asia 22.7 372
Sou ce: own, da a p ocessed om KPMG (2019)
Tab. 1: In es men s in FinTechs (2018)
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le el o 2018, acco ding o o icial epo s
(KPMG, 2019), acco ding o he da a in Tab. 1.
F om he da a in Tab. 1 we can see ha
he amoun in es ed globally is conside able
(mo e han $111 billion), and he Uni ed S a es
has a sha e o abou 50% in his in es men
sec o . Also, i can be no ed ha globally he e
a e app oxima ely 2,200 FinTech deals, which
leads us o hink abou a e y wide di e si y o
inno a i e inancial ini ia i es. The same epo
(KPMG, 2019) shows ha in he las 6 yea s,
each egion has had an upwa d end in e ms
o he alues o he in es men s made.
Acco ding o E ns & Young (2019), one o
he main dis up i e aspec s o FinTechs is he
i s con ac wi h he cus ome ; he ease wi h
which new cus ome s can open a inancial
accoun encou ages mo e and mo e use s o
adop his ype o echnology. A he p esen
ime i has come o he si ua ion whe e he
ac i i y o opening a new ope a ional accoun
has a du a ion o he o de o minu es, and
he alida ion o he use s is done h ough
a i icial in elligence echnologies, which leads
o an inc ease in p oduc i i y and a signi ican
educ ion o he cos s o en olling new clien s.
Rega ding how FinTech echnologies a e
implemen ed, a s udy has been ca ied ou by
Du (2018), which has esul ed in he ac ha
in he con ex o he p oli e a ion o mobile
paymen s, hese echnologies a e o in e es
no only o he big banks, bu also o he
c edi unions. Thus, c edi ins i u ions ha ha e
highe -le el IT capabili ies bu a e acing lowe
pe o mance and s ong ins i u ional p essu es
a e mo e likely o adop mobile paymen s
se ices. Among he ad an ages o e ed in he
case o mobile paymen s, Kang (2018) shows
ha , unlike he exis ing bank paymen se ices,
he paymen se ice o e ed by FinTech allows
cus ome s using a adi ional bank o use an
independen and pe sonalized paymen se ice
which does no depend on he bank sys em.
An ob ious inno a ion o FinTechs is being
discussed by Magnuson (2018), who shows
ha hese companies ha e aken o ano he
le el how o dis ibu e capi al on he ma ke ,
h ough c owd unding pla o ms. Acco ding o
Cumming and Ho nu (2018), he concep o
c owd unding e e s o he phenomenon whe e
companies in ea ly s ages o de elopmen
(usually s a ups) seek o ob ain inancing
om la ge g oups o people h ough In e ne -
based echnologies (mos o en h ough social
ne wo ks o i al ma ke ing campaigns). Abu
Amuna e al. (2019) show ha he ole o
FinTechs and c owd unding pla o ms is an
impo an one in suppo ing and accele a ing
he de elopmen o new businesses. Bes e al.
(2013) es ima e ha he c owd unding indus y
will each $96 billion by 2025.
Gimpel e al. (2018) show ha in e na ional
money ans e s a e a success ul componen
o FinTech s a ups, because hey allow much
cheape mul i-cu ency ans e s (see Die z e
al., 2016) han h ough SWIFT o SEPA banking
sys ems. Also, hese in e na ional ans e s
made h ough FinTechs a e much as e . Issues
ela ed o ading cos s a e also add essed by
Coe zee (2018), who shows ha cus ome s
a e e y sensi i e o his aspec , being willing
o easily mig a e om a mo e expensi e
adi ional inancial se ices p o ide o an
al e na i e FinTech p o ide who o e s se ices
a lowe cos s. Cos educ ion is a di ec esul
o he ac ha FinTechs do no ope a e h ough
subsidia ies and physical o ices as adi ional
banks do. On he o he hand, Ozili (2018)
conduc ed a scien i ic esea ch which showed
ha in he case o de eloped o de eloping
economies, people wi h a iable o low incomes
a e willing o pay a highe cos o he se ices
o e ed by FinTech p o ide s; his op ion is due
o he con enience and ease o use in e ms o
FinTech se ices.
Gombe e al. (2018) show ha a ea u e
ha is eally in e es ing o many FinTech use s is
pee - o-pee (P2P) lending, which con en ional
banks a e no willing o o e . This ype o
loan e e s o he possibili y o making di ec
loans be ween he lende and he deb o , he
FinTech sys em being an in e media y dealing
wi h issues such as: iden i ying he bo owe s,
iden i ying he c edi o s, nego ia ing he di ec
loan ag eemen , highligh ing he paymen s o
debi and in e es , he insu ance loan con ac ,
eco e y o ou s anding amoun s. Lee and
Jae Shin (2018) and Williams-G u (2016)
no e ha an impo an aspec o his business
model is he ac ha FinTechs a e no di ec ly
in ol ed in lending (as wi h adi ional banks),
bu only in “connec ing” he c edi o wi h he
deb o . FinTechs now end o in eg a e in one
way o ano he wi h es ablished mic o inance
pla o ms, pla o ms ha ha e been analyzed
by Bollinge and Yao (2018). Acco ding o
Jag iani and Lemieux (2018), op ions ega ding
P2P loans we e pa icula ly success ul in a eas
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ha we e weake se ed by adi ional banks,
which means ha a new ca ego y o clien s has
been included in he inancial se ices sphe e.
In his way, FinTechs mani es hemsel es
as a ac o o inancial inclusion o a ious
ca ego ies o popula ion, which usually would
no ha e access o speci ic banking se ices.
In andem wi h he P2P lending op ion, Die z
e al. (2016) show ha some FinTechs also o e
he possibili y o make sa ings simila o bank
deposi s. Along he same lines, he p o ision o
online b oke age se ices by FinTechs o hei
clien s is also close , hus b inging he s ock
exchange o small in es o s close . FinTechs
ha e gained an inc easingly signi ican
ma ke sha e e en among adi ional clien s o
adi ional banks. As o Bi coin c yp ocoins, i
seems ha he le el o accep ance by end use s
is s ill qui e low and somewha below he le el
o media impac i has had o e he las 3 yea s.
Acco ding o Wonglimpiya a (2017), among
he ac o s ha inhibi he widesp ead adop ion
o his elec onic cu ency a e he ac ha
i is nei he ecognized no suppo ed by any
go e nmen al au ho i y, and legal egula ions
lack o p ohibi he use o his means o
paymen . Gi en he esea ch elemen s so a in
he li e a u e, a summa y o FinTechs’ ea u es
is p esen ed in Tab. 2.
FinTech ea u e Desc ip ion Au ho s
Digi al money PayPal, Google Walle , Apple
Pay, AliPay, LINE Pay, WePay,
M-PESA Mobile money
Wonglimpiya a , 2017; Lee & Jae
Shin, 2018; Du, 2018; Kang, 2018
Online-b oke age Managemen o s ock-exchange
asse s
Gombe e al., 2018; Kashyap
e al., 2016
Financial obo s Au oma ic messages and
answe s, in elligen inancial
decisions, aud de ec ion
Zhang & Kedmey, 2018; Gombe
e al., 2018
Pee - o-pee paymen s ia
mobile banking
Ins an money ans e s Wonglimpiya a , 2017; Kim e al.,
2016
Pee - o-pee (P2P) lending Cus omized lends Gombe e al., 2018; Magnuson,
2018
C yp o-cu encies Bi coin and assimila ed
c yp o-coins
Wonglimpiya a , 2017; I win &
Tu ne , 2018; Teckla, 2019
Online in e na ional money
ans e
T ans e s wi hin FinTech use s o
be ween FinTech use s and banks
Gimpel e al., 2018; Kashyap
e al., 2016
Sa ings Deposi s Gulamhuseinwala e al., 2015;
Die z e al., 2016
Ease o se ing up an accoun Accoun opening E ns & Young, 2019
Insu ance T a el insu ance, ca ds insu ance,
los -documen s insu ance, loan
insu ance
Dany e al., 2016; Gombe e al.,
2018
Reduced cos s Signi ican educed cos pe
ansac ion
Die z e al., 2016; Coe zee, 2018;
E ns & Young, 2019
Exchange Mul i-cu ency exchanges Oma o a, 2019; Haddad &
Ho nu , 2019
Secu i y & p i acy Sa e ansac ions, his o y o
ansac ions
Gai e al., 2018; Lee & Jae Shin,
2018
C owd unding Dona ions, ewa ds Fellände e al., 2018; Magnuson,
2018; Bes e al., 2013
Sou ce: own
Tab. 2: FinTech ea u e
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106 2021, XXIV, 2
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Among he challenges ha FinTechs ace,
Lee and Jae Shin (2018) iden i y he ollowing
main issues: in es men managemen ,
cus ome managemen , egula ion, echnology
in eg a ion, secu i y and p i acy, and isk
managemen . Zalan and Tou aily (2017) show
ha ano he challenge is due o he ac ha
adi ional banks and FinTechs ha e been
o ced o wo k oge he , which will lead o
a diminishing dis up i e e ec ha he FinTech
inno a ions ha e on hem a his momen . As
a esul , a majo challenge will be o FinTechs
o be able o main ain he accele a ed pace o
inno a ion in he con ex o collabo a ion wi h
Hypo hesis
numbe Hypo hesis desc ip ion P e ious esea ch
H1a Com o and ease o use ha e a posi i e impac on he
ul illmen o he bene i s expec ed by he cus ome s om
he use o FinTech se ices.
Dospinescu e al., 2019;
Da is, 1989; E ns & Young,
2019
H1b The exis ence o legal egula ions on FinTechs ha e
a posi i e impac on he ul illmen o he bene i s
expec ed by he cus ome s om he use o FinTech
se ices.
Lee & Jae Shin, 2018;
Jag iani & John, 2018; Van
Loo, 2018; Buchak e al.,
2018
H1c The ease wi h which a new inancial accoun is opened
has a posi i e impac on he ul illmen o he bene i s
expec ed by he cus ome s om he use o FinTech
se ices.
E ns & Young, 2019
H1d The exis ence o mobile paymen s op ions has a posi i e
impac on he ul illmen o he bene i s expec ed by he
cus ome s om he use o FinTech se ices.
Du, 2018; Kang, 2018;
Wonglimpiya a , 2017
H1e The exis ence o c owd unding op ions ha e a posi i e
impac on he ul illmen o he bene i s expec ed by he
cus ome s om he use o FinTech se ices.
Fellände e al., 2018;
Magnuson, 2018; Bes e al.,
2013
H1 The in e na ional money ans e s op ion has a posi i e
impac on he ul illmen o he bene i s expec ed by he
cus ome s om he use o FinTech se ices.
Die z e al., 2016; Gimpel
e al., 2018; Kashyap e al.,
2016
H1g The educed ope a ion cos s ha e a posi i e impac on
he ul illmen o he bene i s expec ed by he cus ome s
om he use o FinTech se ices.
Ozili, 2018; E ns & Young,
2019; Coe zee, 2018
H1h The exis ence o pee - o-pee , (P2P) lending op ions
has a posi i e impac on he ul illmen o he bene i s
expec ed by he cus ome s om he use o FinTech
se ices.
Gombe e al., 2018;
Magnuson, 2018
H1i The insu ances included in he sys em ha e a posi i e
impac on he ul illmen o he bene i s expec ed by he
cus ome s om he use o FinTech se ices.
Dany e al., 2016; Gombe
e al., 2018
H1j The online-b oke age op ions ha e a posi i e impac on
he ul illmen o he bene i s expec ed by he cus ome s
om he use o FinTech se ices.
Gombe e al., 2018;
Kashyap e al., 2016
H1k The exis ence o c yp ocoins op ion has a posi i e
impac on he ul illmen o he bene i s expec ed by he
cus ome s om he use o FinTech se ices.
Wonglimpiya a , 2017; I win
& Tu ne , 2018; Teckla, 2019
H1l The exchange op ion has a posi i e impac on he
ul illmen o he bene i s expec ed by he cus ome s om
he use o FinTech se ices.
Oma o a, 2019; Haddad &
Ho nu , 2019
Sou ce: own
Tab. 3: Resea ch hypo heses – indica o s
EM_2_2021.indd 106 31.5.2021 10:33:30
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banks. Ozili (2018) conside s ha a u u e
di ec ion in esea ching he challenges ela ed
o FinTechs is he s udy o he ela ionship
be ween economic c ises and digi al inance, in
he ligh o how digi al inance can con ibu e o
imp o e he inancial con agion in he e en o
a c isis.
2. Da a and Me hodology
2.1 Aim and Hypo heses
The pu pose o his scien i ic esea ch is o de ine
he in luence and con ibu ion o he ollowing
12 indica o s: 1) com o and ease o use, 2)
he exis ence o legal egula ions on FinTechs,
3) he ease wi h which a new inancial accoun
is opened, 4) mobile paymen s, 5) exis ence o
c owd unding op ions, 6) in e na ional money
ans e s, 7) ope a ing cos s, 8) pee - o-pee
(P2P) lending, 9) insu ances included in he
sys em, 10) online-b oke age, 11) c yp ocoins,
12) exchange.
The ini ial hypo hesis is ha he
a o emen ioned ac o s ha e a signi ican
in luence on he decision o adop FinTech
se ices by cus ome s in Romania. In addi ion,
in his esea ch we will es he dependence
o hese indica o s on he socio-demog aphic
a iables, such as: educa ion le el, age,
mon hly income le el, gende , use o igin ( u al
s. u ban).
As we p esen ed in he li e a u e e iew
chap e , mos p e ious s udies ha e analyzed
hese ac o s indi idually o in small g oups o
indi idual ac o s. The majo con ibu ion o
ou esea ch lies in he ac ha we a e ying
o p o ide a b oade pic u e o he numbe o
ac o s ha in luence he decision o adop
FinTech se ices and echnologies. Each o
he 12 selec ed indica o s will be e alua ed by
a sepa a e hypo hesis, acco ding o Tab. 3.
In addi ion o he abo e, he cha ac e is ics
ela ed o he le el o inancial educa ion,
he deg ee o banking de elopmen , he
pu chasing powe as well as he demog aphic
cha ac e is ics, may cause di e en decisions
om he cus ome s depending on he
segmen s o which hey a e pa . Taking in o
accoun he di e ences be ween he obse ed
cha ac e is ics, he esea ch will ocus on
iden i ying he di e ences be ween consume s
ha a e di e en in e ms o socio-demog aphic
a iables: educa ion le el, age, mon hly income
le el, gende , cus ome ’s o igin ( u al s.
u ban). As a esul , we p opose he ollowing se
o esea ch hypo heses, acco ding o Tab. 4.
2.2 Measu emen Va iables
and Ins umen
The esea ch is based on a se o a iables ha
allow he con i ma ion o ejec ion o he esea ch
hypo heses. The independen a iables a e he
ollowing indica o s: com o and ease o use,
he exis ence o legal egula ions on FinTechs,
he ease wi h which a new inancial accoun
is opened, mobile paymen s, exis ence o
c owd unding op ions, in e na ional money
ans e s, ope a ing cos s, pee - o-pee (P2P)
lending, insu ances included in he sys em,
online-b oke age, c yp ocoins, exchange. The
g ouping a iables a e: educa ion le el, age,
Hypo hesis
numbe Hypo hesis desc ip ion
H2a Bene i s expec ed by cus ome s h ough he use o FinTech se ices di e acco ding
o age.
H2b Bene i s expec ed by cus ome s h ough he use o FinTech se ices di e acco ding
o educa ion le el.
H2c Bene i s expec ed by cus ome s h ough he use o FinTech se ices di e acco ding
o income le el.
H2d Bene i s expec ed by cus ome s h ough he use o FinTech se ices di e acco ding
o gende .
H2e Bene i s expec ed by cus ome s h ough he use o FinTech se ices di e acco ding
o cus ome ’s o igin ( u al s. u ban).
Sou ce: own
Tab. 4: Resea ch hypo heses – demog aphic ac o s
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108 2021, XXIV, 2
Finance
mon hly income le el, gende , cus ome ’s o igin
( u al s. u ban).
The decision o adop FinTech se ices was
analyzed h ough a ques ionnai e con aining 17
ques ions: 5 ques ions e e o he demog aphic
cha ac e is ics o he g oup and 12 ques ions
e e o he independen a iables. Responden s
we e asked o assess o wha ex en each
indica o is impo an in he expec ed bene i s
o using FinTech se ices and echnologies; he
ques ions we e cons uc ed on he Like scale,
whe e he alue 1 means ha he measu ed
a iable has no impo ance in he decision o
adop FinTechs, while he alue 5 means ha
he a iable has an inc eased impo ance
in his decision. The ques ions allowed
esponden s o choose a alue om 1 o 5 o
each esponse associa ed wi h he a iable:
1) How impo an a e he com o and ease o
use in he decision o adop FinTech? 2) How
impo an is he exis ence o legal egula ions
on FinTechs o you in he decision o adop
FinTech? 3) How impo an is he ease wi h
which a new inancial accoun is opened in he
decision o adop FinTech? 4) How impo an is
he mobile paymen s op ion in he decision o
adop FinTech? 5) How impo an is he op ion
ega ding in e na ional money ans e s in he
decision o adop FinTech se ices?
To e i y he eliabili y o he ques ionnai e,
we applied he C onbach’s Alpha es . The es
e i ies he in e nal consis ency om he poin o
iew o he indi idual sco es and he agg ega e
sco e. The alues o C onbach’s Alpha es is
0.610 and acco ding o Ta akol and Dennick
(2011), his alue con i ms ha ou i ems a e
accep able.
2.3 Resea ch Popula ion, Sampling
Me hod and Sample
The esea ch popula ion is ep esen ed by use s
o FinTech echnologies and se ices, who a e
membe s o Millennials and Gene a ion Z. The
sampling me hod used was he Pu posi e
Sampling. The esea che s conside ed he
speci ic cha ac e is ics, quali ies, knowledge
and expe ience o he esponden s he ele an
c i e ia o he esea ch (E ikan e al., 2016).
This esea ch was based on he
esponses o 162 esponden s. The numbe
o esponden s is highe han he minimum
numbe (139) sugges ed by Raoso (2019)
o a con idence le el o 95%. The dis ibu ion
o esponden s shows a no mal dis ibu ion,
e lec ing he p ope ies o he en i e
popula ion in he analyzed segmen . In e ms
o gende , he sample consis s o 45.7% male
esponden s and 54.3% emale esponden s.
F om he poin o iew o he income le el,
8.6% o he esponden s ha e below a e age
incomes, 69.2% ha e a e age incomes and
22.2% ha e abo e a e age incomes. F om
he poin o iew o he o igin ( u al s. u ban),
77.8% o he esponden s come om he u ban
en i onmen and 22.2% om he u al a ea.
Mos esponden s ha e a uni e si y deg ee
(63.6%), and 31.5% o hem ha e comple ed
hei mas e s s udies, while 1.2% ha e high
school and 3.7% doc o al s udies. In e ms o
age, 48.8% a e om he Gene a ion Z ca ego y,
while 51.2% a e om he Millennials ca ego y.
2.4 P ocedu e and S a is ical Analysis
o Da a
The ques ionnai e was applied in July, Augus
and Sep embe 2019 in Romania. The da a
collec ed om he esponden s we e p ocessed
wi h IBM SPSS S a is ics e sion 21. The
answe s ob ained om he esponden s we e
analyzed by he desc ip i e s a is ics me hod,
being p esen ed a e age alues as well as
de ia ion o each a iable. The accu acy
o he se o hypo heses is analyzed using
a ious s a is ical es s: Pea son co ela ion,
mul i a ia e analysis o a iance and mul iple
eg ession analysis and modeling.
3. Resul s
The alues o he desc ip i e s a is ics o
he dependen a iable ( he sa is ac ion le el
ega ding he use o FinTech se ices) and he
speci ic indica o s a e p esen ed in Tab. 5. As
i can be seen, he esponden s conside ha
he le el o sa is ac ion wi h he use o FinTech
se ices depends la gely on educed cos s
(M = 4.59), mobile paymen s (M = 4.53), a ached
insu ances (M = 4.43), ease o opening a new
inancial accoun (M = 4.15) and in e na ional
money ans e op ions (M = 4.23). In he same
con ex , low sa is ac ion is associa ed wi h
c owd unding op ions (M = 1.53), c yp ocoins
op ions (M = 1.56), online b oke age (M = 1.81),
and pee - o-pee lending op ions (M = 2.40).
These alues ob ained om ou esea ch
con i m some p e ious pa ial esea ch on
educed cos s (Coe zee, 2018; Die z e al.,
2016), a ached insu ances (Dany e al., 2016;
Gombe e al., 2018) and in e na ional money
EM_2_2021.indd 108 31.5.2021 10:33:30
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Finance
ans e op ions (Gimpel e al., 2018; Kashyap
e al., 2016).
To es hypo heses H1a – H
1l, mul iple
linea eg ession analysis was used o y o
p edic he dependen a iable (sa is ac ion
le el in using FinTech se ices) based on
a se o independen a iables: com o
and ease o use, legal egula ions, ease o
Indica o s Minimum Maximum Mean S d. de ia ion
Com o AndEaseO Use 1.00 5.00 3.7963 1.0224
LegalRegula ions 1.75 5.00 3.8426 0.7032
EaseAccoun Open 1.75 5.00 4.1543 0.6219
MobilePaymen s 2.00 5.00 4.5309 0.6419
C owd unding 1.00 5.00 1.5340 0.8522
In e na ionalMoneyT ans e s 1.00 5.00 4.2346 0.7765
ReducedCos s 2.75 5.00 4.5941 0.6716
P2PLending 1.00 5.00 2.4074 1.2538
Insu ances 2.00 5.00 4.4321 0.5963
OnlineB oke age 1.00 5.00 1.8148 1.0469
C yp ocoinsOp ion 1.00 5.00 1.5617 0.9119
ExchangeOp ion 1.00 5.00 4.1790 0.8626
Sa is ac ionLe elFinTech 3.00 5.00 4.2654 0.6185
Sou ce: own
Tab. 5: Values o desc ip i e s a is ics o dependen a iable and indica o s
Model S anda dized coe icien s TSig.
Be a
Com o AndEaseO Use −0.107* −2.024 0.045
LegalRegula ions 0.141* 2.163 0.032
EaseAccoun Open −0.013 −0.241 0.810
MobilePaymen s 0.136* 2.117 0.036
C owd unding 0.055 1.077 0.283
In e na ionalMoneyT ans e s 0.128* 2.370 0.019
ReducedCos s 0.460** 8.003 0.000
P2PLending 0.052 0.973 0.332
Insu ances 0.177** 3.179 0.002
OnlineB oke age 0.080 1.514 0.132
C yp ocoinsOp ion 0.009 0.171 0.865
ExchangeOp ion 0.106* 2.059 0.041
Sou ce: own
No e: * signi ican a he le el 5%; ** signi ican a he le el 1%.
Tab. 6: Con ibu ion o independen a iables o dependen a iable desc ip ion
EM_2_2021.indd 109 31.5.2021 10:33:30
116 2021, XXIV, 2
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