Mobile banking: a study on adoption stages using Government Adoption Model (GAM) and the role of demographic moderators: factors influencing Portuguese consumers’ adoption
Abstract
The present dissertation was developed with the objective of finding the most relevant factors for adoption of mobile banking, at each service stage – static, interaction, and transaction stages. After reviewing the literature addressing mobile banking and its adoption, it was implemented a revised version of GAM model, composed by eleven independent variables, grouped in four main constructs - attitude to use, ability to use, assurance to use, and adherence to use. Given the hypothesis drawn, a questionnaire was applied to Portuguese consumers and a sample of 208 respondents was achieved. Thus, the data gathered was analyzed under path analysis, with SmartPLS 3, by defining the three models separated. It was found that perceived functional benefit is the most relevant predictor at both static and transaction stages, and availability of resources is the most relevant predictor at the interaction stage. In addition, five demographic variables were considered as moderators for mobile banking adoption at each stage – age, gender, education level, income level, and occupation. After verifying the moderator variables in terms of measurement invariance, only gender presented good results, meaning that the other moderators had to be disregarded. By applying multigroup analysis, and given the findings of the most important factors for the adoption of mobile banking at the three presented stages, only the relation between PA and MBA-S was significantly moderated by gender.
Full text
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Mobile banking: a s udy on adop ion s ages using
Go e nmen Adop ion Model (GAM) and he ole o
demog aphic mode a o s
Inês C is ina A onso Cab i a
Fac o s in luencing Po uguese consume s’ adop ion
Disse a ion epo p esen ed as pa ial equi emen o
ob aining he Mas e ’s deg ee in In o ma ion
Managemen , wi h a specializa ion in Knowledge
Managemen and Business In elligence
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NOVA In o ma ion Managemen School
Ins i u o Supe io de Es a ís ica e Ges ão de In o mação
Uni e sidade No a de Lisboa
MOBILE BANKING: A STUDY ON ADOPTION STAGES USING
GOVERNEMENT ADOPTION MODEL (GAM) AND THE ROLE OF
DEMOGRAPHIC MODERATORS
FACTORS INFLUENCING PORTUGUESE CONSUMERS’ ADOPTION
by
Inês C is ina A onso Cab i a
Disse a ion epo p esen ed as pa ial equi emen o ob aining he mas e ’s deg ee in In o ma ion
Managemen , wi h a specializa ion in Knowledge Managemen and Business In elligence
Ad iso : P o esso Dou o Rui Alexand e Hen iques Gonçal es
Augus 2021
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ACKNOWLEDGEMENTS
A p esen e disse ação não es a ia concluída se não osse pelo apoio e incen i o da minha amília, do
meu namo ado e dos meus amigos. Ob igada po não me deixa em desis i . Ag adeço ambém ao meu
o ien ado pela ajuda e disponibilidade.
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ABSTRACT
The p esen disse a ion was de eloped wi h he objec i e o inding he mos ele an ac o s o
adop ion o mobile banking, a each se ice s age – s a ic, in e ac ion, and ansac ion s ages. A e
e iewing he li e a u e add essing mobile banking and i s adop ion, i was implemen ed a e ised
e sion o GAM model, composed by ele en independen a iables, g ouped in ou main cons uc s -
a i ude o use, abili y o use, assu ance o use, and adhe ence o use. Gi en he hypo hesis d awn, a
ques ionnai e was applied o Po uguese consume s and a sample o 208 esponden s was achie ed.
Thus, he da a ga he ed was analyzed unde pa h analysis, wi h Sma PLS 3, by de ining he h ee
models sepa a ed. I was ound ha pe cei ed unc ional bene i is he mos ele an p edic o a bo h
s a ic and ansac ion s ages, and a ailabili y o esou ces is he mos ele an p edic o a he
in e ac ion s age. In addi ion, i e demog aphic a iables we e conside ed as mode a o s o mobile
banking adop ion a each s age – age, gende , educa ion le el, income le el, and occupa ion. A e
e i ying he mode a o a iables in e ms o measu emen in a iance, only gende p esen ed good
esul s, meaning ha he o he mode a o s had o be dis ega ded. By applying mul ig oup analysis,
and gi en he indings o he mos impo an ac o s o he adop ion o mobile banking a he h ee
p esen ed s ages, only he ela ion be ween PA and MBA-S was signi ican ly mode a ed by gende .
KEYWORDS
Mobile banking adop ion; S a ic s age; In e ac ion s age; T ansac ion s age; Po uguese consume s;
Pa h analysis; GAM model; Mode a ion analysis
INDEX
1. In oduc ion .................................................................................................................. 1
1.1. Backg ound and p oblem iden i ica ion ................................................................ 1
1.2. S udy objec i es..................................................................................................... 1
2. S udy ele ance and impo ance .................................................................................. 3
3. Li e a u e e iew .......................................................................................................... 4
3.1. Mobile banking g ounded in FinTech .................................................................... 4
3.2. Mobile banking – de ini ion and ou look .............................................................. 7
3.3. Fac o s in luencing adop ion o mobile banking ................................................... 9
3.4. Mode a ion e ec ............................................................................................... 11
4. Theo e ical amewo k ............................................................................................... 12
4.1. Concep ual model desc ip ion ............................................................................ 12
4.2. Concep ual hypo heses ....................................................................................... 14
5. Me hodology .............................................................................................................. 18
5.1. Da a p epa a ion and sample cha ac e iza ion .................................................. 18
5.2. Measu emen models ......................................................................................... 19
5.3. S uc u al model .................................................................................................. 24
5.4. Mode a ion analysis ............................................................................................ 27
5.4.1. Gende as he mode a o a iable ............................................................... 27
5.4.2. Age as he mode a o a iable ..................................................................... 29
5.4.3. Income le el as he mode a o a iable ...................................................... 30
6. Resul s analysis and discussion .................................................................................. 31
7. Conclusions ................................................................................................................. 34
8. Limi a ions and ecommenda ions o u u e wo ks ................................................. 35
9. Re e ences .................................................................................................................. 36
10. Appendix............................................................................................................... 48
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LIST OF FIGURES
Figu e 1. Fin ech se ices by sec o . .......................................................................................... 5
Figu e 2. P oposed heo e ical model. ..................................................................................... 14
Figu e 3. Sample cha ac e iza ion. ........................................................................................... 19
Figu e 4. Empi ical model o mobile banking adop ion a he s a ic s age. ........................... 31
Figu e 5. Empi ical model o mobile banking adop ion a he in e ac ion s age. .................. 32
Figu e 6. Empi ical model o mobile banking adop ion a he ansac ion s age. ................. 32
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LIST OF TABLES
Table 1. Theo e ical amewo k. .............................................................................................. 17
Table 2. I em loadings. ............................................................................................................. 21
Table 3. In e nal consis ency eliabili y.................................................................................... 22
Table 4. Con e gen alidi y. .................................................................................................... 23
Table 5. Disc iminan alidi y. .................................................................................................. 23
Table 6. Va iance infla ion ac o . ............................................................................................ 26
Table 7. Signi icance o pa h coe icien s, using p- alue. ........................................................ 26
Table 8. R2 adjus ed. ................................................................................................................. 26
Table 9. Model i . .................................................................................................................... 26
Table 10. Mul ig oup analysis o MBA-I, wi h p- alue. .......................................................... 28
Table 11. Mul ig oup analysis o MBA-S, wi h p- alue. .......................................................... 29
Table 12. Mul ig oup analysis o MBA-T, wi h p- alue........................................................... 29
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LIST OF ABBREVIATIONS AND ACRONYMS
EBA Eu opean Banking Au ho i y
FSB Financial S abili y Boa d
BCBS Basel Commi ee on Banking Supe ision
BIS Bank o In e na ional Se lemen s
OECD O ganisa ion o Economic Co-ope a ion and De elopmen
SMEs Small and medium-sized en e p ises
AFS Al e na i e inancial se ices
ADB Asian De elopmen Bank
P2P Pee - o-pee
ECB Eu opean Cen al Bank
CBDC Cen al bank digi al cu ency
B2B Business o business
FX Fo eign exchange
TAM Technology accep ance model
UTAUT Uni ied Theo y o Accep ance and Use o Technology
GAM E-go e nmen Adop ion Model
MBA Mobile Banking Adop ion
PLS Pa ial Leas Squa es
PA Pe cei ed Awa eness
AOR A ailabili y o Resou ces
CSE Compu e -Sel E icacy
PATU Pe cei ed Abili y o Use
MLO Mul ilingual Op ion
PIQ Pe cei ed In o ma ion Quali y
PT Pe cei ed T us
PU Pe cei ed Unce ain y
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PS Pe cei ed Secu i y
PFB Pe cei ed Func ional Bene i
PI Pe cei ed Image
MBA-S Mobile Banking Adop ion a he S a ic S age
MBA-I Mobile Banking Adop ion a he In e ac ion S age
MBA-T Mobile Banking Adop ion a he T ansac ion S age
MGA Mul ig oup Analysis
MICOM Measu emen In a iance o Composi e Models
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compe i ion wi hin he banking sec o is welcomed, due o he bene i s i b ings o socie y, namely
inc eased e iciency, and new se ices, p o iding ha such compe i ion is duly egula ed o ensu e
inancial s abili y (Vi es, 2019). On ano he pe spec i e, Jooyong & Eunjung, 2016 call a en ion o one
speci ic esul achie ed, ha coope a ion and pa ial in eg a ion be ween inancial and non- inancial
companies, in he e ail paymen s’ ma ke , can ha e mo e ad an ages o o e all wel a e han
compe i ion o eplacemen .
3.2. MOBILE BANKING – DEFINITION AND OUTLOOK
To unde s and how FinTech, banks, and mobile banking glue oge he , i is pa amoun o de ine a bank
as well. Thus, “a bank is an ins i u ion whose cu en ope a ions consis in g an ing loans and ecei ing
deposi s om he public”, co esponding o he de ini ion ha guides egula o s and e e s o he main
ac i i ies o banks – deposi s and loans (F eixas & Roche , 2008). As obse ed p e iously in FinTech,
banks also p o ide a ious inancial se ices, g ouped in ou dis inc a eas - liquidi y and paymen
se ices ( e e ing o money changing and p o ision o paymen se ices), ans o ming asse s (which
can be seen in h ee di e en pe spec i es - con enience o denomina ion, quali y ans o ma ion,
and ma u i y ans o ma ion), managing isks (a ising om c edi isk, in e es a e isk, liquidi y isk,
and o -balance-shee ope a ions), and moni o ing and in o ma ion p ocessing (as a solu ion o he
p oblem o impe ec in o ma ion on bo owing) (F eixas & Roche , 2008).
Rega ding he concep o digi al banking, i is su ounded wi h misconcep ions and con usion, namely
because i has di e en names poin ing o he same concep , bo h in academic and p ac ical se ings
– “elec onic banking”, “ i ual banking”, “sel -se ice banking”, “Banking 2.0” and “elec onic und
ans e ” (Shaikh & Ka jaluo o, 2016). Acco dingly, he de ini ion adop ed is he ollowing – “digi al
banking is an any day, any ime, and anywhe e banking sys em consis ing o a a ie y o al e na i e
deli e y channels, p oduc s, and se ices de eloped and deployed by a banking company o
mic o inance ins i u e so ha consume s can access banking in o ma ion o conduc inancial and non-
inancial ansac ions using an elec onic de ice – commonly, bu no exclusi ely, an ATM, he In e ne ,
cell phone, sma phone, able , e c.“ (Shaikh & Ka jaluo o, 2016). I is in his con ex ha he concep
o mobile banking a ises, which is included in he deli e y channels o digi al banking - online, in e ne ,
and mobile banking (Shaikh & Ka jaluo o, 2016; Shaikh, Ka jaluo o, & Chinje, 2015). Fo he con ex o
he p esen disse a ion, he de ini ion o mobile banking o be adop ed is he one p oposed by Tam
& Oli ei a, 2016 - “M-banking is a se ice o p oduc o e ed by inancial ins i u ions ha makes use o
po able echnologies”.
E en hough nowadays’ inno a ions in banking a e unpa allel, digi al banking is no a new opic. Wi h
he in en ion o he in e ne and consequen ly o elec onic comme ce, digi al banking s a ed i s
g ow h pa h, and i was no clea which banking a eas o se ices would be he mos impac ed
(Aladwani, 2001), bu such ea ly e sions o digi al banking we e de eloped by adi ional banks,
subjec s o a egula o y amewo k (Anagnos opoulos, 2018). The cu en digi al pa adigm in banking
eme ged mainly a e he 2008’s inancial c isis, wi h he appea ance o new FinTech playe s in he
ma ke (Lee & Shin, 2018). I has been a gued ha he cu en dis up ion in banking can be analyzed
unde he dis up i e inno a ion model, which s a es ha , as incumben i ms aim o sa is y he high
end o he ma ke ( ansla ed in highe p o i abili y), he needs o low end and many mains eam
consume s a e neglec ed, p o iding an opening o new i ms, ha will e en ually mo e upma ke o
bene i om highe p o i abili y and challenge incumben companies (Anagnos opoulos, 2018). On he
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one hand, i is demons a ed ha FinTech companies can nega i ely and signi ican ly impac banks'
pe o mance, especially s a e-owned and ma u e banks (Phan, Na ayan, Rahman, & Hu aba a , 2020).
On he o he hand, e idence is ound ha FinTech inno a ions can inc ease banks’ e iciency and
imp o e he echnology used (Lee, Li, Yu, & Zhao, 2021), ein o cing he ad an ages o coope a ion
men ioned be o e.
Since 2008, he numbe o bank b anches in he Eu opean Union dec eased om 237,7 housand o
174 housand, in 2018 (s a is a, 2020) and he low p o i abili y o Eu opean banks has been a cause
o conce n o egula o s (Feng & Wang, 2018). Recen ly, banks s a ed o acknowledge he h ea
posed by FinTech, in a es ained manne hough (Bunea, Kogan, & S olin, 2016), being he di e en
impac s comp ehensi ely s udied in he li e a u e. Pa icula ly, Be g, Bu g, Gombo ić, & Pu i, 2019
demons a e ha in o ma ion om he digi al oo p in is equi alen o he con en o c edi bu eau
sco es, a guing ha i b ings a signi ican complemen a y ad an age o adi ional c edi sco es.
Addi ionally, Dobbie, Libe man, Pa a isini, & Pa hania, 2018 ound conside able bias in consume
lending agains immig an and olde applican s and show ha machine lea ning can help educe such
bias.
When conside ing wha migh dis inguish banks om FinTech compe i ion, one ac o poin ed in he
li e a u e is ela ionship banking (Thako , 2019). Fo he pu pose o he p esen disse a ion, he
de ini ion o ela ionship banking o be conside ed is “ he p o ision o inancial se ices by a inancial
in e media y ha in es s in ob aining cus ome -speci ic in o ma ion, o en p op ie a y in na u e, and
e alua es he p o i abili y o hese in es men s h ough mul iple in e ac ions wi h he same cus ome
o e ime and/o ac oss p oduc s”, as opposed o ansac ion banking, which is e e ed o be
associa ed wi h he capi al ma ke , based on a single ansac ion occu ing wi h he clien , a a gi en
poin in ime, and no ele an in o ma ion on he clien being collec ed (Boo , 2000; Thako , 2019).
FinTech companies a e also conside ed o be close o a ansac ion-o ien ed model (Thako , 2019;
Jakšič & Ma inč, 2019), and ela ionship lending is likely o play an impo an ole in ma ke
segmen a ion, since bo owe s wi h less a ailable in o ma ion, o digi al oo p in , will emain
dependen on ela ionship lending, bu bo owe s wi h a mo e ex ensi e digi al oo p in will lean
owa ds FinTech’s ansac ion lending solu ions (Boo , Ho mann, Lae en, & Ra no ski, 2021).
On he one hand, i is a gued ha ela ionship banking is s ill a compe i i e ad an age o banks,
conside ing ha geog aphic and cul u al p oximi y wi h consume s is impo an , and hence human
banke s canno be comple ely eplaced by a i icial in elligence ye , as echnology suppo is s ill jus
complemen a y (Jakšič & Ma inč, 2019). On he o he hand, applying algo i hms based on ha d da a
o make lending decisions, o ins ance, elimina es banke s’ possible disc e ion, a ac o documen ed
as no leading o a be e lending decision (Libe i & Pe e sen, 2018).
As a consequence, banks s a ed o adap o he new scena io, by adop ing new echnologies and
in es ing in specialized human capi al, so ha an answe can be p o ided o he new expec a ions
om cus ome s, namely ega ding anspa ency and use - iendliness o in e aces (Vi es, 2019).
No wi hs anding, i is impo an o acknowledge ha he e a e di e en ypes o consume s, wi h
a ious needs, beha io s, and pe cep ions. On he one hand, olde clien s, ha ha e been clien s o a
speci ic bank o yea s, highligh ing consume loyal y impo ance, and on he o he hand, a ech-sa y
young gene a ion, ueling he FinTech pa adigm and demanding om adi ional banks a quick
esponse, o main ain i s posi ioning in he ma ke (Anand & Man ala, 2018).
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3.3. FACTORS INFLUENCING ADOPTION OF MOBILE BANKING
The adop ion o mobile banking by consume s is widely s udied by academia, ega ding he en i e ime
spec um be ween elec onic banking inno a ions o he 90’s (Daniel, 1999; Ib ahim, Joseph, & Ibeh,
2006) and s a e-o - he-a ’s FinTech companies p o iding banking se ices (Singh, Sahni, & Ko id,
2020; Alkhowai e , 2020). When analyzing he ac o s a ec ing mobile banking adop ion, wo models
s and ou in he li e a u e – he echnology accep ance model (TAM), which came o ligh in 1989
(Da is, 1989), build om he heo y o easoned ac ion (TRA) (Fishbein & Ajzen, 1975) and hea ily
applied and imp o ed e e since (Sha ma, 2019), and he uni ied heo y o accep ance and use o
echnology (UTAUT), he model ha de i ed om he combina ion o di e en models o echnology
accep ance, including TAM, ou pe o ming he p e ious models bo h on a iance in beha io al
in en ion and echnology use (Dwi edi, Rana, Jeya aj, Clemen , & Williams, 2017; Alqah ani & Al‐Badi,
2017; Alalwan, Dwi edi, Rana, & Algha aba , 2018; Me hi, Hone, & Ta hini, 2019).
O iginally, TAM was based only on wo measu es - pe cei ed ease o use and pe cei ed use ulness -,
leading o nume ous ex ensions o e he yea s o imp o e i s explaining capabili y (Sha ma, 2019).
Di e en cons uc s we e conside ed, alongside TAM, o explain adop ion o mobile banking, namely
pe cei ed enjoymen , in o ma ion on online banking, secu i y and p i acy, and quali y o in e ne
connec ion (Pikka ainen, Pikka ainen, Ka jaluo o, & Pahnila, 2004), o he combina ion wi h compu e
e icacy, a i ude, pe cei ed isk, and in en ion (Fawzy & Esawai, 2017), o in eg a ing wi h us and
a i ude (Al-Ajam & No , 2013). Besides, i is also combined wi h a ious models, such as he sel -
de e mina ion heo y, based on in insic and ex insic mo i a ions (Sha ma, 2019), o wi h he
inno a ion di usion heo y, which explains a ious ypes o usage and con inual usage (Gio anis,
Binio is, & Polych onopoulos, 2012), o e en used as a benchma k o de elop new models, namely he
alue-based adop ion model (Kim, Chan, & Gup a, 2007).
UTAUT is g ounded on ou a iables - pe o mance expec ancy, e o expec ancy, social in luence,
and acili a ing condi ions – and ou mode a ing a iables – gende , age, expe ience, and
olun a iness o use (Im, Hong, & Kang, 2011). The model has been applied o he s udy o mobile
banking adop ion, no only in i s o iginal o m (Yu, 2012), bu also i s ex ended e sion, UTAUT2, adding
h ee main a iables - p ice alue, habi , and hedonic mo i a ion (Kwa eng, A iemo, & Appiah, 2018).
As seen p e iously in TAM, UTAUT is also combined wi h di e en de e minan s, o be e explain
mobile banking adop ion, speci ically elec onic se ice quali y (Rahi, Mansou , Alghizzawi, & Alnase ,
2019), pe cei ed isk (Oli ei a & Popo ič, 2014), websi e design and usage beha io (Qeisi & Al-
Abdallah, 2014), o mass media and us (Gha aibeh & A shad, 2018). Acco dingly, i is used alongside
o he models as well, such as he ask echnology i model, which implies ha adop ion occu s i he
echnology in ques ion execu es e icien ly (Zhou, Lu, & Wang, 2010), ask echnology i model and
ini ial us model, s a ing ha con enience, lexibili y, and pe cei ed bene i s a e impo an
cons uc s o ini ial us o ma ion (Oli ei a, Fa ia, Thomas, & Popo ič, 2014), and di usion o
inno a ion model, based on ou elemen s o di usion - inno a ion, communica ion channel, ime and
social sys em (Rahi & Ghani, 2018). In he same logic, academic pape s applied bo h TAM and UTAUT
o be e unde s and mobile banking adop ion, o example Boonsi i omachai & Pi chayadejanan ,
2017, wi h he main objec i e o de e mining he media ing e ec o hedonic mo i a ion on
independen mobile banking adop ion.
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The applica ion o such models has he objec i e o e i ying which ac o s a e he mos in luen ial on
mobile banking adop ion. Va ious esul s a e ound in he li e a u e, ega ding he main p edic o s, o
ins ance, us and au onomous mo i a ion (Sha ma, 2019), pe o mance expec ancy, e o
expec ancy, hedonic mo i a ion, p ice alue and pe cei ed isk (Alalwan, Dwi edi, Rana, & Algha aba ,
2018), habi , pe cei ed secu i y, pe cei ed p i acy and us (Me hi, Hone, & Ta hini, 2019), pe cei ed
use ulness and in o ma ion on online banking (Pikka ainen, Pikka ainen, Ka jaluo o, & Pahnila, 2004),
websi e cha ac e is ics, compu e e icacy and pe cei ed isk (Fawzy & Esawai, 2017), pe cei ed
ela i e ad an ages, pe cei ed ease o use and us (Al-Ajam & No , 2013), pe cei ed compa ibili y,
pe cei ed use ulness, pe cei ed ease o use and pe cei ed secu i y and p i acy isk (Gio anis, Binio is,
& Polych onopoulos, 2012), pe cei ed alue (Kim, Chan, & Gup a, 2007), social in luence, pe cei ed
inancial cos , pe o mance expec ancy and pe cei ed c edibili y (Yu, 2012), habi , p ice alue and us
(Kwa eng, A iemo, & Appiah, 2018), inno a i eness and pe cei ed echnology secu i y (Rahi, Mansou ,
Alghizzawi, & Alnase , 2019), pe cei ed isk, pe o mance expec ancy, e o expec ancy and social
in luence (Oli ei a & Popo ič, 2014) o elec onic wo d o mou h, media ed by ini ial us (Shanka ,
Jeba ajaki hy, & Ashaduzzaman, 2020).
Fo FinTech adop ion, in luen ial ac o s a e also poin ed ou in he li e a u e, speci ically us ,
pe cei ed use ulness, inno a i eness, go e nmen suppo , o b and image, acco ding o a s udy based
on an ex ended TAM (Hu, Ding, Li, Chen, & Yang, 2019). In addi ion, he legal isk was ound o be an
impo an p edic o , wi h a nega i e e ec , such as con enience, wi h a posi i e e ec , bu bo h
mode a ed by being an ea ly o la e adop e (Ryu, 2017). No wi hs anding, a simila s udy in B azil, did
no ind he legal isk ac o as being signi ican , con a ily o economic bene i s o ea ly adop e s and
seamless ansac ions o la e adop e s (Masca enhas, Pe pé uo, Ba o e, & Pe ides, 2020).
On an unique pe spec i e, Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018 p opose ha in en ion
o adop mobile banking di e s acco ding o se ice s ages, namely s a ic, which consis s o e i ying
accoun o in es men in o ma ion, in e ac ion, ega ding any o m o wo-sided communica ion, and
ansac ional s age, e e ing o sensi i e inancial ope a ions, o ins ance, money ans e be ween
accoun s o paymen s o bills. The e o e, i is applied he E-Go e nmen Adop ion Model (GAM),
b ough o ligh wi h he objec i e o s udying he ac o s in luencing e-Go e nmen , go e nmen
se ices p esen ed h ough communica ion and in o ma ion echnologies, a di e en s ages o
se ice ma u i y (Sha ee , Kuma , Kuma , & Dwi edi, 2011), a scena io conside ed by Sha ee ,
Baabdullah, Du a, Kuma , & Dwi edi, 2018 as ha ing se e al simila i ies (such as echnology o se ice
deli e y channel) wi h mobile banking adop ion. The indings e eal ha pe cei ed unc ional bene i
and pe cei ed abili y o use a e he d i e s o in en ion o adop ac oss he h ee s ages, us s ands
ou in he s a ic s age and secu i y a he ansac ion one.
The di ision o adop ion in se ice s ages is mos ly in luenced by such di ision in e-go e nmen
se ices, p esen ed in di e en s udies (Sha ee M. A., Kuma , Kuma , & Dwi edi, 2011; Ro issa,
Demissie, & Pa do, 2011; Ka okola & Kowalski, 2012; Jayash ee & Ma handan, 2010; Lee J. , 2010),
and he applica ion o such concep s in o he ields is imidly explo ed in he li e a u e. E en hough
he ac o s in luencing mobile banking adop ion as a whole a e well s udied in he li e a u e, so as he
di e ences in adop ion o di e en banking channels (Na a ajan, Balasub amanian, &
Manicka asagam, 2010; Mish a & Singh, 2015; Hoehle, Sco na acca, & Hu , 2012; Baabdullah, e al.,
2019), he adop ion in di e en s ages was app oached only by Sha ee , Baabdullah, Du a, Kuma , &
Dwi edi, 2018, wi hin he mobile banking con ex , and was no es ed ye in a de eloped coun y.
11
3.4. MODERATION EFFECT
Besides he di ec e ec o a gi en a iable on mobile banking, he mode a ion e ec is widely
discussed in he li e a u e (Wa same & I e i, 2018; Goula e & Zilbe , 2018; Ryu, 2017; Alshu ideh,
Ku di, Masa’deh, & Salloum, 2021; Chawla & Joshi, 2018; Kwa eng, A iemo, & Appiah, 2018). A
mode a o a iable e e s o a a iable ha in luences he ela ion be ween wo o he s, al e ing he
magni ude o he e ec one causes in he o he (Aguinis, Edwa ds, & B adley, 2017; Memon, e al.,
2019).
Wi hin he mobile banking con ex , i is called a en ion o he ole o gende and eligious belie s as
a mode a o a iable, acco ding o a s udy de eloped in Kenya (Wa same & I e i, 2018), and o he one
in Uni ed A ab Emi a es, highligh ing he impo ance o gende as a mode a o on elec onic paymen
echnology accep ance (Alshu ideh, Ku di, Masa’deh, & Salloum, 2021). Mo eo e , i was ound in
Ko ea ha he use ype, ea ly o la e adop e , mode a es he adop ion o FinTech (Ryu, 2017). A mo e
comp ehensi e s udy, de eloped in India, ound ha gende , age, quali ica ion, expe ience,
occupa ion, income, and ma i al s a us we e signi ican mode a ing a iables, whe eas educa ional
backg ound was no (Chawla & Joshi, 2018). In he same logic, a s udy on Ghana consume s ound ha
gende , age, educa ional le el, and use expe ience a e ele an mode a o s (Kwa eng, A iemo, &
Appiah, 2018). On he o he hand, a s udy applied in B azil e ealed ha cul u al ac o s, ep esen ed
by Ho s ede cul u al dimensions, a e no ele an mode a o s o mobile banking (Goula e & Zilbe ,
2018).
12
4. THEORETICAL FRAMEWORK
Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018 add essed a clea gap in he li e a u e o mobile
banking adop ion, by p oposing ha he ac o s in luencing adop ion di e be ween adop ion s ages.
Such esul was achie ed by applying he GAM model, de eloped by Sha ee , Kuma , Kuma , &
Dwi edi, 2011, who comp ehensi ely explo e di e en ac o s a ec ing e-Go e nmen se ices
adop ion a di e en s ages. The model was applied o mobile banking because ega ding “ echnology,
ope a ion, sel -se ice echnology, i ual medium, secu i y equi emen s, and se ice deli e y
channel”, he adop ion beha io is simila o e-Go e nmen adop ion. Acco dingly, he gene al model
is based on i e main cons uc s – a i ude o use, abili y o use, assu ance o use, adhe ence o use
and adap abili y o use. Each cons uc conside s one o mo e ac o s, in a o al o ou een ac o s
a ec ing, di ec ly o indi ec ly, e-Go e nmen adop ion. Conside ing he di e en s ages o adop ion,
wo models we e de i ed – GAM (S), o he s a ic s age, and GAM (I), o he in e ac ion s age. In a
la e s udy, he model o he ansac ion s age, GAM (T), was de eloped, g ounded on abili y o use
and assu ance o use (Sha ee , Kuma , & Dwi edi, 2014).
F om he inal GAM, Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018, a e ised e sion o he
gene al model was applied o he con ex o mobile banking, wi h ou main cons uc s – a i ude o
use, abili y o use, assu ance o use, and adhe ence o use, each one composed by di e en
independen a iables ha in luence mobile banking adop ion. In he s udy de eloped, Sha ee M. A.,
Kuma , Kuma , & Dwi edi, 2011 conside ed he adop ion o e-Go a con inuous p ocess, which begins
wi h “awa eness o he sys em, belie s o he sys em bene i s, a i ude owa d using i , in en ion o
use, ac ual use, sa is ac ion, and ecu ing use”, jus i ying he di ision in i e cons uc s (howe e ,
Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018 ound ha ou cons uc s would i be e wi hin
he mobile banking con ex ).
4.1. CONCEPTUAL MODEL DESCRIPTION
The i s cons uc is composed by h ee a iables – pe cei ed awa eness (PA), a ailabili y o esou ces
(AOR), and compu e -sel e icacy (CSE). Pe cei ed awa eness is desc ibed in he li e a u e as he
deg ee consume s deem o be su icien o lea n and unde s and he cha ac e is ics, bene i s and
challenges o mobile banking, and being able o ope a e wi h i (Sha ee , Baabdullah, Du a, Kuma , &
Dwi edi, 2018; Fonchamnyo, 2012; San ini, Ladei a, Sampaio, Pe in, & Dolci, 2019). A ailabili y o
esou ces is e e ed as he echnological esou ces and in as uc u e necessa y o use mobile
banking, in e ms o eedom o access, speed, and cos (Sha ee , Baabdullah, Du a, Kuma , & Dwi edi,
2018; Ja uwachi a hanakul & Fink, 2005). The las a iable, compu e -sel e icacy, is desc ibed as he
pe cei ed capaci y o use, in e ac , and pe o m ansac ions on a mobile banking se ice, wi h no o
minimal assis ance, de ining he use ulness o he la es echnologies (Sha ee , Baabdullah, Du a,
Kuma , & Dwi edi, 2018; Nas i & Za ai, 2014; Zainab, Bha i, & Alshagawi, 2017; Sha ma &
Go indalu i, 2014). These p edic ed a iables a e ca ego ized as he “a i ude o use”, because Sha ee
M. A., Kuma , Kuma , & Dwi edi, 2011 conside ed ha pe cei ed awa eness is he i s s imulus in
inducing a i ude, a ailabili y o esou ces de elops a belie o use, leading o an a i ude o use, and
compu e sel -e icacy ega ds he pe cep ion o skills one has, ega dless o he ac ual skills.
Fo he second cons uc , abili y o use, wo p edic ed a iables a e laid ou – pe cei ed abili y o use
(PATU) and mul ilingual op ion (MLO). The i s a iable implies he echnological, o ganiza ional, and
13
psychological pe spec i es, e e ing o he judgemen o one’s compe ence in using mobile banking
sys ems (Sha ee M. A., Kuma , Kuma , & Dwi edi, 2011; Sha ee , Baabdullah, Du a, Kuma , &
Dwi edi, 2018). In addi ion, mul ilingual op ion ega ds he a ailabili y o di e en p ima y languages
in digi al baking sys ems, enhancing he abili y o use wi h be e se ice quali y (Sha ee M. A., Kuma ,
Kuma , & Dwi edi, 2011; Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018).
The hi d cons uc , assu ance o use, is g ounded on pe cei ed in o ma ion quali y (PIQ) and
pe cei ed us (PT), being p oposed ha he la e is in luenced by pe cei ed unce ain y (PU) and
pe cei ed secu i y (PS). Pe cei ed in o ma ion quali y ega ds he comple eness, accu acy, o ma , and
cu ency p o ided by he mobile banking sys em, as i is unde s ood by he cos ume (Sha ee ,
Baabdullah, Du a, Kuma , & Dwi edi, 2018; Jayasi i, Gunawa dana, & Dha madasa, 2016; Milan,
Suélen, Toni, & Ebe le, 2015). Conce ning he a iable pe cei ed us , i is de ined as he consume
judgemen o con idence “ o eliabili y, c edibili y, sa e y, and in eg i y o mobile banking sys em om
he echnical, o ganiza ional, social, and poli ical s andpoin s and also om he e ec i e, e icien ,
p omp , and sympa he ic cus ome se ice esponse”, ega dless o he possibili y o con ol he
banks’ ac ions (Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018; Yousa zai, Pallis e , & Foxall,
2003; Mokh a , Ka an, & Hidaya -u -Rehman, 2017). Bo h cons uc s e lec he con idence in using
mobile banking, being cha ac e ized as he assu ance o use (Sha ee M. A., Kuma , Kuma , & Dwi edi,
2011).
Gi en he complexi y and he nume ous s udies on pe cei ed us , Sha ee M. A., Kuma , Kuma , &
Dwi edi, 2011 p oposed pe cei ed unce ain y and pe cei ed secu i y would in luence pe cei ed us .
The i s a iable is de ined as he le el o isk unde s ood by consume s in each ansac ion, jus i ied
by unknown si ua ions impac ing he mobile banking sys em and e e ing o di e en dimensions,
namely inancial o ope a ional (Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018; Bazgosha, Eizi,
Nawase , & Pa hizga , 2012). Rega ding pe cei ed secu i y, i is desc ibed as he consume s’
judgemen on whe he i is sa e o sha e pe sonal in o ma ion, wi hin he mobile banking con ex ,
bo h on he in e ac ion and ansac ion s ages, wi h he belie ha he p o ide will ake he
app op ia e ac ions o p o ec such in o ma ion om secu i y b eaches (Sha ee , Baabdullah, Du a,
Kuma , & Dwi edi, 2018; Balapou , Nikkhah, & Sabhe wal, 2020).
Adhe ence o use, he las cons uc , includes wo a iables – pe cei ed unc ional bene i (PFB) and
pe cei ed image (PI). Pe cei ed unc ional bene i is ound o ha e economic, o ganiza ional,
ma ke ing, beha io al, and social dimensions, being he le el o which consume s acknowledge bo h
he absolu e and ela i e p ac ical ad an ages o using mobile banking, a he expense o using
adi ional physical b anches (Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018; Sha ee M. A.,
Kuma , Kuma , & Dwi edi, 2011). The second p edic ed a iable is desc ibed as he ex en o which
he use o mobile banking is belie ed, in e ms o beha io and cul u e, o enhance social s a us
(Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018; Abdulkadi , Galoji, & Razak, 2013; Mohammadi,
2015). Bo h a iables a e classi ied as adhe ence o use because Sha ee M. A., Kuma , Kuma , &
Dwi edi, 2011 conside ed hose as con eying easons o adop .
The gene al model de ined is depic ed bellow:
14
Figu e 2. P oposed heo e ical model.
Sou ce: Adap ed om Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018.
In o de o model he adop ion in h ee s ages, h ee dependen a iables we e de ined – MBA,
s anding o mobile banking adop ion, (S), o he s a ic s age, MBA (I), ega ding he in e ac ion s age,
and MBA (T), o he ansac ion s age. Each a iable is cha ac e ized by he ac i i ies i accoun s o ,
e lec ing a decision o accep and use mobile banking sys ems o such ac i i ies, as one equi es, wi h
he posi i e pe cep ion o bene i ing om a compe i i e ad an age. MBA (S) ega ds checking accoun
balance, accoun s a us, and accoun ansac ion, sea ching accoun and in es men in o ma ion and
in e es a es, o downloading o ms o accoun ela ed unc ions. In MBA (I), i is conside ed
in e ac ing and seeking cus ome se ices o pose ques ions, o di e en inancial easons. Fo MBA
(T), he ac i i ies conside ed a e ansac ions o ans e money om one accoun o ano he and/o
pay bills.
In addi ion o he concep ual model explained abo e, i will be analyzed he impac o i e demog aphic
mode a o a iables – gende , age, educa ion le el, occupa ion, and income le el – in all he cons uc s
de ined, in o de o e i y i he e a e di e en pa e ns o adop ion beha io wi hin dis inc g oups.
4.2. CONCEPTUAL HYPOTHESES
Fo pe cei ed awa eness, Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018 hypo hesized ha he
a iable had a posi i e ela ion wi h adop ion o mobile banking, inding ha i was only ele an a
he s a ic s age. O he au ho s (Daud, Kassim, Said, & Noo , 2011; Fonchamnyo, 2012) also ound a
15
signi ican , posi i e ela ion wi h adop ion o mobile banking. In his line, he ollowing hypo hesis is
d awn:
H1. Pe cei ed awa eness (PA) has a posi i e ela ion wi h mobile banking adop ion.
A ailabili y o esou ces and compu e -sel e icacy we e p oposed as ha ing a posi i e ela ion wi h
mobile banking as well, bu he da a ga he ed e ealed ha nei he one was ele an in any s age.
Rega ding compu e -sel e icacy, s udies (Ndubisi, 2006; Gu i ing, Chunwen, & Ndu, 2007) show,
howe e , ha i can be an impo an ac o o adop ion o mobile banking. Fo a ailabili y o
esou ces, no s udies we e ound e ealing i s impo ance, bu o he sake o applying o new da a
he model pu o wa d by Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018. Acco dingly, wo
hypo heses a e laid ou :
H2. A ailabili y o esou ces (AOR) has a posi i e ela ion wi h mobile banking adop ion.
H3. Compu e -sel e icacy (CSE) has a posi i e ela ion wi h mobile banking adop ion.
In he second cons uc , o pe cei ed abili y o use, a posi i e ela ion wi h adop ion o mobile banking
was p oposed, and he da a e ealed i o be ele an a he h ee phases. Di e en s udies (Sha ee
M. A., Kuma , Kuma , & Dwi edi, 2011; Ak am & Malik, 2012; Kenny & Connolly, 2017) also ind i o
be a ele an a iable o he adop ion o new echnologies. Thus, i is p esen ed he ollowing
hypo hesis:
H4. Pe cei ed abili y o use (PATU) has a posi i e ela ion wi h mobile banking adop ion.
On he o he hand, mul ilingual op ion, hypo hesized as ha ing a posi i e ela ion wi h mobile banking
adop ion, was ound as no ele an in any s age. E en hough he au ho s did no ob ain a ele an
esul , and no o he s udies we e ound a es ing i s impo ance in mobile banking adop ion, o he
sake o he model p esen ed, i is p oposed he ollowing hypo hesis:
H5. Mul ilingual op ion (MLO) has a posi i e ela ion wi h mobile banking adop ion.
Conside ing he hi d cons uc , in wha conce ns pe cei ed in o ma ion quali y, also hypo hesized as
ha ing a posi i e ela ion wi h mobile banking adop ion, i was ound o be ele an in bo h s a ic and
in e ac ion s ages. Mo eo e , Ezeh & Nkamnebe, 2018 co obo a ed his inding and Ma hew, Jose, G,
& Chacko, 2020 disco e ed ha , in he ligh o e-se ice eco e y sa is ac ion in banking, his a iable
was he mos signi ican p edic o . Acco dingly, he ollowing hypo heses is p oposed:
H6. Pe cei ed in o ma ion quali y (PIQ) has a posi i e ela ion wi h mobile banking adop ion.
Fo pe cei ed us , hypo hesized as ha ing a posi i e ela ion wi h mobile banking adop ion, i was
only ound o be ele an in he s a ic phase. Ba kho da i, Nou ollah, Mashayekhi, Mashayekhi, &
Ahanga , 2016 s udied pe cei ed us impac on e-paymen se ices adop ion, and ealized ha i
was an impo an p edic o as well. So did Mokh a , Ka an, & Hidaya -u -Rehman, 2017, when
16
disco e ing ha pe cei ed us impac ed posi i ely mobile banking adop ion. Hence, he ollowing
hypo heses is p oposed:
H7. Pe cei ed us (PT) has a posi i e ela ion wi h mobile banking adop ion.
Rega ding he wo a iables in luencing pe cei ed us , pe cei ed unce ain y and pe cei ed secu i y,
hypo hesized as ha ing a nega i e and posi i e ela ion wi h us o mobile banking, espec i ely, bo h
we e ound o be ele an a all s ages. Fo pe cei ed unce ain y, Liu, Huang, & Zhu, 2008 alida ed
ha i p esen s a nega i e ela ion wi h us as well, and Yousa zai, Pallis e , & Foxall, 2009
co obo a ed he posi i e ela ion o pe cei ed secu i y wi h us . Acco dingly, he nex hypo hesis
a e d awn:
H8. Pe cei ed unce ain y (PU) has a nega i e ela ion wi h us o mobile banking.
H9. Pe cei ed secu i y (PS) has a posi i e ela ion wi h us o mobile banking.
Las ly, in he ou h cons uc , pe cei ed unc ional bene i and pe cei ed image we e bo h
hypo hesized as ha ing a posi i e ela ion wi h mobile banking adop ion, bu only pe cei ed unc ional
bene i was ound o be ele an in he h ee s ages, con a ily o pe cei ed image, ha was no
ele an a all. Sha ee M. A., Kuma , Kuma , & Dwi edi, 2011 ound pe cei ed unc ional bene i o be
ele an a he s a ic s age o e-Go adop ion and pe cei ed image a he in e ac ion s age. Fo
pe cei ed image, only one s udy (Bashi & Madha aiah, 2015) ound i o be e ele an o adop ion,
bu o he sake o he model p esen ed, i will be conside ed, and he ollowing hypo heses a e
p oposed:
H10. Pe cei ed unc ional bene i (PFB) has a posi i e ela ion wi h mobile banking adop ion.
H11. Pe cei ed image (PI) has a posi i e ela ion wi h mobile banking adop ion.
To close he opic o concep ual hypo hesis, Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018 ound
ha pe cei ed secu i y was a s ong p edic o in he ansac ion phase, e en hough no ela ion was
hypo hesized be ween pe cei ed secu i y and mobile banking adop ion. The e e ed inding is
co obo a ed by Baabdullah, Alalwan, Rana, Pa il, & Dwi edi, 2019; Ong & Lin, 2015 and Salimon,
Yuso , & Mokh a , 2016. Acco dingly, he las hypo heses is de ined as ollows:
H12. Pe cei ed secu i y (PS) has a posi i e ela ion wi h mobile banking adop ion.
The ollowing able summa izes he hypo heses de ined abo e and he espec i e li e a u e:
23
Table 4. Con e gen alidi y.
Sou ce: Sma PLS 3.
Las ly, disc iminan alidi y ega ds he deg ee o which a gi en cons uc is empi ically di e en om
o he cons uc s in he inne model (Hai , Rishe , Sa s ed , & Ringle, 2018), and i is assessed h ough
he Fo nell-La cke c i e ion, s a ing ha he squa e oo o AVE should be highe han he co ela ion
wi h o he cons uc s (Hamid, Sami, & Sidek, 2017), and he he e o ai -mono ai (HTMT) a io o
co ela ions, appoin ed as ha ing supe io pe o mance han he Fo nell-La cke c i e ion, de ining a
h eshold o less han 0.90 (Hensele , Ringle, & Sa s ed , 2014). A e conduc ing his analysis, PA and
CSE, PATU and CSE, PATU and PA, PFB and PA, PFB and PATU, PFB and MBA-T (only in MBA-T) all unde
he he e o ai -mono ai a io (Table 5).
Table 5. Disc iminan alidi y.
AOR CSE MBA_S MLO PA PATU PFB PI PIQ PS PT
CSE 0.6124
MBA_S 0.6031 0.7659
MLO 0.3697 0.4143 0.4473
PA 0.6826 0.9321 0.8433 0.5066
PATU 0.6982 0.9385 0.7607 0.4111 0.9236
PFB 0.6247 0.8486 0.8595 0.4818 0.9100 0.9150
PI 0.1964 0.0973 0.0953 0.3291 0.1089 0.1383
0.0939
PIQ 0.6278 0.7618 0.7083 0.2698 0.7618 0.8443
0.7703
0.0603
PS 0.6185 0.5510 0.5523 0.3203 0.6845 0.5877
0.6108
0.1363 0.6388
PT 0.6128 0.5803 0.6268 0.2797 0.7084 0.6622
0.6912
0.0710 0.7753 0.8235
PU 0.5827 0.7831 0.7038 0.4291 0.7848 0.8179
0.8665
0.1436 0.6469 0.4819 0.4833
MBA_S
24
Sou ce: Sma PLS 3.
Conside ing he p oblems ound wi h he measu emen models, and conside ing all measu emen s
seen so a , i s pe o mance is signi ican ly imp o ed when d opping he i ems wi h he lowes loadings
- PFB3, MLO1 (only in MBA-S and MBA-T), and MLO2 (only in MBA-S and MBA-T) – and he PI cons uc
om he MBA-T, as all he loadings we e below he h eshold. By doing so, in e nal consis ency
eliabili y is imp o ed, as only PI (in MBA-I) pe o m unde he C onbach's al a h eshold, so is AVE,
ha has no a iable below he h eshold, and disc iminan alidi y. Since he model is imp o ed wi h
hese mino al e a ions, no o he changes a e made in he measu emen model, o a oid losing
in o ma ion (Bido & Sil a, 2019; Li le, Lindenbe ge , & Nessel oade, 1999).
5.3. STRUCTURAL MODEL
Concluded he assessmen o he measu emen model, he s uc u al model, based on he pa h
ela ionships be ween he independen la en a iables and he dependen la en a iables, is
e alua ed. Well-es ablished assessmen c i e ia encompass he coe icien o de e mina ion (R2), and
he s a is ical signi icance o he pa h coe icien s (Hai , Rishe , Sa s ed , & Ringle, 2018). Howe e , i
is ele an o i s e i y he collinea i y be ween independen and dependen a iables, so ha he
eg ession ou come is no biased, by using he a iance infla ion ac o (VIF), which should be lowe
AOR CSE MBA_I MLO PA PATU PFB PI PIQ PS PT
CSE 0.6124
MBA_I 0.6044 0.6705
MLO 0.3697 0.4143 0.3704
PA 0.6826 0.9321 0.6980 0.5066
PATU 0.6982 0.9385 0.7137 0.4111 0.9236
PFB 0.6247 0.8486 0.6624 0.4818 0.9100 0.9150
PI 0.1964 0.0973 0.1041 0.3291 0.1089 0.1383
0.0939
PIQ 0.6278 0.7618 0.6758 0.2698 0.7618 0.8443
0.7703
0.0603
PS 0.6185 0.5510 0.4227 0.3203 0.6845 0.5877
0.6108
0.1363 0.6388
PT 0.6128 0.5803 0.5285 0.2797 0.7084 0.6622
0.6912
0.0710 0.7753 0.8235
PU 0.5827 0.7831 0.5255 0.4291 0.7848 0.8179
0.8665
0.1436 0.6469 0.4819 0.4833
AOR CSE MBA_T MLO PA PATU PFB PI PIQ PS PT
CSE 0.6124
MBA_T 0.5948 0.7552
MLO 0.3697 0.4143 0.3860
PA 0.6826 0.9321 0.7935 0.5066
PATU 0.6982 0.9385 0.7919 0.4111 0.9236
PFB 0.6247 0.8486 0.9125 0.4818 0.9100 0.9150
PI 0.1964 0.0973 0.0694 0.3291 0.1089 0.1383
0.0939
PIQ 0.6278 0.7618 0.6493 0.2698 0.7618 0.8443
0.7703
0.0603
PS 0.6185 0.5510 0.5751 0.3203 0.6845 0.5877
0.6108
0.1363 0.6388
PT 0.6128 0.5803 0.6252 0.2797 0.7084 0.6622
0.6912
0.0710 0.7753 0.8235
PU 0.5827 0.7831 0.7585 0.4291 0.7848 0.8179
0.8665
0.1436 0.6469 0.4819 0.4833
MBA_I
MBA_T
25
han 5 (Hai , Hul , Ringle, & Sa s ed , 2017). I is impo an o no e ha , in o de o e i y he
signi icance o pa h coe icien s, i was applied he boo s apping algo i hm, since a dis ibu ion mus
be de i ed om he da a, so ha he signi icance can be es ed. This is because PLS-SEM makes no
assump ions o he dis ibu ion o he da a (Hai , Hul , Ringle, & Sa s ed , 2017).
In wha conce ns R2 and aking in o conside a ion he con ex o he p esen disse a ion, he e e ence
alues a e 0.75, o subs an ial, 0.50 o mode a e, and 0.25 o weak (Hensele , Ringle, & Sinko ics,
2009). To a oid a oid bias agains complex models, R2 adjus ed is used (Hai , Hul , Ringle, & Sa s ed ,
2017).
Model i is also conside ed o assess he model, e en hough i is a new assessmen me hod, in he
con ex o pa ial leas squa es pa h modeling, and acco dingly, mus be used wi h cau ion (Beni ez,
Hensele , Cas illo, & Schube h, 2020; Hai , Hul , Ringle, & Sa s ed , 2017; Hai , Sa s ed , & Ringle,
2019). In his line, i is used he s anda dized oo mean squa e esidual (SRMR), p o iding an insigh
on he di e ence be ween he empi ical da a and he ac ual model, and should p esen alues lowe
han 0.1 (Wes on, 2006). No med Fi Index (NFI) is also e alua ed, e e ing o an inc emen al i
measu e based on compa ing he Chi-squa e alue o he p esen model wi h a signi ican benchma k,
and i ep esen s accep able model i i abo e 0.9 (Ben le & Bone , 1980).
In he s a ic phase (MBA-S), PATU and CSE a e he only cons uc s wi h a VIF alue abo e he h eshold.
Mo eo e , he cons uc s signi ican o he dependen a iable a e CSE, PA, PFB, PS and PU (bo h o
PT). Rega ding R2 adjus ed, he alues o MBA-S and PT a e 0,686 and 0,536, espec i ely, being
mode a e in bo h cases, e en hough he o me is close o a subs an ial le el. In opic o model i ,
he alue ob ained o SRMR is 0,084, being wi hin he h eshold conside ed, and o NFI he alue is
0,715.
Fo he in e ac ion s age (MBA-I), ega ding collinea i y be ween he cons uc s and he dependen
a iable, PATU and CSE p esen alues abo e he h eshold. In signi icance analysis, AOR and PS
e ealed o be signi ican o mobile banking adop ion in he in e ac ion s age, and PS and PU a e
signi ican o PT, in he same s age. Analyzing he R2 adjus ed, he alues o MBA-I and PT a e 0,524
and 0,529, espec i ely, which leads o a classi ica ion o mode a e in bo h cases. Fo model i , and
aking in o conside a ion ha i is s ill in ea ly s ages, as explained be o e, he SRMR p esen s a alue
o 0,086, being wi hin he h eshold de ined, and NFI pe o ms below he h eshold, wi h 0,711.
Las ly, in he ansac ion s age (MBA-T), PATU and CSE a e he only cons uc s wi h a VIF alue abo e
he h eshold. Conside ing he signi icance o pa h coe icien s, PFB is signi ican in MBA-T, and PS and
PU a e signi ican o PT. R2 adjus ed p esen s alues o 0,727 and 0,530 o MBA-T and PT, espec i ely,
wi h he o me close o an e alua ion o subs an ial and he la e close o mode a e. In wha
ega ds model i , he SRMR p esen s a alue o 0,086, being wi hin he h eshold de ined, and he NFI
has a alue o 0,719, alling below he h eshold.
26
Table 6. Va iance infla ion ac o .
Sou ce: Sma PLS 3.
Table 7. Signi icance o pa h coe icien s, using p- alue.
Sou ce: Sma PLS 3.
Table 8. R2 adjus ed.
Sou ce: Sma PLS 3.
Table 9. Model i .
Sou ce: Sma PLS 3.
MBA_S 0.6690
PT MBA_S 0.5205
MBA_I 0.4940
PT MBA_I 0.5228
MBA_T 0.7119
PT MBA_T 0.5186
MBA_S MBA_I MBA_T
SRMR 0.0838 0.0858 0.0861
NFI 0.7153 0.7112 0.7194
MBA_S PT MBA_S MBA_I PT MBA_I MBA_T PT MBA_T
AOR 1.7301 1.7243 1.7209
CSE 5.3617 5.3860 5.3853
MLO 1.5834 1.4897 1.4970
PA 3.1841 3.0849 3.2138
PATU 9.1688 9.0201 8.8721
PFB 3.7211 3.6683 3.4723
PI 1.2122 1.1230 N/A N/A
PIQ 3.6550 3.6472 3.6530
PS 2.2062 1.2510 2.1154 1.2461 2.1907 1.2536
PT 3.0162 3.0570 2.9394
PU 1.2510 1.2461 1.2536
27
5.4. MODERATION ANALYSIS
As explained be o e, a c ucial pa o he p esen disse a ion is mode a ion analysis, as i is
hypo hesized ha i e mode a ing a iables – gende , age, educa ion le el, occupa ion, and income
le el – migh in luence he adop ion o mobile banking a he di e en s ages. Taking in o
conside a ion ha i is in ended o e i y he mode a ion e ec o he e e ed a iables on all he
cons uc s de ined, he mos e icien me hod is o apply mul ig oup analysis (MGA) (Hai , Hul , Ringle,
& Sa s ed , 2017; Sa s ed , Hensele , & Ringle, 2011). In MGA, di e en g oups a e de ined, based on
one mode a o a iable, and new models a e es ima ed o each g oup, so ha i can be e i ied i he
di e ences be ween g oups a e signi ican (Vinzi, Chin, Hensele , & Wang, 2010; Cheah, Thu asamy,
Memon, Chuah, & Ting, 2020; Hai , Hul , Ringle, & Sa s ed , 2017).
By analyzing he da a ga he ed on he mode a o a iables, only gende , age, and income le el p esen
enough samples and equal sample sizes on di e en g oups, so ha s a is ical powe is achie ed
(Cheah, Thu asamy, Memon, Chuah, & Ting, 2020). Due o his limi a ion in he da a ga he ed, i is no
possible o e i y he hypo heses d awn o educa ion le el and occupa ion, as he s a is ical powe
canno be gua an eed (Hai , Hul , Ringle, & Sa s ed , 2017; Aguinis, Edwa ds, & B adley, 2017).
5.4.1. Gende as he mode a o a iable
When analyzing gende as he mode a o a iable, wo g oups a e clea ly es ablished – male
esponden s, wi h 80 samples, and emale esponden s, wi h 127 esponden s. Using G*Powe (Faul,
E d elde , Lang, & Buchne , 2007; Faul, E d elde , Buchne , & Lang, 2009), i is ound ha a s a is ical
powe o 80% is epo ed, as pe ecommenda ion in Cheah, Thu asamy, Memon, Chuah, & Ting, 2020.
Be o e applying he algo i hm, measu emen in a iance mus be e i ied (Cheah, Thu asamy, Memon,
Chuah, & Ting, 2020), mos speci ically he measu emen in a iance o composi e models (MICOM),
which e e s o a h ee-s ep app oach – i s , con igu al in a iance mus be e i ied, ollowed by
composi ional in a iance, and las ly he equali y o composi e mean alues and a iances (Hensele ,
Ringle, & Sa s ed , 2016). By doing so, i is assu ed ha he di e ences be ween wo g oups a e ound
in he s uc u al model and no caused by di e ences in he measu emen model (Hensele , Ringle, &
Sa s ed , 2016). I s ep one is achie ed bu s ep wo is no , measu emen in a iance is no assu ed. On
he o he hand, i s eps one and wo a e achie ed bu s ep h ee is no , pa ial measu emen in a iance
is gua an eed and he MGA can be pe o med, o e i y i he e a e signi ican di e ences in he
s uc u al models o bo h g oups (Hensele , Ringle, & Sa s ed , 2016; Cheah, Thu asamy, Memon,
Chuah, & Ting, 2020). S ep one is no di ec ly ob ained in Sma PLS 3, as unning MICOM in he
p og am al eady es ablishes con igu al in a iance. S eps wo and h ee a e e i ied by using he
pe mu a ion algo i hm in Sma PLS 3.
S a ing wi h he MBA-I model, conside ing he age g oups de ined, i was ound a singula ma ix
p oblem when unning MICOM, sol ed by dele ing MLO1 and PT3 (bo h p esen ing he wo s alues
o i em loadings) om he MBA-I model. When obse ing he alues o MICOM, in s ep wo, all
a iables pe o med well, excep o PIQ, leading o he dele ion o PIQ5 indica o om he model. In
s ep h ee, all cons uc s all wi hin he de ined cons uc s, excep o H9 a iance, meaning ha pa ial
measu emen in a iance is achie ed. Fo MBA-S model, bo h s ep wo and h ee p esen ed good
alues o all cons uc s, implying ull measu emen in a iance. Conside ing inally MBA-T, when
obse ing he alues o MICOM, in s ep wo, all a iables pe o med well. In s ep h ee, all cons uc s
28
all wi hin he de ined cons uc s, excep o H9 a iance, meaning ha pa ial measu emen
in a iance is assu ed. Wi h such ou comes, i is possible o p oceed wi h MGA in all models.
The e a e se e al app oaches a ailable o MGA, ha can be g ouped in pa ame ic and nonpa ame ic
app oaches (Hai , Hul , Ringle, & Sa s ed , 2017; Sa s ed , Hensele , & Ringle, 2011). Pa ame ic
app oach, p oposed by (Keil, e al., 2000), is appoin ed as he mos libe al, subjec o ype I e o s, and
i is inconsis en wi h PLS nonpa ame ic na u e, as i assumes a no mal dis ibu ion o he sample da a
(Sa s ed Hensele , & Ringle, 2011). The Welch-Sa e hwai e es is iden ical o he pa ame ic
app oach, hough i does no assume equal a iances when compa ing he mean o he wo g oups
(Cheah, Thu asamy, Memon, Chuah, & Ting, 2020). On he o he hand, nonpa ame ic app oaches
we e p oposed in academia as well, such as he pe mu a ion es (Chin & Dibbe n, 2010), poin ed as
being simila o he pa ame ic app oach bu less libe al, and equi es samples o iden ical size (Hai ,
Hul , Ringle, & Sa s ed , 2017). PLS-MGA app oach was p oposed by Hensele , Ringle, & Sinko ics,
2009, and i is based in boo s apping esul s. Las ly, o compa ing mo e han wo g oups, i was
de eloped he omnibus es o g oup di e ences (Sa s ed Hensele , & Ringle, 2011).
The i s s ep is o apply he PLS algo i hm o he models o bo h g oups, indi idually, o e i y whe he
he e a e di e ences be ween he alues o bo h, and he e is. Nex , i is e i ied whe he such
di e ence is signi ican . In MBA-I, he only ela ionship signi ican ly di e en is he one p oposed in
H9, be ween PS and PT, and i is es ablished in all app oaches o MGA excep in he pe mu a ion
algo i hm (Table 10). Women show a highe pa h coe icien (0.729 o 0.436), meaning ha pe cei ed
us in mobile banking a he in e ac ion s age, o women, is mos ly a ec ed by pe cei ed secu i y.
When conside ing MBA-S, he ela ion be ween PA and MBA-S appea s as signi ican ly di e en
be ween g oups in he pe mu a ion algo i hm (Table 11), wi h men p esen ing a signi ican ly highe
pa h coe icien (0.428 o 0.102). Las ly, in MBA-T, he ela ion be ween PA and MBA-T appea s as
signi ican ly di e en in all app oaches (Table 12), wi h men p esen ing a highe pa h coe icien (0.360
o -0.112). Mo eo e , he ela ion be ween PIQ and MBA-T is also depic ed as signi ican ly di e en in
he pe mu a ion algo i hm only (Table 12), ha ing men a highe pa h coe icien (0.099 o -0.195).
Table 10. Mul ig oup analysis o MBA-I, wi h p- alue.
Sou ce: Sma PLS 3.
PLS-MGA
Pa ame ic
es
Welch-
Sa e hwai es
Pe mu a ion
es
AOR -> MBA_I 0.9743 0.9697 0.9698 0.9638
CSE -> MBA_I 0.4355 0.4301 0.4396 0.4354
MLO -> MBA_I 0.3019 0.2708 0.3152 0.2608
PA -> MBA_I 0.3438 0.3494 0.3464 0.3654
PATU -> MBA_I 0.8001 0.7991 0.8094 0.7980
PFB -> MBA_I 0.2334 0.2558 0.2360 0.2460
PI -> MBA_I 0.8777 0.8895 0.8902 0.9182
PIQ -> MBA_I 0.8354 0.8431 0.8451 0.8486
PS -> MBA_I 0.9855 0.9897 0.9895 0.9882
PS -> PT 0.0341 0.0278 0.0447 0.0618
PT -> MBA_I 0.2884 0.3075 0.2895 0.3060
PU -> PT 0.1862 0.1458 0.1814 0.2390
29
Table 11. Mul ig oup analysis o MBA-S, wi h p- alue.
Sou ce: Sma PLS 3.
Table 12. Mul ig oup analysis o MBA-T, wi h p- alue.
Sou ce: Sma PLS 3.
5.4.2. Age as he mode a o a iable
Fo he mode a o a iable age, a di e en app oach is ollowed, as h ee di e en g oups we e
iden i ied – he younges , be ween 18 and 29 yea s old, wi h 88 esponden s, he adul s, be ween 30
and 49 yea s old, wi h 63 esponden s, and he oldes , ega ding mo e han 50 yea s’ indi iduals, wi h
56 esponden s. A s a is ical powe o 80% is achie ed in he h ee g oups.
When s a ing he analysis wi h MBA-I, i is ound ha i is no possible o compa e he wo younges
age g oups, as he samples ga he ed a e oo simila , and i is no meaning ul o he analysis a hand.
Wi h his scena io a hand, and he ac ha he sample size o he oldes age g oup is much smalle
han he sum o he wo younge g oups, i will be compa ed he younges age g oup wi h he oldes .
The e was a singula ma ix p oblem when unning he e e ed algo i hm, which was sol ed by
dele ing MLO1 and PT3 om he MBA-I model (wi h he wo s alues o i em loadings). Howe e ,
PLS-MGA
Pa ame ic
es
Welch-
Sa e hwai es
Pe mu a ion
es
AOR -> MBA_T 0.5070 0.5070 0.5141 0.5400
CSE -> MBA_T 0.7906 0.7906 0.7774 0.7730
MLO -> MBA_T 0.0813 0.0813 0.0824 0.0820
PA -> MBA_T 0.0014 0.0014 0.0033 0.0030
PATU -> MBA_T 0.1670 0.1670 0.1630 0.1360
PFB -> MBA_T 0.0844 0.0844 0.0678 0.0890
PIQ -> MBA_T 0.0556 0.0556 0.0731 0.0330
PS -> MBA_T 0.1947 0.1947 0.2046 0.2550
PS -> PT 0.0968 0.0968 0.1270 0.1280
PT -> MBA_T 0.0700 0.0700 0.0833 0.1400
PU -> PT 0.4147 0.4147 0.4040 0.4230
30
when obse ing he alues o MICOM, in s ep wo, mos a iables do no pe o m well, meaning ha
pa ial in a iance is no achie ed. Acco dingly, i is no possible o pe o m a mul ig oup analysis wi h
age g oups in MBA-I.
In he con ex o MBA-S, he e is also a collinea i y p oblem be ween he wo younge age g oups. In
his line, he oldes and he younges age g oups will be he ones chosen. Howe e , when obse ing
he alues o MICOM, in s ep wo, mos a iables do no pe o m well, meaning ha pa ial in a iance
is no achie ed. Acco dingly, i is no possible o pe o m a mul ig oup analysis wi h age g oups in MBA-
S. Rega ding MBA-T, i is ound a collinea i y p oblem be ween all age g oups. Acco dingly, by
pe o ming a MICOM analysis, i is concluded ha he e a e no signi ican di e ences be ween age
g oups, a any model.
5.4.3. Income le el as he mode a o a iable
The las obse ed mode a o a iable is income le el, in which wo g oups we e ound – low income,
o a ne mon hly income le el below 1000 eu os, wi h 97 esponden s, and high income, o a ne
mon hly income le el abo e 1000 eu os, wi h 94 esponden s, and a s a is ical powe o 80% is
achie ed.
Howe e , when pe o ming MICOM, i is ound ha he e a e se e al issues wi h se e al a iables in
s ep wo, o all he h ee models, which means ha measu emen in a iance is no achie ed in nei he
one.
31
6. RESULTS ANALYSIS AND DISCUSSION
The i s pa o he analysis in he p esen disse a ion ega ds e i ying whe he ac o s in luencing
adop ion di e ac oss se ice s ages. In MBA-S, he da a ga he ed e ealed ha he mos signi ican
ac o s a e CSE, PA, PFB, PS, and PU, which means ha H3, H1, H10, H9, and H8 s and o MBA-S. When
conside ing MBA-I, AOR, PS, and PU e ealed o be he only signi ican cons uc s. In MBA-T, PFB, PS, and
PU e ealed o be he signi ican cons uc s. Facing his scena io, he a iables ha a e no ele an a
any s age a e PATU, MLO, PIQ, PT, and PI, meaning ha he hypo heses H4, H5, H6, H7, and H11, do no
s and.
Fo mobile banking adop ion a he s a ic s age, he s onges p edic o is PFB, he exac same inding as
Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018, bu wi h a s onge e ec – a uni posi i e change on
PFB leads o a 0.456 uni posi i e change on he adop ion o mobile banking a he s a ic s age, explaining
almos hal o he a ia ion, gi en ha he emain p edic o s a e cons an . Acco dingly, i is key o
consume s o achie e unc ional bene i when using mobile banking o check accoun o in es men
in o ma ion, and alue being able o do so a any gi en ime in any place. Mo eo e , compu e sel -
e icacy and pe cei ed awa eness appea as ele an p edic o s - he consume comp ehends ha he
sel -knowledge o his compu ing skills is key o check i s accoun o in es men in o ma ion, in he con ex
o mobile banking applica ion, and unde s ands he concep and he bene i s o using i . E en hough CSE
was no a signi ican p edic o in Sha ee , Baabdullah, Du a, Kuma , & Dwi edi, 2018 a he s a ic s age,
PA was also ound as signi ican . The impo ance o he las wo a iables migh indica e ha he inqui ied
consume s a e s ill in an ea ly s age o mobile banking adop ion. I is also impo an o men ion ha , e en
hough PS and PU a e signi ican p edic o s o PT, he la e is no a signi ican p edic o o he adop ion
o mobile banking a he s a ic s age, so he wo indica o s will no be conside ed o he p esen model
(Figu e 4).
Figu e 4. Empi ical model o mobile banking adop ion a he s a ic s age.
Sou ce: By au ho .
Conside ing mobile banking adop ion a he in e ac ion s age, in which he e is a wo-way communica ion
be ween consume s and he se ice p o ide , a ailabili y o esou ces e ealed o be an impo an
p edic o o he inqui ied consume s, con a ily o wha was ound in Sha ee , Baabdullah, Du a, Kuma ,
& Dwi edi, 2018 o e en in Sha ee M. A., Kuma , Kuma , & Dwi edi, 2011, o he con ex o e-
go e nmen se ices, which gi es s eng h o he conclusion e ie ed in he p e ious model – consume s
migh s ill be in an ea ly s age o adop ion and ind issues on he esou ces a ailable o access mobile
banking, as in e ne a ailabili y and i s cos , o ins ance. Fo his model, PS is ound o be a signi ican
p edic o o mobile banking adop ion a he in e ac ion s age, a ec ing di ec ly he dependen a iable,
bu wi h a nega i e e ec , ha is, one uni posi i e change in PS, leads o a 0.132 nega i e change on he
adop ion o mobile banking a he in e ac ion s age, which is he con a y o wha is ound in he li e a u e
32
and consequen ly in he p oposed posi i e ela ion in H12. Such inding migh indica e p oblems in
indica o a iables conside ed o PS (Figu e 5).
Figu e 5. Empi ical model o mobile banking adop ion a he in e ac ion s age.
Sou ce: By au ho .
In he ansac ion s age, e en hough PU and PS a e signi ican o PT, PT is no signi ican o MBA-T,
hence such e ec is dis ega ded. Acco dingly, he only ele an ac o o mobile banking adop ion a he
ansac ion s age is PFB – one uni change in PFB leads o a 0.586 posi i e change in mobile banking
adop ion a he e e ed s age. Consume s alue he possibili y o make a paymen o a ans e anywhe e
and any ime, conside ing ha he u iliza ion o he mobile banking se ice p o ides mo e bene i s han
going o a physical elle o pe o m he same ope a ions (Figu e 6).
Figu e 6. Empi ical model o mobile banking adop ion a he ansac ion s age.
Sou ce: By au ho .
Rega ding he ac o s ha we e no ound o be signi ican a any s age, i is no su p ising ha a mul i-
lingual op ion is no ele an o he inqui ied consume s, as mos mobile banking se ices a e a ailable
in Po uguese. E en hough he e was no ques ion ega ding he esponden ’s na ionali y, he
ques ionnai e was handed ou in Po uguese, meaning ha only Po uguese speake s we e able o
answe . Acco dingly, i is expec ed ha MLO is no a ele an ac o a any s age. PATU, acco ding o
Sha ee M. A., Kuma , Kuma , & Dwi edi, 2011, is a simila concep o CSE, meaning ha a lack o
signi icance o his a iable is expec ed (since CSE is signi ican a he s a ic s age). Mo eo e , pe cei ed
image is no signi ican as well, in line wi h he indings o Sha ee , Baabdullah, Du a, Kuma , & Dwi edi,
2018, conside ing ha he u iliza ion o digi al se ices is he no m nowadays. On he o he hand, he lack
o signi icance o PT and PIQ is unexpec ed, conside ing he inding in Sha ee , Baabdullah, Du a, Kuma ,
& Dwi edi, 2018, o e en in Ba kho da i, Nou ollah, Mashayekhi, Mashayekhi, & Ahanga , 2016; Mokh a ,
Ka an, & Hidaya -u -Rehman, 2017; Ezeh & Nkamnebe, 2018; Ma hew, Jose, G, & Chacko, 2020.
In he second pa o he analysis, i was analysed he in luence o i e demog aphic mode a o a iables.
Due o s a is ical p oblems in ou o hose, gende was he only a iable conside ed in he analysis as a
mode a o . Acco dingly, i was ound ha he ela ion be ween PA and MBA-S, and PA and MBA-I, appea s
as signi ican ly di e en o men and women. In MBA-I, he e is a signi ican di e ence in he ela ion
be ween PS and PT, o men and women, wi h a s onge e ec o PS in PT o he la e .
Gi en he indings o he mos impo an ac o s o he adop ion o mobile banking a he h ee p esen ed
s ages, only he di e ences (be ween men and women) in he ela ion o PA and MBA-S a e ele an o
he analysis. Acco dingly, i is ound ha men p esen a highe pa h coe icien (0.428 o 0.102), leading
o he conclusion ha men end o be mo e awa e o he possibili y o check accoun in o ma ion h ough
39
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10. APPENDIX
Table 1 – Ques ionnai e applied
Cons uc I em Type o a iable
Gende Géne o Cha ac e iza ion/Mode a o a iable
Age Idade Cha ac e iza ion/Mode a o a iable
Educa ion le el Escola idade Cha ac e iza ion/Mode a o a iable
Occupa ion Ocupação Cha ac e iza ion/Mode a o a iable
Income le el Rendimen o líquido mensal Cha ac e iza ion/Mode a o a iable
U iliza ion o
mobile banking
Já u ilizei o se iço bancá io mó el (possibilidade de usu ui de se iços
bancá ios a a és do meu elemó el).
Cha ac e iza ion/Mode a o a iable
1. Sei que exis e a possibilidade de u iliza o se iço bancá io mó el.
2. Conheço os bene ícios de u iliza o se iço bancá io mó el.
3. Ti e algum ipo de o mação (po pa e do meu banco, ou po e
p ocu ado in o ma -me, po exemplo) quan o às ca a e ís icas do se iço
bancá io mó el.
4. A conexão de In e ne que u ilizo no meu elemó el não é ca a.
5. A minha conexão de In e ne é boa o su icien e pa a u iliza o se iço
bancá io mó el em qualque lado.
6. Tenho semp e acesso a uma conexão de In e ne de al a elocidade em
qualque lado, a a és do meu elemó el, pa a acede ao se iço bancá io
mó el.
7. Tenho quali icações su icien es pa a u iliza o se iço bancá io mó el.
8. Tenho quali icações su icien es pa a u iliza o se iço bancá io mó el
a a és do na egado do meu elemó el.
9. Tenho uma capacidade ele ada na u ilização do se iço bancá io mó el.
10. Tenho con iança nas minhas capacidades na u ilização do se iço
bancá io mó el.
11. Pessoas/emp esas que u ilizam o se iço bancá io mó el pa a
acede em a se iços inancei os êm uma posição de ele o na sociedade.
12. Pessoas/emp esas que u ilizam o se iço bancá io mó el pa a
acede em a se iços inancei os êm mais p es ígio do que aqueles que
não u ilizam.
13. Usu ui do se iço bancá io mó el pa a acede a se iços inancei os
melho a o es a u o social das pessoas/emp esas.
14. Ap ende a in e agi com o se iço bancá io mó el é ácil pa a mim.
15. É ácil in e agi com o se iço bancá io mó el.
16. U iliza o se iço bancá io mó el é cla o e comp eensí el pa a mim.
17. Consigo acilmen e aze as ope ações que p e endo quando u ilizo o
se iço bancá io mó el.
18. A in o mação disponibilizada no se iço bancá io mó el es á
a ualizada.
19. A in o mação disponibilizada no se iço bancá io mó el é ácil de
comp eende .
20. O se iço bancá io mó el disponibiliza oda a in o mação ele an e e
necessá ia pa a co esponde às minhas necessidades.
21. O se iço bancá io mó el o nece in o mação co e a quan o aos
se iços disponibilizados.
22. O se iço bancá io mó el disponibiliza in o mação de o ma
sequencial e sis emá ica.
Pe cei ed
in o ma ion
quali y (PIQ)
Independen a iable
Pe cei ed
awa eness (PA)
Independen a iable
Independen a iable
Independen a iable
Independen a iable
Independen a iable
Pe cei ed
abili y o use
(PATU)
Pe cei ed
image (PI)
Compu ing-sel
e icacy (CSE)
A ailabili y o
esou ces
(AOR)