The future of banking: What are the actual barriers to bank digitalization?

Ul ich-Diene , Flo ian; D oule ý, Ondřej; Špaček, Mi osla
A icle
The u u e o banking: Wha a e he ac ual ba ie s o
bank digi aliza ion?
BRQ Business Resea ch Qua e ly
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Sugges ed Ci a ion: Ul ich-Diene , Flo ian; D oule ý, Ondřej; Špaček, Mi osla (2025) : The u u e o
banking: Wha a e he ac ual ba ie s o bank digi aliza ion?, BRQ Business Resea ch Qua e ly,
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In oduc ion
Digi al ans o ma ion and he adop ion o new echnolo-
gies ha e inc easingly aised ques ions abou changes ha
adi ional companies and hei managemen mus s a e-
gically ace (Fe nandez-Vidal e al., 2022; Hess e al.,
2016). Digi aliza ion has in luenced in e nal and ex e nal
pe spec i es conce ning s a egic di ec ion, compe i i e-
ness, business models, decision-making, inno a ion,
en ep eneu ship, and business pe o mance, as well as
cus ome ela ions (Aydalo & Keeble, 2018; B. Cohen
e al., 2017; L. Li e al., 2017). To cope wi h ma ke -d i en
changes, such as he inc eased digi aliza ion igge ed by
he ecen COVID-19 pandemic (Fa aj e al., 2021;
Ri e a-P ie o e al., 2022), companies mus adap e en
as e o hei en i onmen by e hinking and, i necessa y,
e o ming hei adi ional p inciples (Benz e al., 2021;
Da idsson e al., 2021; D oule ý e al., 2021; Gombe ,
Kau mann, e al., 2017; Hund & G ün, 2022; Maícas,
2023; Rie mann, 2021).
Wi hin he pas ew yea s, se e al new digi ally d i en
business models eme ged in a ious sec o s, including
banking (Gimpel e al., 2016; Lee & Shin, 2018; Yang &
Wang, 2022). So-called inancial echnology companies
(known unde he ac onym FinTechs) a e new echnology-
based businesses ha aim o compe e wi h adi ional
inancial ma ke pa icipan s. They a e also seen as a pu e
echnology seeking o imp o e and au oma e he deli e y
o inancial se ices (Schue el, 2016).
Today, inancial se ices a e pa icula ly exposed o
addi ional p essu e om (1) o hcoming ma ke egula ions
The u u e o banking: Wha a e he ac ual
ba ie s o bank digi aliza ion?
Flo ian Ul ich-Diene , Ondřej D oule ý
and Mi osla Špaček
Abs ac
The banking sec o mus con on challenges a ising om globaliza ion, he demand o new business models (BMs),
inc easing egula ion, and e e -ad ancing digi aliza ion. In his con ex , inno a i e compe i o s, namely FinTechs, a e
challenging banks and o cing hem o e hink exis ing s a egies and s uc u es. In pa icula , he digi al ans o ma ion
o BMs ha ha e been in place o decades ep esen s a majo challenge o companies and hei execu i es. In his
a icle, 407 Ge man bank ep esen a i es we e su eyed o iden i y, quan i y, and analyze implemen a ion ba ie s in
he con ex o bank digi aliza ion om a decision-make ’s pe spec i e. By applying s uc u al equa ion modeling, he
au ho s quan i ied a a ie y o ba ie s and es ed hei in luence on he deg ee o digi aliza ion a banks. The s udy
unco e ed s uc u al ela ionships be ween ba ie s exp essed as obse ed a iables—pe sonal in ol emen , s a egic
co po a e managemen , echnology and egula ion, and employees—and he deg ee o digi aliza ion as a la en a iable o
banks. The indings inc ease bank p ac i ione s’ unde s anding and awa eness o ba ie s o digi aliza ion and con ibu e
o he ield o bank digi aliza ion.
JEL CLAssIFICATIOn: G21, M1, O33
Keywo ds
Banks, ba ie s, decision-make s, digi aliza ion, inancial se ices, ans o ma ion
Facul y o Business Adminis a ion, P ague Uni e si y o Economics and
Business, P ague, Czech Republic
Co esponding au ho :
Flo ian Ul ich-Diene , Facul y o Business Adminis a ion, P ague
Uni e si y o Economics and Business, nám. Wins ona Chu chilla
1938/4, 130 67 P ague, Czech Republic.
Email: [email p o ec ed]
1211597BRQ 0010.1177/23409444231211597BRQ Business Resea ch Qua e lyUl ich-Diene e al.
esea ch-a icle2023
Regula Pape
492 Business Resea ch Qua e ly 28(2)
(e.g., Basel III/IV, Sol ency I/II, Sus ainable Finance
Disclosu e Regula ion, Co po a e Sus ainabili y Repo ing
Di ec i e, e c.), which a e expec ed o inc ease cos s
(Wei e , 2014); (2) inc easing compe i ion wi h global
ma ke pa icipan s ha en e he inancial se ices a ena
(e.g., Apple, Amazon, Google, e c.); and (3) changes in
cus ome beha io and he demand o adi ional inan-
cial p oduc s and se ices (Diene & Špaček, 2021). In
hei ecen quali a i e s udy, Diene and Špaček (2021)
p o ided insigh s o bank manage s in o he nume ous
hu dles o o e come ega ding digi aliza ion in he bank-
ing sec o . While his o me wo k by Diene and Špaček
(2021) iden i ied he main ba ie s o digi al ans o ma-
ion in a speci ic pa o he Ge man banking sys em om
a quali a i e pe spec i e, he cu en a icle di es deepe
in o he issue and illus a es quan i iable ela ionships
among he obse ed a iables and hei unde lying la en
cons uc s. The combina ion o quali a i e and quan i a-
i e app oaches o digi aliza ion ba ie s iden i ica ion
complemen s each o he .
I is s iking ha inancial se ices ha e become inc eas-
ingly digi al (Jünge & Mie zne , 2020). Howe e , he main
p oblem emains ha all-encompassing and apid digi aliza-
ion is no equally possible a e e y bank. Today, digi al
compe i o s wi h inno a i e concep s, p oduc s, and se -
ices add ess cus ome s in mul iple ways, especially using a
mode n mul i-channel app oach o sales, communica ion,
and ma ke ing (Co iñas e al., 2010). Do lei ne & Ho nu
(2016), Do lei ne e al. (2020) obse ed he g owing in lu-
ence o hese business models on de elopmen in he inan-
cial indus y, including an inc ease in ma ke sha e.
Fo companies seeking a sus ainable compe i i e
ad an age, e iciency, inno a ion, and pe sis ence a e he
mos c i ical s a egic ac o s o success o ailu e (Lama a
e al., 2003). Meanwhile, many banks and hei decision-
make s ecognized he need o ins i u ional changes o
cope wi h de elopmen s and began e hinking and/o
e o ming hei s a egy (B aun, 2016; Mohan, 2015; Nagy
e al., 2016).
Building on indings like hose o Diene and Špaček
(2021), highligh ing manage ial pe spec i e on ba ie s o
bank digi aliza ion, and Chhaida e al. (2022), no ing he
posi i e e ec s o digi aliza ion, i seems mo e impo an
han e e be o e o ocus on ba ie s o digi aliza ion in
banking. Since execu i es ha e a signi ican in luence on
hei o ganiza ions, including hei s a egic o ien a ion,
and u u e success (Cu i & Mu gia, 2018), his s udy pa -
icula ly aims o examine digi aliza ion om a decision
make ’s pe spec i e, as well as o quan i y ba ie s o digi-
aliza ion. Thus, he ollowing esea ch ques ions (RQs)
we e de eloped:
RQ1: Wha a e he ba ie s o digi aliza ion in an
inc easingly echnological banking en i onmen ?
RQ2: Wha e ec do ba ie s o digi aliza ion ha e on
he deg ee o digi aliza ion o banks om a decision-
make ’s pe spec i e?
By ocusing on changes in he inancial se ices ma -
ke , his s udy p o ides a de ailed analysis ha add esses
he digi aliza ion o banking om a b oade pe spec i e,
including he ans o ma ion o he indus y, and decision-
make s unde s anding o he deg ee o digi aliza ion o
banks ela ed o hei wide su oundings. As a esul , we
add ess a signi ican issue in he echnological de elop-
men o banking, in ending o obse e new e ol ing busi-
ness models and echnologies.
The o hcoming sec ions summa ize he li e a u e o
da e and exis ing e idence on digi aliza ion in he inancial
sec o , ocused on he banking indus y. The li e a u e
inspi ed he o mula ion o he leading hypo heses o be
es ed. The da a collec ion p ocedu es aken in 2020
owa d eaching ou sample o Ge man bank ep esen a-
i es (N = 407) a e desc ibed in de ail in he ollowing sec-
ion, as well as an analy ical app oach elying on s uc u al
equa ion modeling (SEM) as he p ima y me hod.
Subsequen ly, he analysis esul s a e p esen ed and dis-
cussed om bo h p ac ical and heo e ical pe spec i es.
Finally, he s udy’s limi a ions and u u e esea ch di ec-
ions a e desc ibed.
Li e a u e e iew
Changes in banking due o digi aliza ion
In ligh o inc easing digi aliza ion, schola s assume ha
digi al echnologies will p o oundly change exis ing s uc-
u es and he gene al wo ld o wo k (Fedo e s e al., 2021).
These echnology-d i en de elopmen s a ec bo h
na ional and in e na ional banking ma ke s, his o ically
cha ac e ized by hei e ol ed o ganiza ional s uc u es
(Deu sche Bundesbank, 2022, pp. 88–129; Dö y, 2022;
Godda d e al., 2007; Kna o, 2022).
In his espec , “digi iza ion” e e s o he p ocess o
ans o ming analog o physical o ms o in o ma ion
in o digi al ones, whe eas “digi aliza ion” e e s o he
ans o ma ion o indus ies, business models, and p o-
cesses. In ecen decades, digi aliza ion has enabled
di ec and indi ec ans o ma ion in he inance indus y
(Hana izadeh & Amin, 2023; Shche ba ykh e al., 2021).
Today, digi al ans o ma ion appea s as a compelling
p ocess o change o which indi iduals and en i e o gan-
iza ions mus ace and espond (Vey e al., 2017). This is
a p ocess o using digi al echnologies o c ea e new
business p ocesses, co po a e cul u es, ways o wo king,
cus ome expe iences and o e ings, o o change exis -
ing ones o mee changing business and ma ke demands
(Hess e al., 2016; Nadka ni & P ügl, 2021; Pa iainen
e al., 2017, p. 64).
Ul ich-Diene e al. 493
In o ma ion and communica ion echnologies a e p ob-
ably he mos impo an ac o s in luencing digi al change,
igge ing i bo h ac i ely and passi ely (Wan, 2006, pp.
1–3). Allen e al. (2002) men ioned ha he mode n inance
indus y p o ides se ices ia elec onic communica ion
and compu a ion. Digi aliza ion, howe e , is mo e han he
me e unde s anding ha an indus y o a company is
changing a a echnological le el. Ra he , i is a holis ic
app oach o inno a i e p ocesses in banking and has mul-
iple d i e s (Al , 2016, pp. 30–32; Do schel, 2018; Ki sios
e al., 2021; Manz, 2018, p. 175; Naimi-Sadigh e al.,
2022; S ie zel e al., 2018, p. 28).
Ohle e al. (2022) e ealed ha di e en sec o s o he
economy a e a a ious s ages o de elopmen , wi h inan-
cial se ices being among he mos ad anced. Digi aliza ion
has accele a ed apidly in ecen yea s and signi ican ly
impac s how banks ope a e and p o ide hei se ices in
he u u e (Niemand e al., 2021). One c ucial aspec o
digi aliza ion in banking is he de elopmen o new busi-
ness models and p oduc s. I is mo e han online banking;
i is he echnology-based de elopmen o companies
o e ing inno a i e inancial p oduc s and se ices, nowa-
days o en enabled by he use o echnologies such as
blockchain o a i icial in elligence (Rahman e al., 2021;
Vale o e al., 2020).
Ano he impo an aspec o digi aliza ion in banking
is he au oma ion o p ocesses. Many banking p ocesses
a e au oma ed using echnologies such as Robo ic P ocess
Au oma ion o Machine Lea ning, esul ing in highe
e iciency and lowe cos s (Villa & Khan, 2021). In pa -
icula , he accele a ed de elopmen d i en by he
COVID-19 pandemic led o signi ican changes in he
banking ma ke (Flögel & Gä ne , 2020; Guang-Wen &
Siddik, 2023; Romdhane, 2021). The e o e, banks had o
accele a e hei in-house de elopmen o inno a ions in
o de o keep up wi h compe i o s in he u u e (Ba a &
Ruggie o, 2022). This equi es de eloping mode n solu-
ions, al hough s a egic pa ne ships and coope a ion
be ween banks and echnology companies o hei acqui-
si ion a e also needed, om which bo h sides can bene i
(Bella dini e al., 2022; Ho nu e al., 2021; Ho á h
e al., 2022; Kwon e al., 2023).
I seems e iden ha digi aliza ion accele a es de elop-
men s in banking and esponds o changing cus ome
beha io (Men ad & Va ga, 2020; Oehle e al., 2021;
Reichs ein e al., 2019; an de C uijsen & Dieps a en,
2017). This is e lec ed in apidly g owing online ma ke s
and inc easingly indi idualized cus ome o e ings
(Eu opean Banking Au ho i y, 2021; S a is a, 2021c). In
he u u e, i can be assumed ha digi al banking in all i s
o ms will es ablish i sel e en mo e apidly in he ma ke .
(EHI Re ail Ins i u e, 2019; Men ad & Va ga, 2020;
Wewege e al., 2020).
Since echnological change is o en associa ed wi h
po en ial dis up ion (Ch is ensen & Bowe , 1996),
decision-make s o en hesi a e o de elop and implemen
digi al solu ions and new business concep s (Moschko
e al., 2020; Oks e al., 2016). B eidbach e al. (2020)
iden i ied managemen challenges in digi al ans o ma-
ion by analyzing 1,545 a icles ela ed o inancial ech-
nology and inno a i e business models in inancial
se ices. They emphasized he complexi y o digi al sys-
ems; he o ches a ion o alue c ea ion h ough coop-
e a ion wi h FinTechs; and he de elopmen o elas ic
in as uc u es, models, and ma ke s.
Iheanacho and Umuko o (2022) con i med ha pa -
ne ships play a c ucial ole in unlocking he eno mous
po en ial o digi al inancial se ices. Accep ing he ech-
nology i sel is a p e equisi e o implemen ing a digi al
s a egy and digi aliza ion (Filo o e al., 2020). Jo ge e al.
(2019) we e he i s o analyze bank manage s’ unde -
s anding and he impac o digi al ans o ma ion and dis-
up i e echnology on hei daily ou ine, wi h he la e
being a p ocess ha begins in a small, inconspicuous niche
o an indus y (Ch is ensen e al., 2015). Based on a new
echnology o business model, p oduc s o se ices a e
de eloped o ini ially appeal o only a small segmen o
cus ome s. Acco ding o Ch is ensen and Bowe (1995),
his o e ing gains momen um, hen becomes a dominan
ma ke ac o , displacing many es ablished companies and
hei p oduc s. In his con ex , Gombe , Koch, and Sie ing
(2017) conside ed i c ucial o decision-make s o ha e a
clea pe cep ion o ma ke de elopmen s and an unde -
s anding o possible ba ie s o he implemen a ion o digi-
aliza ion, as well as a gene al unde s anding o echnology
since, acco ding o Kelche skaya e al. (2019), expe s’
digi al knowledge has a cons an and signi ican e ec on
he deg ee o digi aliza ion.
Digi aliza ion and digi al ans o ma ion in
banking
A de ailed examina ion o he in luence o digi aliza ion on
banking e ealed ha i a ec s cus ome s, banks, and
ex e nal p o ide s. Al (2016) de ined consolida ion,
decen aliza ion, in e na ionaliza ion, egula ions, spe-
cializa ion, and cus ome o ien a ion, while Do schel
(2018), Manz (2018), and S ie zel e al. (2018) de ined
p ocess op imiza ion and gene al accele a ion. These d i -
e s ou line a dynamic ma ke en i onmen o banks,
which ha e long been able o ope a e in a compa a i ely
calm and egula o y-p o ec ed en i onmen . Al hough dig-
i aliza ion is unde s ood as he key d i e , i in luences
o he d i e s, such as he consolida ion and in e na ionali-
za ion o exis ing businesses, s anda diza ion o p ocesses,
o cus ome o ien a ion and, he e o e, has a b oade e ec
(Al e al., 2018).
This esul s in adjus men s o exis ing s uc u es, a ec -
ing no only in e nal p ocesses and sys ems bu , abo e all,
in e ac ion wi h cus ome s and se ice p o ide s (Vale o
494 Business Resea ch Qua e ly 28(2)
e al., 2020). In e nally, bank digi aliza ion includes he
applica ion o concep s o indus ializa ion, such as mod-
e nizing exis ing a chi ec u es and co e banking sys ems,
which a e o en implemen ed on olde echnologies
(Mekinjić, 2019). In cus ome o ien a ion, i a ge s he
u u e design o he cus ome in e aces, while se ice p o-
ide in e ac ion may aim a a mo e cos -e icien se ice
p o ision in ne wo ks as well as he expansion o p oduc
o e ings and ma ke p esence (Gimpel e al., 2018).
Do lei ne e al. (2017b, 2020) con i med he g owing
and p ospec i e in luence on banking o new digi al busi-
ness models in he inance indus y. Mo eo e , Fe nández-
Po illo e al. (2019) analyzed he impac o digi aliza ion
on business and inno a ion pe o mance. Siedle e al.
(2021) concluded ha a company’s le el o digi aliza ion
is di ec ly ela ed o i s pe o mance and ha i is neces-
sa y o include his ac o in i s pe o mance model.
Based on he concep o en ep eneu ial o ien a ion,
Niemand e al. (2021) in es iga ed how banks can use ac-
ics and s a egies o achie e supe io pe o mance in he
age o digi aliza ion. They used a single-i em cons uc o
measu e a bank’s digi aliza ion le el. The esul s indica ed
ha a bank’s le el o digi aliza ion does no a ec i s p o -
i abili y o achie e supe io pe o mance. In addi ion,
banks can s ill succeed e en i hey lag behind hei di ec
compe i o s in ansi ioning o digi al se ices and online
banking.
Tho dsen e al. (2020) showed ha he unde s anding o
digi al is no widesp ead, and mos iden i ied measu emen
models do no mee scien i ic e alua ion c i e ia. Howe e ,
G obe g e al. (2016) hema ized digi aliza ion om a
scale de elopmen pe spec i e and analyzed i s e ec s on
he pe o mance o new p oduc s and se ices, aking in o
accoun aspec s o analy ics, alue-added, ma ke ing and
sales, p oduc s, se ices, and p ocesses. In his con ex ,
hey de eloped a scale called “Deg ee o Digi aliza ion”
(DoD).
Digi al inno a ion and inancial echnology
Inno a ions in he digi al and inancial con ex a e cha ac-
e ized by mul iple in luencing ac o s (Agyei-Boapeah
e al., 2022; Beck e al., 2016). Two main heo ies explain
inno a i e de elopmen and simila ly apply o banking
and FinTech: Schumpe e ’s heo y o c ea i e des uc ion
(Schumpe e , 1943) and Ch is ensen’s heo y o dis up i e
echnology (Julapa & Kose, 2018).
In gene al, he e m “FinTech” is he abb e ia ion o
“ inancial echnology,” which can be used o desc ibe
mode n digi al inancial se ices (Paule & Ma oo i, 2019;
Schue el, 2016). I ep esen s digi aliza ion and is used
o companies ha use and apply new echnology o hei
wo k (Do lei ne e al., 2017a). Howe e , i can also be
used o e e o he echnology i sel . In his case, he con-
ex ual meaning is decisi e. Do lei ne and Ho nu (2016,
p. 4) e e ed o Kawai (2016, p. 1), who desc ibed FinTech
“as echnologically enabled inancial inno a ion. I is gi -
ing ise o new business models, applica ions, p ocesses
and p oduc s. This could ha e a ma e ial e ec on inan-
cial ma ke s and ins i u ions and he p o ision o inancial
se ices.”
Ba oso and Labo da (2022), Boo e al. (2021), and
Nug oho and Hamsal (2021) desc ibed FinTech as he cen-
al concep o s uc u ally signi ican change and digi ali-
za ion in he inancial se ices indus y. Findings by
Oma ini (2017) and Thako (2020) e ealed he di e si y
o FinTech business models. FinTech uses digi al in a-
s uc u es o es ablish no el o e s and ansac ion me h-
ods in wha is adi ionally seen as he emi o he banking
business (e.g., in es men s a egies, and lending and pay-
men ansac ions; B. Li & Xu, 2021; Tseng & Guo, 2022).
The cha ac e is ics o his digi aliza ion p ocess include
simpli ied access o bank p oduc s and se ices o end
use s, ia he in e ne o mobile apps; an inc ease in p ocess-
ing speed, including au oma ion p ocesses; cos educ ions;
s ong se ice o ien a ion and con enience; anspa ency;
and he use o ne wo k e ec s (S a is a, 2021a). Howe e ,
acco ding o K oene (2017), digi aliza ion is no au oma i-
cally conside ed as FinTech. Ra he , he conside s i o be (1)
new in e aces o he cus ome , (2) new ma ke places, (3)
new p ocesses, and (4) added alue h ough new beha io-
al possibili ies. Feue iegel and Neumann (2017, p. 77)
s a ed ha FinTech could be unde s ood as he inpu o
echnology; an o ganiza ion; and he money low ha
leads o new se ices, p oduc s, p ocesses, o new business
models—FinTechs a e de ined as c ea o s, change s, o
imp o e s ha dis up and he eby c ea e compe i ion
h ough he use o in o ma ion echnology (IT) in he
inancial sec o .
The inancial echnology ma ke and i s
cha ac e is ics
The FinTech ma ke can be desc ibed om a quan i a i e
pe spec i e. Na ional ma ke s di e conside ably in e ms
o size and ma ke pa icipan s. Fo example, he Ge man
banking sec o , as one o he mos de eloped ma ke s in
he wo ld, is one o he la ges FinTech ma ke s in he
wo ld, nex o he Uni ed S a es (Camb idge Cen e o
Al e na i e Finance, 2016, p. 56; E ns & Young, 2016;
KPMG, 2020; S a is a, 2021a). In pa icula , Do lei ne
e al. (2020) iden i ied 694 ac i e FinTechs in Ge many
as o 2020. Howe e , signi ican and anspa en da a on
he ma ke om public au ho i ies emain lacking.
Comp ehensi e da a a e cu en ly only a ailable om
S a is a’s annual Digi al Ma ke Ou look, which desc ibes
de elopmen s in he global FinTech ma ke (S a is a,
2021b, 2021c). The da a show ha he inancial echnol-
ogy ma ke con inues o expand. Thus, i can be seen as a
long- e m compe i o o adi ional banking.

Ul ich-Diene e al. 495
In he global FinTech ma ke , digi al paymen s ep e-
sen he la ges segmen , wi h an es ima ed o al ansac-
ion alue o US$8,502 billion as o 2022 (S a is a, 2021c).
By 2026, his sec o is p ojec ed o ha e a use base o
app oxima ely 5,197 million people. In al e na i e inance,
he a e age ansac ion alue pe use is expec ed o be
US$30.13,000 in 2022 (S a is a, 2021c). Fu he mo e,
acco ding o o ecas s, neo-banking (e.g., Re olu , Chime,
Nubank, N26 and Monzo, which a e hidden champions in
he ma ke ; Benz e al., 2021) is p ojec ed o g ow by
40.3% in 2023, and he o al ansac ion olume o
US$8.61 billion is expec ed o be eached by 2026, which
co esponds o he expec ed annual g ow h o 22.36%
(CAGR) in ansac ion olume (S a is a, 2022).
Some banks ha e al eady ecognized his de elopmen
and he need o change (Deme zis e al., 2018; Mu inde
e al., 2022). In esponse, hey es ablished hei own uni s
and companies ia accele a o s and/o incuba o s (S.
Cohen, 2014) o a emp ed o massi ely in es in o col-
labo a e wi h FinTechs o secu e a i s -mo e ad an age
(Bella dini e al., 2022; Ho nu e al., 2021; Pauwels e al.,
2015; Riikkinen & Pihlajamaa, 2022).
In summa y, he en i e inancial ma ke is ans o ming,
in o de o emain compe i i e in he long e m (Elia e al.,
2023; Jappa o a & Rupeika-Apoga, 2017). Va ious a ings
and ankings assess he s a e o digi aliza ion conce ning
FinTech by coun y, o which Ge many is one o he lead-
ing coun ies (La inenko e al., 2023). The Ge man bank-
ing sys em is cha ac e ized by i s banking di e si y and, a
he same ime, by s ic supe ision and egula ion by he
s a e inancial supe iso y au ho i ies. A he same ime, i
ep esen s one o he wo ld’s mos de eloped and solid
sys ems (In e na ional Mone a y Fund, 2022). Inno a i e
inancial p oduc s ecei e signi ican a en ion om
Ge man banks (PwC, 2020), p o iding a a o able ma ke
en i onmen o digi aliza ion s udies.
Hypo hesis de elopmen
Li le esea ch on managemen gaps has been iden i ied in
banks’ digi al adap a ion o he a o emen ioned changing
compe i i e si ua ion. Diene and Špaček (2021) conduc ed
a quali a i e s udy based on con ex ual in e iews wi h
banking p o essionals o iden i y ba ie s o digi aliza ion.
Thei esul s yielded a po en ial i em se highligh ing eigh
ca ego ies ele an o cope wi h digi al ans o ma ion in
banking: s a egy and managemen , cus ome s, employees,
echnology and egula ion, knowledge and p oduc , ma -
ke , pa icipa ion, and bene i s. Thei indings sugges ed
ha he espec i e main ca ego iza ions ha e a g ea di e -
si y o in e p e a ions and a high le el o de ail. On his
basis, an explo a o y ac o analysis (EFA) was conduc ed,
which acili a ed he de elopmen o he hypo heses o
his s udy, aking in o accoun p e ious s udies (Fab iga
e al., 1999; Hallen e al., 2020; Sh es ha, 2021; Vissa,
2012).
Based on he ac o analysis esul s, he hypo heses a e
assumed o a ec he DoD o banks, ha is, he ou inde-
penden a iables ha ing an e ec on he dependen a i-
able DoD. Hence, he hypo heses we e based on he
espec i e a iable explana ions and heo e ical assump-
ions, which a e decisi e o he di ec ion o he e ec o
he independen a iable. Fu he mo e, hese hypo heses
a e suppo ed by scien i ic e idence ha is ans e able o
he p esen s udy. The ollowing hypo heses (H) we e
examined:
H1: Pe sonal in ol emen in digi al de elopmen has a
posi i e e ec on he deg ee o digi aliza ion o banks.
The exis ing li e a u e suppo s he idea ha cus ome
and employee pe sonal in ol emen in digi al de elop-
men posi i ely a ec s DoD a banks. Schola s ha e high-
ligh ed ha pe sonal in ol emen mainly exe s an e ec
h ough p oac i e cus ome o ien a ion and de eloping
employee compe ence in digi al ans o ma ion (Blocke
e al., 2011; Ce indama Kozanoglu & Abedin, 2021; on
Leipzig e al., 2017; Wa ne & Wäge , 2019):
H2: S a egic co po a e managemen has a posi i e
e ec on he deg ee o digi aliza ion o banks.
H2 is subs an ia ed by s udies suppo ing he assump ion
ha s a egic managemen and leade ship a e essen ial pil-
la s o he co po a e change and de elopmen p ocesses
(Belias & Kous elios, 2014; Hosme , 1982; Johnson, 1992;
Nag e al., 2007). In his ega d, s a egic managemen and
ela ed p ocesses a e o be unde s ood as he se o commi -
men s, decisions, and ac ions equi ed o achie e s a egic
compe i i eness and supe io e u ns (Hi e al., 2019, p. 6).
This in u n is closely ela ed o leade ship, desc ibed as a
p ocess whe eby one indi idual in luences a g oup o peo-
ple o achie e a common goal (No house, 2021, p. 5):
H3: Complex echnology and inc eased egula ion ha e
a nega i e e ec on he DoD o banks.
The nega i e e ec s o complex echnology and
inc eased egula ion (e.g., Basel III/IV, Banking Ac ) ha e
been iden i ied by se e al s udies ha highligh ed he ech-
nical obs acles o in as uc u e, digi al secu i y, and
inc easing egula ion (Anagnos opoulos, 2018; Haag
e al., 2020; Sa dana & Singhania, 2018; Si oni, 2018):
H4: Employee ci cums ances1 ha e a nega i e e ec on
he DoD o banks.
S udies ha e con i med employee esis ance o o gani-
za ional change, which suppo s he hypo hesized nega i e
496 Business Resea ch Qua e ly 28(2)
e ec o employee ci cums ances on DoD (Fu s & Cable,
2008; S anley e al., 2005; an Dijk & an Dick, 2009;
Zwick, 2002).
Me hodology
Resea ch design and scales used o measu e
ba ie s and digi aliza ion
This s udy conside s he indings o G obe g e al. (2016),
who hema ized digi aliza ion om a scale de elopmen
poin o iew and analyzed i s e ec s on he pe o mance
o new p oduc s and se ices. They de eloped a scale
called Deg ee o Digi aliza ion (DoD) and examined he
ollowing a iables in de ail: digi al p oduc s and se ices,
digi al ope a ions, digi al analy ics, digi al ma ke ing and
sales, and digi al ecosys ems.
This alida ed scale could be used in i s en i e y o
u u e in es iga ions. Howe e , his s udy aimed o exam-
ine decision-make s’ pe cep ions o digi aliza ion and
quan i a i ely assess ba ie s o digi aliza ion. Thus, based
on he li e a u e and con ex ual in e iews, 53 i ems we e
iden i ied as possible ba ie s o he implemen a ion o
digi aliza ion (Diene & Špaček, 2021). They o med he
basis o he EFA, con i ma o y ac o analysis (CFA), and
exogenous measu emen s in he s uc u al equa ion model
(SEM) in o de o conduc hypo hesis es ing. These me h-
ods a e in line wi h me hodological s anda ds.
Since no app op ia e scale o measu ing managemen
pe cep ion o digi aliza ion (in banking) was a ailable, a
scale was de eloped in acco dance wi h esea ch by
Podsako e al. (2012). G obe g e al.’s (2016) indings
seemed o p o ide he mos s able and comp ehensi e
app oach o measu ing DoD. Howe e , due o he ex en-
si e scale, i was no possible o use hei scale sys ema i-
cally, as his could lead o a high d opou a e (Hollenbe g,
2016; Po s , 2011). Thus, only he s onges i ems ha
hey de eloped we e conside ed. Finally, one i em was
added o measu e decision-make s’ gene al pe cep ions
o digi aliza ion wi hin hei o ganiza ion. This i em was
ecommended and alida ed by expe s, as i has no been
conside ed so a in he i em se . Howe e , i seemed
essen ial o de e mining banks’ digi aliza ion and he
scale’s comple eness.
De i a ion and condi ions o s a is ical me hods
Fac o analysis (FA) can be pe o med using a ious s a is-
ical me hods and p og ams o analyze a iable cons uc s.
FAs can gene ally achie e hei pu poses om an explo a-
o y o con i ma o y pe spec i e (Hai e al., 2018, p. 125).
In pa icula , EFA is e ec i e in p elimina y analysis
when a su icien ly de ailed heo y is lacking abou he
ela ionship be ween he a iables and he unde lying con-
s uc s (Ge bing & Ande son, 1988). I can be used o
ac o ize a comple e se o i ems and hen cons uc scales
based on he esul ing ac o loadings. EFA p o ides he
mos in e p e able esul s when educing la ge amoun s o
da a (Loke & Pe due, 1992). Because we wan o measu e
di e en cons uc s o digi aliza ion and i s associa ed ba -
ie s, EFA is an app op ia e echnique o explaining co -
ela ions be ween a numbe o obse ed a iables and a
ew ac o s (Chu chill, 1979; DeVellis, 2016). I aims o
ace many co ela ed, mani es a iables o a small se o
la en a iables ( ac o s) ha cla i y a iance in he ini ial
a iables as a as possible.
Each mani es a iable is a linea combina ion o ac-
o s, whe eby a a iable’s weigh s ands o i s so-called
ac o loading (Bühne , 2010, p. 181). This assumes ha
he alue o a a iable can be addi i ely b oken down in o
a weigh ed sum o ac o s (Klopp, 2010). Since he p esen
s udy aims o ace co ela ions be ween he i ems and
hei la en a iables, p inciple axis ac o analysis (PAF)
was applied (Mabel & Olayemi, 2020; Russell, 2002).
Mo eo e , o a ion was used o op imize he in e p e -
abili y o he a iables h ough high loadings on one ac-
o and low loadings on ano he (Cos ello & Osbo ne,
2005). Due o he unknown na u e o he da a, he lack o
heo e ical assump ions, and he aim o achie ing in e -
p e abili y, se e al o a ions we e applied (Hai e al.,
2018, p. 151). EFA assumes p ima y amewo k condi-
ions essen ial o calcula ing eliable esul s (Weibe &
Mühlhaus, 2014, p. 148). The Kaise –Meye –Olkin c i-
e ion (KMO) and Ba le ’s es we e used o assess he
sampling adequacy o he da a. Bo h p o ide in o ma-
ion abou he cohe ence o he a iables and hei o e -
all i . Kaise and Rice (1974) s a ed ha he alue o
sampling adequacy (measu emen sys ems analysis
(MSA)) measu es should no be unde .60. O he sou ces
sugges ed a h eshold o .50 (Ce ny & Kaise , 1977).
Hai e al. (2018, pp. 152–153) men ioned ha “ ac o
loadings o ±0.30 o ±0.40 a e minimally accep able [.
. .] o be conside ed signi ican ,” while Cos ello and
Osbo ne (2005) s a ed ha only loadings g ea e han
0.30 should be conside ed.
Finally, Yong and Pea ce (2013) no ed ha psychologi-
cal signi icance and in e p e a ion play an impo an ole.
In addi ion, C onbach’s alpha was calcula ed o assess eli-
abili y. The gi en h esholds ange om .70 o .80. Values
close o .60 can also be accep able in explo a o y esea ch
as long as he in e p e abili y o a scale is explained
(Dö ing & Bo z, 2016; Nunnally & Be ns ein, 1994;
Schmi , 1996).
CFA is he me hod o e alua ing cons uc alidi y
(P udon, 2015). In many scena ios, he a iables o in e -
es a e so-called hypo he ical cons uc s (Ma sh e al.,
2013). These a e no di ec ly obse able and a e ope a ion-
alized h ough measu emen models. Such a ac o model
is a s a is ical s a emen abou he ela ionships be ween
he a iables, ha is, he ba ie s o digi aliza ion (Suh ,
Ul ich-Diene e al. 497
2006). CFA assumes ha he la en a iables can be ope -
a ionalized h ough so-called e lec i e measu emen
models (Hai e al., 2018, p. 730).
SEM is based on CFA. I is a mul i a ia e s a is ical
amewo k ha models complex ela ionships be ween
di ec ly and indi ec ly obse ed a iables (Kline, 2015, pp.
9–10). Jos (2014, p. 5) de ined SEM as a me hod ha
explains he ela ionship be ween mul iple a iables in a
hypo he ical cons uc . The pu pose o SEM “[. . .] is o
accoun o a ia ion and co a ia ion o he measu ed a i-
ables” (Suh , 2008, p. 1). The e ec and s eng h o p e i-
ously de i ed la en a iables (i.e., ac ual ba ie s o
digi aliza ion) we e es ed. SEM ocuses on wo issues:
“(1) o e all and ela i e model i as a measu e o accep -
ance o he p oposed heo y and (2) s uc u al pa ame e
es ima es ep esen ing di ec and indi ec ela ionships
wi h on-headed a ows wi hin a pa h diag am” (Hai e al.,
2018, p. 702).
Once he bes model is selec ed, i is isualized as a pa h
diag am ha includes indica o s o la en a iables and
causal pa hways (S ein e al., 2012, p. 510). The pa h dia-
g am o he SEM ep esen s unc ional ela ionships
be ween mul iple eg ession analyses, which in u n ep e-
sen s a special case o SEM (S ein e al., 2012). One mus
di e en ia e be ween he pa h analysis (PA) and he CFA,
which a e bo h pa s o he SEM.
The PA o he model is he p esen a ion o p- alues o
each pa h coe icien and some assessmen o he inal
model’s goodness-o - i (GOF; S ein e al., 2012, p. 510).
Deng e al. (2018) highligh ed he issue o sample size (N)
and i s e ec s on model eliabili y. In addi ion, Weibe and
Mühlhaus (2014, p. 148) summa ized he unde lying con-
di ions ul illed in his s udy.
The o e all model i ep esen s “ he deg ee o which a
pa e n o ixed and ee pa ame e s speci ied in he model,
consis en wi h he pa e n o a iances and co a iances
om a se o obse ed da a” (Suh , 2008, p. 3). The sample
size impac s i indices and many ela i e and non-cen al-
i y indices depend on i . Thus, a la ge sample size is con-
side ed as a be e i .
I is ecommended o use se e al measu es in pa allel
o achie e mo e eliable esul s. The e is s ill a need o
explain whe he o he op ions exis ha could imp o e
he model and why hey we e adop ed o no (Xia &
Yang, 2018). In line wi h a Simms e al. (2019) ecom-
menda ion, a six-poin Like - ype scale was used o all
measu emen s.
sample selec ion and da a collec ion
p ocedu es
Da a collec ion
The Ge man banking ma ke is p ima ily domina ed by
coope a i e and sa ings banks, wi h 62%2 o he o al
banking ma ke in e ms o employees and he la ges
sha e o egional and sup a- egional b anch co e age in
e ail banking (AGVBanken, 2022). Bo h ypes o banks
a e anked equally as good se ice p o ide s, o e ing
almos iden ical p oduc anges o hei cus ome s (Diene
& Špaček, 2021), which is why hey a e in ocus o his
s udy. Quan i a i e da a we e collec ed be ween 15
Sep embe 2020 and 22 Oc obe 2020 ia an online su ey
ha a ge ed decision-make s a sa ings and coope a i e
banks. Fo his pu pose, a web link o access he ques ion-
nai e was sen by e-mail. Since access o decision-make s
is limi ed and challenging, a pa ne ship was es ablished
wi h a polling company called Ques ionP o.3 Ques ionP o
p o ided he pla o m and so wa e equi ed o he su ey
and independen ly collec ed da a based on he su ey
de eloped o his s udy.
In addi ion, we collec ed 6,000 e-mail add esses o
CxO4 employees a andomly selec ed Ge man banks on
he LinkedIn ca ee pla o m. To alida e he add esses and
a oid e u ned e-mails, he QuickEmailVe i ica ion,
Ne e Bounce, and MailTes e ools we e used.
Da a desc ip ion
Based on he a ailable bank da a, 814 coope a i e and 376
sa ings banks (DSGV, 2021) o m he o al popula ion o
his quan i a i e s udy app oach. Hence a o al o 1,190
banks and app oxima ely 336,300 employees, as i can be
assumed ha each ins i u ion has a leas one decision-
make and/o expe . Howe e , he ac ual popula ion o
decision-make s is much mo e p ominen as banks a e no
au ho i a ian-led companies in which decisions a e made
by one pe son alone. F om an objec i e poin o iew, he
highes ope a ional decision-making le el wi hin a bank is
be ween 1 and 10 boa d membe s. Conside ing he o al
numbe o coope a i e and sa ings banks (N = 1,190) and
a possible boa d size o 10 pe sons, i esul s in 11,900
pe sons.
Acco ding o Dillman (2011, pp. 205–210), Dillman
e al. (2014, p. 80), and Salan and Dillman (1994, pp. 54–
58), a popula ion o 10,000, assuming a con idence le el
o 95% and an accep able le el o sampling e o ma gin
±5%, would esul in a ecommended sample size o
N = 370. In o al, 1,233 ecipien s opened he su ey link.
O his numbe , 760 began o ill ou he su ey; his co -
esponded o a a e o 12.7%. Responses o 724 ou o 760
su eys we e di ec ly collec ed ia e-mail, while 36 su -
eys we e collec ed by Ques ionP o ia lead panel.
Subsequen ly, 167 ou o 760 su eys we e excluded due
o incomple eness, as only comple ed su eys could be
conside ed o he analysis. This co esponded o an o e -
all a e o 9.9% o alid su eys.
The da a we e also examined o non-manage ial
esponden s. This le 407 alid su eys (6.8%), which
co esponds o an app op ia e sample size, acco ding o
498 Business Resea ch Qua e ly 28(2)
Dillman e al. O hese, 175 (43%) we e comple ed by pa -
icipan s om sa ings banks a di e en hie a chical le -
els, while 232 (57%) we e comple ed by pa icipan s om
coope a i e banks (Table 1).
Fu he mo e, esponden s we e classi ied acco ding o
hei wo k quali ica ions and da e o bi h. The la e ena-
bled us o ob ain an o e iew o he esponden age s uc-
u es and con i med ha all age g oups we e ep esen ed.
In addi ion, wo k expe ience was ele an o pa icipan s’
pe cep ions o en ep eneu ial issues (Figu e 1). The
inclusion o his c i e ion showed ha he e was an
app op ia e balance be ween esponden expe ience le -
els (Table 2).
Resul s
Pilo es ing
Be o e conduc ing he main s udy, he su ey was sub-
jec ed o a 1-week p e es (Kaya, 2009). To his end, i was
i s es ed o comp ehensibili y by h ee esponden s, all
chie execu i e o ice s a banks. This e ealed a need o
mino changes, a he han a wholesale e o mula ion o
he su ey i ems. Subsequen ly, a second p e es ound was
adminis e ed o a la ge es g oup ia Ques ionP o
(n = 131). Due o he use o an online su ey, no a en ion
could be paid o he es condi ions, as hey could no be
di ec ly in luenced. The esul s showed accep able ou -
comes o loadings and co ela ions. The KMO (0.736)
and Ba le ’s es (chi-squa e: 3,142.139, d : 1,378, sig.
0.000) also p oduced accep able and signi ican esul s.
Only a ew po en ial c oss-loadings we e iden i ied.
Howe e , due o he small sample o he p e es , no adjus -
men s we e made.
EFA
Subsequen ly, an EFA was pe o med in SPSS wi h n = 407.
The da a showed signi ican sui abili y, which was con-
i med by he KMO (0.842) and Ba le ’s es (chi-squa e:
6,098.557, d : 1,378, sig. 0.000). MSA alues we e abo e
he h eshold o 0.50. The commonali ies5 ailed o show
any excessi e abno mali ies, al hough i should be no ed
ha some a iables yielded a low alue, which was also
due o a la ge numbe o a iables. Howe e , since he
esul s showed ha he communali ies we e a he h esh-
old, hese a iables emained included in he s udy.
The esul s o he i s -o de o a ed ac o ma ix iden-
i ied a 15- ac o solu ion. Va imax was applied, as his is
a p o en me hod, and he ac o s we e assumed o be
unco ela ed. Oblimin was also conside ed in o de o
mee scien i ic c i e ia. Since scales should ideally include
a leas h ee i ems (Hai e al., 2018, p. 666), scales wi h
less han wo i ems we e excluded om he analysis. I ems
wi h c oss-loadings6 we e also elimina ed, acco ding o
Hai e al. (2018, pp. 155–156).7
The i s e-speci ica ion se ed as a basis o he u -
he de i a ion o he i ems. The second-o de calcula ions
we e pe o med unde he same condi ions as he i s .
Again, c oss-loadings we e iden i ied, which p o ed no o
be p oblema ic. In he e ision o he EFA esul s, a mo e
p ecise measu emen app oach was ollowed by se ing a
loading h eshold o 0.40.8 Since he a iables PC and PE
we e assigned o he same ac o and showed high load-
ings, dele ing a wo-i em scale was inapp op ia e.
Mo eo e , he in e p e a ion and assignmen o he a ia-
ble cons uc was gi en in e ms o con en . As a esul , a
Table 1. Valid es subjec s.
Managemen Sa ings banks Coope a i e banks ∑
Male Female Male Female
Top 35 7 80 3 125
Middle 95 9 124 13 241
Fi s -line 25 4 11 1 41
∑155 20 215 17 407 Figu e 1. P o essional expe ience o alid es subjec s.
Table 2. Highes quali ica ion o alid es subjec s.
Managemen P o essional aining Unde g adua e s udies Pos g adua e s udies O he s ∑
Male Female Male Female Male Female Male Female
Top 26 – 36 9 43 1 10 – 125
Middle 82 10 70 11 59 – 8 1 241
Fi s -line 17 2 11 3 7 – 1 – 41
∑125 12 117 23 109 1 19 1 407
Ul ich-Diene e al. 505
cus ome and employee in ol emen a e essen ial com-
ponen s o digi al ans o ma ion.
Rega ding H2 and H4, condi ions ela ed o s a egic
co po a e managemen and employees we e no ound o
be signi ican . The e o e, no conclusions abou hei in lu-
ence on DoD and e ec size could be d awn. I does no
mean ha he ole o hese ac o s is no signi ican a all,
bu hey migh need u he e i ica ion and explo a ion
in la ge -sized and longi udinal ongoing s udies in
u u e. Simply said, ou da a do no ind su icien
empi ical suppo , and we may only specula e wha
could be he possible easons. One possible explana ion
o he non-suppo o H2 could be ound in he delay o
he s a egic pe iod, while IT managemen p e e s agile
app oaches o he usual s a egic app oach ( ollowing
di e en wa e all models). H4, on he con a y, could be
due o he speci ici ies o banking o IT jobs, which a e
conside ed e y p es igious, and some job- ela ed “ci -
cums ances” migh no be as in luen ial. These aspec s
equi e u he in es iga ion since bo h seem in ui i ely
impo an o bank digi aliza ion.
Theo e ical pe spec i es
An explo a o y iangula ion app oach was used in his
s udy, which led o he de elopmen o a ques ionnai e on
he opic o digi aliza ion and ba ie s o i s implemen a-
ion in banking. Se e al i em se s we e de eloped using
EFA, o ming in e p e i e scales wi h high in e nal con-
sis ency o ac o s ha in luence DoD banks. In pa icu-
la , he s uc u e o an exis ing esea ch scale (DoD:
46-i em scale; G obe g e al., 2016) was op imized o ena-
ble measu emen wi h only six i ems.
In addi ion, he i s s uc u al equa ion model was con-
s uc ed in his ega d. I led o some ambiguous no ions o
digi aliza ion in banking and con i med, among o he
hings, PI’s posi i e e ec on banks’ DoD (Blocke e al.,
2011; Ce indama Kozanoglu & Abedin, 2021; on Leipzig
e al., 2017; Wa ne & Wäge , 2019). In addi ion, he ind-
ings o Anagnos opoulos (2018), Sa dana and Singhania
(2018), and Si oni (2018) we e suppo ed, which high-
ligh s ha complex echnology and inc eased egula ion
ha e a nega i e e ec on DoD.
In gene al, he model e ealed he in luence o he ou
independen ac o s and p o ided a clea and pe suasi e
se o dependencies among a iables and ac o s, which
en iched he indings o Diene and Špaček (2021) and
c ea ed comp ehensi e scales o he i s ime. No ably,
he ep esen a ion o echnology and egula ion in a single
a iable highligh s hei close ela ionship and could be
gene ally e e ed o as “inc easing complexi y” in he
u u e. Fu he mo e, he model illus a es he di ec ions
and in ensi y o in e dependencies o a iables. This p o-
ides u he suppo o e ec i e esea ch on digi al ans-
o ma ion. The e o e, he indings p o ide a ounda ion
o s udies in he banking sec o and call o using he
es ed measu es and ob ained esul s in u u e esea ch.
Limi a ions and u he esea ch
Some limi a ions in his s udy de i e om he cons ain s
o he esea ch me hods used. Fo ins ance, ins ead o a
pos ula ed heo y-based app oach, as in he p esen s udy,
deduc i e ca ego y building migh be he be e app oach
o a oid e o s in ca ego y o ma ion and subsequen
esea ch s eps. Fu he mo e, he Diene and Špaček (2021)
pa aph asing and summa izing app oach mus be c i ically
iewed. This small-scale app oach, inspi ed by he psy-
chology o wo d p ocessing, may lead o he o ma ion o
ca ego ies ha ha e li le o no hing o do wi h each o he
and do no co espond o he opic o in e es (May ing,
2015, pp. 88–89).
Fu he mo e, he a ailable da a limi ed he s udy, which
posed an addi ional challenge due o he na ow ocus on
decision-make s. O he limi a ions include widely known
weaknesses in selec ing he numbe o ac o s in FA, he
me hod o analysis, and he in e p e a ion o ac o s due o
subjec i e decisions on he pa o he esea che (Bache
& Wol , 2010, p. 360). Simila ly, SEM’s h eshold-based
op imiza ion po en ial was conside ed based on GOF
a ios, ye his is con o e sial among expe s. Thus, i can
be seen as a s udy limi a ion. Howe e , in he SEM, hese
h esholds we e aligned wi h ecommenda ions om he
li e a u e, which mi iga ed he limi a ions o he e alua ion
and p oduced obus esul s.
In e ms o u he esea ch, he me hodology o i s
applying EFA o iden i y ac o s and es ing he e ec s o
ac o s in SEM pa es he way no only o u he s udies
in banking, bu also o o he segmen s. Due o he s ill
e y young ield o ba ie esea ch, o he ac o s ha may
in luence he DoD o banks should be explo ed in mo e
de ail. Pa icula a en ion should be paid o sub-ba ie s
whose e ec may be subs an ial. Di e en ia ion be ween
indi idual banks and coun ies could p o ide insigh ul
esul s o o e coming ba ie s in u u e.
The SEM model de eloped o his s udy can se e as a
poin o depa u e o o he esea ch ha ex ends he
explo a ion o his opic o o he aspec s (e.g., go e nmen
s imuli, c oss-sec o al impac s, o in e e ence in he digi-
aliza ion p ocess by o he s akeholde s). Fu u e s udies
could also d aw a en ion o he impac o o he , mo e
socio-cul u al managemen aspec s, such as mo i a ion,
leade ship, co po a e cul u e, and managemen s yles, o
he easibili y o bank digi aliza ion, which, howe e , ha e
no been he ocus o scien i ic s udies hus a .
Implemen ing longi udinal esea ch design, s udying
di e en ypes o banks in di e en coun ies wi h, o
example, e en di e en inancial sys ems and ways hey
cope wi h digi aliza ion challenges would also en ich he
cu en s a e o knowledge. No ably, i would also add ess

506 Business Resea ch Qua e ly 28(2)
some o he limi a ions o he s udy, such as one coun y-
ocus, no ully ep esen a i e sampling, as well as su e -
ing o some ex en om he non esponse bias issue.
Conclusion
This a icle add esses he opical issue o ba ie s o bank
digi aliza ion, which a e common ac oss banking sec o s
in de eloped Eu opean Union (EU) coun ies. I ackles
he se e e p oblem o iden i ying ba ie s o digi aliza ion,
which a e closely ied o he digi aliza ion p ocess a i u-
ally all banks.
The e is a need o de elop digi al business models o
ensu e ha ins i u ions emain compe i i e in a highly
challenging and compe i i e en i onmen . FinTechs ha e
he po en ial o accele a e he deli e y o inancial se ices
and pe sonalize hem o mee cus ome s’ needs. Mo eo e ,
hey p o ide cus ome s wi h mul iple channels, which
allow hem o choose he mos app op ia e solu ions o
hei si ua ions. Ul ima ely, hese cus omizable inancial
se ices a e cheape o clien s.
The ac ha ins i u ions canno keep up wi h his digi-
al de elopmen and iden i y ba ie s o digi aliza ion c e-
a es p oblems o he en i e banking sec o and he p ocess
o bank digi aliza ion i sel . The cu en scien i ic and p o-
essional li e a u e does no ully e lec he u gency o
inding a solu ion o his p oblem.
No wi hs anding ongoing e o s o iden i y ba ie s o
digi aliza ion, banks o en lag in comple ing digi aliza ion
p ocesses. As a esul , hey a e a high isk o being ou -
compe ed and dis up ing hei business. The e o e, his
esea ch b idges he gap be ween po en ially uniden i ied
ba ie s o digi aliza ion and banks’ ac ual deg ee o digi-
aliza ion. I also iden i ies he mos ele an ba ie s o
digi aliza ion ha may in luence he e ec i eness o he
digi aliza ion p ocess.
As a poin o depa u e, we de ined he p ima y goal o
his a icle as he iden i ica ion and analysis o ba ie s o
digi aliza ion in he con ex o he banking sec o om he
pe spec i e o decision-make s. This goal was suppo ed
by o mula ing wo RQs, b inging addi ional cla i y o he
analysis p ocess. The indings ob ained h ough EFA ena-
bled he de elopmen o ou hypo heses ha we e subse-
quen ly e i ied h ough he SEM app oach.
Due o he b oad scope o his opic, he s udy was lim-
i ed o he Ge man banking sec o . I s o ien a ion was
d i en no only by his sec o ’s high le el o ma u i y bu
also by he easonable ans e abili y o he esul s o o he
EU and non-EU coun ies.
The cu en s udy builds on p e ious quali a i e
esea ch ha e ealed as many as 53 ba ie s o digi aliza-
ion, which c ea ed a basis o subsequen quan i a i e
analysis h ough EFA and he de elopmen o SEM o
desc ibe mu ual dependencies among indi idual ac o s
and p o ide he s a is ical ele ance o hese dependencies.
The p ima y sample collec ed in 2020 encompassed da a
om 407 Ge man sa ings and coope a i e banks.
These indings con ibu e signi ican ly o he knowl-
edge o digi al ans o ma ion in banking p ocesses and
ba ie s o digi aliza ion. In addi ion, i p o ides bank
manage s and ans o ma ion expe s in inancial se ices
wi h a se o po en ial obs acles o digi aliza ion, which a e
si ua ed in he con ex o he digi al ans o ma ion p ocess
a banks. By conside ing he po en ial ba ie s iden i ied in
his esea ch, manage s may become mo e awa e o obs a-
cles o he digi aliza ion p ocess and moni o hem mo e
e icien ly.
Decla a ion o con lic ing in e es s
The au ho (s) decla ed no po en ial con lic s o in e es wi h espec
o he esea ch, au ho ship, and/o publica ion o his a icle.
Funding
The au ho (s) disclosed eceip o he ollowing inancial suppo
o he esea ch, au ho ship, and/o publica ion o his a icle: I
was suppo ed by he In e nal G an Agency o he Facul y o
Business Adminis a ion, P ague Uni e si y o Economics and
Business, unde No. IGS F3/14/2020.
ORCID iDs
Flo ian Ul ich-Diene h ps://o cid.o g/0000-0002-7505-3305
Ondřej D oule ý h ps://o cid.o g/0000-0001-9151-2033
no es
1. “Ci cums ances” a e unde s ood as he o ali y o condi ions
(e.g., lexibili y, age s uc u e, quali ica ion, e c.) ha a ise
in o due o employees.
2. Coope a i e banks 26%; Sa ings banks 36%.
3. Online: h ps://www.ques ionp o.de/.
4. CxO: C-le el posi ions, some imes also called “C-Sui e,”
a e he op managemen le el o a company. The le e s C
and O in his con ex s and o “Chie ” and “O ice ,” as in
CEO/Chie Execu i e O ice , CFO/Chie Financial O ice .
I can also include managemen a lowe le els.
5. Because o i s ex ensi e layou , i canno be p esen ed, bu
is a ailable upon eques om he au ho s.
6. “C oss-loadings o a iables (loadings o wo ac o s) can
be e alua ed by he a io o hei squa ed loading and clas-
si ied as p oblema ic ( a io be ween 1.0 and 1.5), po en ial
( a io be ween 1.5 and 2.0), o igno able ( a io g ea e han
2.0). P oblema ic and pe haps e en po en ial c oss-loadings
a e dele ed unless heo e ically jus i ied o he objec i e is
s ic ly da a educ ion” (Hai e al., 2018, p. 158).
7. The i ems SMDMP, TROIT, and SMDS we e elimina ed.
8. The i ems EF , CT, TRU, CK, KPC, Cas, and KPEEA we e
elimina ed.
9. The pa e n ma ix: ac o loadings ep esen eg ession
coe icien s.
10. The s uc u al ma ix: co ela ions be ween he a iables
and he ac o s.
Ul ich-Diene e al. 507
11. Wha a e he ba ie s o digi aliza ion in an inc easingly
echnological banking en i onmen ?
12. Wha e ec do ba ie s o digi aliza ion ha e on he
deg ee o digi aliza ion o banks om he pe spec i e o
decision-make s?
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Appendix 1. Es ima ed s uc u al equa ion model ( ull model).