In e na ional Jou nal o Social Science and Human Resea ch
ISSN (p in ): 2644-0679, ISSN (online): 2644-0695
Volume 08 Issue 10 Oc obe 2025
DOI: 10.47191/ijssh / 8-i10-24, Impac ac o - 8.007
Page No: 7746-7759
IJSSHR, Volume 08 Issue 10 Oc obe 2025 www.ijssh .in Page 7746
The In luence o Online T ansac ions on Cus ome s Deposi and Ope a ing
Expenses o License Deposi Money Banks (DMBs) In Nige ia
Ma y O ayioza Ezeji1, Nasi u Abdulsalam Kao’je2, Yakubu Shaba3, Mohammed Auwal Babangida4
1,2,3Depa men o Accoun ing, Facul y o Managemen Sciences, Usmanu Dan odiyo Uni e si y, Soko o.
4Depa men o Taxa ion, Facul y o Managemen Sciences, Fede al Uni e si y Du se, Jigawa S a e.
ABSTRACT: Online ansac ions policy has gained signi ican ac ion o e he yea s, wi h he inc easing accep abili y o elec onic
ansac ion channels such as Global Pay Sys em (GPS), Co po a e In e ne Banking (CIB), Mobile Banking Sys em (MBS), Pay-
Di ec Sys em (PDS), and Remi a (RMT). The e o e, his s udy examines he in luence o online ansac ions policy on cus ome s'
deposi s and bank ope a ing expenses in Nige ia. Using mixed me hod sou ces o da a and ex pos ac o esea ch design, he SEM-
PLS models e eal ha CIB (p = 0.000), GPS (p = 0.000), MBS (p = 0.029), and PDS (p = 0.000) s ongly in luence cus ome s'
deposi s and ope a ing expenses o DMBs in Nige ia. Howe e , he panel eg ession models indica e ha online ansac ion sys ems
ha e no signi ican in luence on cus ome s' deposi s, and only GPS, wi h a p- alue o 0.021 and MBS, wi h a p- alue o 0.095,
inc eases ope a ing expenses o he sampled DMBs. The s udy he e o e sugges s ha imp o ed online banking acili ies, use
educa ion, and policy alignmen a e equi ed o achie e he ull bene i o online ansac ions policies in Nige ia. The s udy u he
ecommends ha DMBs should a oid a one-size- i s-all digi al s a egy. Ins ead, hey should ailo digi al ools o speci ic
pe o mance objec i es such as deposi mobilisa ion, cos e iciency, and p o i abili y.
KEYWORDS: Online ansac ions, Cus ome deposi s, Ope a ing Expenses, Global Pay Sys em, In e ne Banking, Pay Di ec
Sys em.
1. INTRODUCTION
The Cen al Bank o Nige ia (CBN) launched he cashless banking (Online ansac ions) policy in 2012. On Janua y 1, 2012, a
cashless sys em policy es un began in Lagos s a e. The second s age o his s a egy was implemen ed in Abia, Anamb a, Kano,
Ogun, Ri e s, and he FCT on July 1, 2013, wi h s a ewide implemen a ion on July 31, 2014 (CBN, 2019). This sys em is no
in ended o elimina e cash usage in ansac ions, bu a he o limi physical cash handling and he amoun o cu ency in ci cula ion
(Gbanado , 2021). The online banking sys em aims o inc ease he usage o elec onic paymen channels such as au oma ed elle
machines (ATM), poin o sale (POS), mobile banking sys em (MBS), NIBSS ins an paymen , NIBSS elec onic und ans e , and
o he al e na i e paymen channels. The o e all impac o he cashless banking sys em on he pe o mance o DMBs is complex and
mul i ace ed (Oka o , 2020).
Nige ia's online ansac ions (cashless banking) policy signi ican ly imp o es business g ow h and GDP. Howe e , ce ain
disad an ages exis , such as inadequa e in o ma ion sys ems acili ies, he possibili y o aud, high cos o IT acili ies, high cos
o unning a business, and he pe cep ion o audulen ac i i ies linked o he cashless banking sys em. Nige ia has seen se e al
cybe c imes and auds o e he yea s due o he cashless banking sys em's emphasis on elec onic money ansac ions and he
coun y's high deg ee o un eliable ne wo ks (Ogbeide & Fapohunda, 2017).
S udies ha e been conduc ed on he in luence o online ansac ions policy and he pe o mance o DMBs (Makinwa, 2021;
Okechukwu & Yua, 2021; Akani & Obiosa, 2020; Usman, 2020; Akindayo, Shade a & Solomon, 2020; Gambo, 2020; Nwakoby,
Chukwu & Ogbene ega, 2020; Ul-Hug & Hossain, 2020; James & Eloho, 2020; Agu & Agu, 2020; Oka o , 2020; Igno oje &
Oko oyibo, 2020; Ogu u & Fa oki, 2019; Agu & Nwakwo, 2019). These s udies ha e been cha ac e ised by inconclusi e indings
anging om posi i e o nega i e and non-signi ican ela ionships.
I has been obse ed ha mos o he s udies ha e used a iables such as ATM, Mobile Banking Sys em (MBS), POS, CIB, and
Elec onic Fund T ans e (Akindayo, Shade a & Solomon, 2020; Gambo, 2020; Nwakoby, Chukwu & Ogbene ega, 2020; Ul-Hug
& Hossain, 2020; Agu & Agu, 2020; Oka o , 2020) as p oxies o online ansac ions policy. The e o e, his esea ch ex ends he
knowledge gap by add essing he in luence o online ansac ions on cus ome s' deposi s and banks' ope a ing expenses in Nige ia.
The pe iod co e ed by mos o he s udies e iewed (Akani & Obiosa, 2020; Usman, 2020; Agu & Agu, 2020; Oka o , 2020;
Igno oje & Oko oyibo, 2020; Ogu u & Fa oki, 2019) ends a he 2021 go e nmen calenda yea , indica ing ha ac i i ies o he
The In luence o Online T ansac ions on Cus ome s Deposi and Ope a ing Expenses o License Deposi Money Banks
(DMBs) In Nige ia
IJSSHR, Volume 08 Issue 10 Oc obe 2025 www.ijssh .in Page 7747
yea s 2022, 2023, and 2024 emained unco e ed. I should be no ed ha 2020, 2021, 2022, and pa o 2023 we e cha ac e ised by
he global pandemic, which saw he es ic ion o banks' ope a ions emana ing om he social dis ancing and also cash c ises in
2022 and 2023; hence he use ulness o he a ious cashless sys em pla o ms by he populace in Nige ia and a ound he wo ld.
The e o e, based on he o egoing, his esea ch wo k s udies he in luence o online bank ansac ion sys ems on cus ome s' deposi s
and banks' ope a ing expenses in Nige ia, co e ing 13 yea s om 2012 o 2024.
2. CONCEPTUAL FRAMEWORK
2.1 Concep o Online T ansac ions Policy
The Online T ansac ions Policy (OTP) e e s o he conduc o inancial ansac ions wi hou he use o physical cu ency, le e aging
ins ead c edi and debi ca ds, elephonic and elec onic und ans e s, he in e ne , and mobile banking. Cashless ansac ions occu
using ATM, debi o c edi ca ds, EFTs, o o he media ha do no equi e ac ual cash. Cashless banking is a sys em ha aims o
dec ease he ac ual cu ency in ci cula ion, wi hou elimina ing i , by p omo ing elec onic ansac ions o paymen s, ans e s, and
o he ac i i ies. (Kamboh & Legha i, 2016; Da a, 2021; E nes & Fadiya, 2022). I signi ies a ansi ion pe iod in he ad ancemen
o paymen sys ems in mos de eloping na ions (Ikpe an, 2018). A cashless economy is he in e media e s age o a h ee-phase
paymen sys em economic model. This sugges s ha na ions, pa icula ly hose in he de elopmen p ocess, would shi om a
mos ly cash-based economic sys em o a cashless economy (Igho oje & Oko oyibo, 2020).
2.2 Bene i s o Online T ansac ions.
The p edic ed pa onage o online banking ansac ions sys em is expec ed o p o ide many ad an ages o a ious s akeholde s.
This p o ides use s wi h mo e con enience, a b oade a ay o se ice op ions, less ulne abili y o cash- ela ed c imes, and mo e
economical access o banking se ices and c edi beyond con en ional b anches. Businesses may bene i om imp o ed
accessibili y, educed e enue loss, and dec eased expenses associa ed wi h cash handling (A anda & Alimi, 2018); enhanced ax
e enue, mo e access, and imp o ed economic ad ancemen o he go e nmen . A cashless banking sys em would enhance he
s anda d o li ing by lowe ing he du a ion o ansac ions, boos ing sales, simpli ying cash collec ion, minimising ans e and
p ocessing cos s, imp o ing p ocessing and ansac ion ime, o e ing se e al paymen op ions, and deli e ing ins an no i ica ions
o all clien accoun ope a ions (Akhalumeh & Ohiakha, 2012; Andabai & Bina, 2019; Asenge, 2019).
2.3 The Challenges o he Online T ansac ions.
The challenge o online ansac ions is ha banks a e ulne able o a ious isks, such as ansac ional, s a egic, epu a ional, and
o eign cu ency h ea s. Lack o app op ia e ope a ional in as uc u e, inadequa e in e ne amewo ks, and high expenses
associa ed wi h acqui ing and main aining hem some imes p e en a signi ican po ion o he popula ion om using hem, e en in
a eas whe e hey a e a ailable (Chison & Mike, 2018).
2.4 Cus ome Deposi
Cus ome deposi s e e o he money banks hold on hei cus ome s' behal . These deposi s a e an essen ial elemen o a bank's
obliga ions and se e as he ounda ion o he bank's lending ope a ions. Va ious ca ego ies o deposi s include sa ings, cu en ,
ixed deposi s, among o he deposi s accoun s. De i ing ce ain pi o al indica o s on clien deposi s migh p o ide aluable insigh s
in o a bank's pe o mance and inancial well-being.
2.5 Ope a ing Expenses
Ope a ing expendi u es in DMBs e e o he a ious cos s o managing he bank's daily ope a ions. These expendi u es a e essen ial
o con inuing banking se ices and main aining a compe i i e posi ion in he inancial ma ke . Typical ope a ional expendi u es in
DMBs include compensa ion o bank wo ke s, including sala ies, wages, bonuses, and pe ks, which cons i u e a subs an ial
p opo ion o ope a ional cos s. Expenses associa ed wi h en ing o leasing o ice spaces, b anches, and o he acili ies a e pa o
he ope a ional cos s o DMBs. Ope a ing expenses include he expendi u es ela ed o u ili ies, such as powe , wa e , and hea ing
o cooling sys ems, a bank b anches and o ices.
Expenses ela ed o he upkeep and enhancemen o banking sys ems, so wa e licensing, and echnical in as uc u e a e c ucial
elemen s o ope a ional cos s. Ope a ing expendi u es a e inc eased by he cos s incu ed in main aining secu i y measu es, which
include s a , su eillance sys ems, and secu i y echnology. DMBs may be subjec o cha ges o p o essional se ices such as legal
counsel, consul ancy, and audi ing, con ibu ing o ope a ional cos s. The ou ine upkeep and necessa y ixes o angible asse s, such
as ATMs, buildings, and equipmen , add o he ope a ional expenses o banks.
2.6 Empi ical Re iew and Hypo heses De elopmen
A comp ehensi e in es iga ion has been ca ied ou on he nexus be ween he cashless banking sys em and he g ow h o DMBs in
Nige ia. Howe e , he e is a signi ican lack o esea ch ha combines se e al elemen s o measu e pe o mance. Examples,
Makinwa (2021), Okechukwu and Yua (2021), Akani and Obiosa (2020), Usman (2020), Akindayo, Shade a, and Solomon (2020),
The In luence o Online T ansac ions on Cus ome s Deposi and Ope a ing Expenses o License Deposi Money Banks
(DMBs) In Nige ia
IJSSHR, Volume 08 Issue 10 Oc obe 2025 www.ijssh .in Page 7748
Gambo (2020), Nwakoby, Chukwu, and Ogbene ega (2020), Ul-Hug and Hossain (2020), James and Eloho (2020), analyse he
impac o a cashless policy on DMBs pe o mance in Nige ia. Adu and Williams (2022) in es iga es he e ec s o cashless policies
on he p o i abili y o Nige ia DMBs. The objec i es encompass examining he in luence o ATM, NEFT, POS, and e-banking
ansac ions. The esea ch indica ed ha ATM, NIP, mobile banking, and cheque ansac ions signi ican ly in luenced he
pe o mance o he sampled DMBs. The epo ecommended banks o und aining o echnical pe sonnel o e seas o acqui e
knowledge and s ay cu en wi h new and inno a i e echnology u ilised in he banking indus y. Adamu and Adao a (2023) examine
he e ec o inclusion ini ia i es on he pe o mance o Nige ia's banking indus y. Using an ex-pos ac o echnique, he OLS model
e eals ha POS and ATM subs an ially impac sys em's e iciency and e ec i eness. The esea ch ecommends, among o he
hings, ha he CBN should educe elec onic paymen ansac ion a es o p omo e b oade inclusion.
Cha i y, Vic o ia, Chima, and Udeoba (2024) explo e he impac o ATMs and mobile banking on he pe o mance o Nige ian
DMBs. The analysis used da a om he CBN Bulle in om 2009 o 2021. The indings indica es ha ATMs ha e a signi ican
impac on he pe o mance o Nige ian DMBs. The s udy concluded ha e-banking has enhanced banking access o use s while
also allowing banks o ex end hei ope a ions o each mo e people. Ajuonu and Uzodike (2024) in es iga e he impac o he
Cashless Policy on he Accoun ing Pe o mance o Some Selec ed DMBs in Nige ia. The ex-pos ac o esea ch design was used,
and he da a we e analysed using mul iple eg ession analysis. The s udy disco e ed ha ATM ansac ions subs an ially a ec ed
he ROE o DMBs in Nige ia. Howe e , POS ansac ions signi ican ly a ec he NPM o selec ed DMBs. The s udy concluded ha
he olume o ATM and E-banking ansac ions subs an ially impac s Nige ian DMBs' e enues, as does he olume o POS
ansac ions on clien deposi s. P io esea ch, including Agu and Agu (2020), Oka o (2020), Igno oje and Oko oyibo (2020), Ogu u
and Fa oki (2019), Agu and Nwakwo (2019), I ah and Ene (2014), and Abaenewe, Ogbulu, and Ndugbu (2013), has equen ly
employed me ics such as ROE, ROA, EPS, and NPAT. Ne e heless, he e exis inconsis encies in hei indings. Owing o hese
con o e sies in hei indings, he s udy hypo hesised ha :
H01: Online ansac ions policy does no signi ican ly impac cus ome s' deposi s o lis ed Deposi Money Banks (DMBs) in
Nige ia.
H02: Online ansac ions policy does no signi ican ly impac he ope a ing expenses o he lis ed Deposi Money Banks (DMBs)
in Nige ia.
3. RESEARCH METHOD
3.1 Resea ch Design
The s udy used a mixed-me hods esea ch s a egy, using an ex pos ac o app oach. The ex pos ac o esea ch design, o a
e ospec i e o non-expe imen al esea ch design, is an obse a ional s udy whe e he esea che examines he ela ionship be ween
a iables wi hou ac i ely manipula ing hem. The s udy popula ion comp ises nine een (19) lis ed comme cial banks classi ied in o
na ional and in e na ional licensed banks on he Nige ian Exchange G oup (NGX) as o 2021. Census sampling echniques we e
employed since he en i e popula ion was s udied.
3.2 Sou ces o Da a
Da a was sou ced ia s uc u ed/closed-ended ques ionnai e and annual accoun s and epo s o he sampled i ms. A i e-poin
Like - ype scale was used o seek a esponse a e and educe esponden s’ us a ion le el, as no ed by Ponden (2017). In addi ion,
a s uc u ed ques ion is mos ly ecommended due o he desi e o a oid a lacklus e esponse, which could esul in common me hod
bias (Dillman, Smy h & Ch is ian, 2014). The da a was analysed wi h he aid o SEM-PLS and panel eg ession echniques. The
s udy co e s 12 yea s, spanning om 2012 o 2023. The jus i ica ion o selec ing his ime ame is based on he ac ha he
cashless sys em was in oduced on Janua y 1 2012, and since hen, i has occupied a majo policy applicable in he Nige ian banking
sec o .
3.3 Me hods o Da a Analysis
The s udy u ilises bo h in e en ial and desc ip i e s a is ics o da a analysis. The eg ession analysis was employed o analyse he
quan i a i e da a and u he show he signi icance le el o he a iables, om which a s ance was made on he ailu e o ejec o
ejec he ea lie o mula ed hypo heses. In addi ion, he s udy used a s uc u al equa ion model, speci ically Sma -PLS, o analyse
he quali a i e da a and asce ain he esea ch ins umen 's alidi y and eliabili y. These we e unde aken a e p elimina y analyses,
including missing alues, non esponse bias, common me hod bias, de ec ion o ou lie s, and no mali y es s using SPSS e sion 27.
3.4 Ins umen s o Da a Analysis
The ins umen s used in his s udy we e adop ed om pas s udies, wi h mino changes o sui he s udy. Fi m pe o mance was
measu ed using ope a ing expenses and cus ome deposi s adop ed om Spillan and Pa nell (2006). The online ansac ions we e
measu ed using he adop ed 7-i em Adamson Uni e si y Su ey Ques ionnai e on Cashless Socie y (2018). A sample o a ques ion
The In luence o Online T ansac ions on Cus ome s Deposi and Ope a ing Expenses o License Deposi Money Banks
(DMBs) In Nige ia
IJSSHR, Volume 08 Issue 10 Oc obe 2025 www.ijssh .in Page 7749
is “Online ansac ions a e much mo e con enien ". The epo ed C onbach's alpha is 0.798, showing he ins umen is eliable and
adequa e o he s udy.
3.5 Model Speci ica ion
The panel eg ession model o es he hypo heses was adop ed om he s udy o Chikwemme and Nwadialo (2019). The models
we e speci ied as ollows:
FP = OT…………………………………………………..…………………………………….………………… (i)
Whe e he equa ion p o ided eads hus:
FP = β0+β1CIBi +β2MBSi +β3GPSi +β4PDSi +β5RMTi +Ɛi ………………………….…….(ii)
OE = β0 + β1CIBi + β2MBSi + β3GPSi + β4PDSi + β5RMTi + Ɛi ….........................(iii)
CD = β0 + β1CIBi + β2MBSi + β3GPSi + β4PDSi + β5RMTi + Ɛi ………….…….........(i )
Whe e:
OT = Online T ansac ions
GPS = Global Pay Sys em
CIB = Co po a e In e ne Banking
MBS = Mobile Banking Sys em
PDS = Pay Di ec Sys em
RMT= Remi a
FP = Financial Pe o mance
CD = Cus ome Deposi
OE = Ope a ing Expenses
β1 - β5 = The pa ame e s o be es ima ed
i = Rep esen s indi idual i ms
= S ands o ime pe iod
ε = The e o e m o he model
3.6 Va iables Desc ip ion and Measu emen
This sub-sec ion desc ibes he s udy's a ious a iables, which a e made up o bo h dependen and independen a iables, and how
hey a e measu ed (p oxies) as p esen ed in Table 1.
Table 1: Va iables’ Desc ip ion and Measu emen
Va iables
Measu emen
P io S udies
Dependen Va iables = Fi ms’ Pe o mance
Cus ome s' Deposi (CD)
Log o o al ope a ing expenses o sampled
DMBs
Hussein and Elyjoy (2018), Eminah
(2022)
Ope a ing Expenses (OE)
Log o cus ome s' deposi s
Emenike (2020)
Independen Va iables: Cashless Sys em
Global Pay Sys em (GPS)
To al olume o global pay ansac ions
o DMBs
Ve y ew s udies ha e employed his
a iable o measu e he cashless sys em.
Co po a e In e ne Banking
(CIB)
To al olume o co po a e in e ne
banking ansac ions o DMBs
Ihpe an e al. (2018), Mus apha (2018),
Oyomo (2018), and Oka o (2020).
Mobile Banking Sys em (MBS)
To al olume o mobile banking
ansac ions o DMBs
Ihpe an e al. (2018), Mus apha (2018),
and Oyomo (2018).
Pay Di ec Sys em (PDS)
To al olume o di ec pay ansac ions o
DMBs
Ene (2020)
Remi a (RMT)
To al olume o Remi a ansac ions o
DMBs
Ene (2020)
Sou ce: Au ho ’s Compila ion (2024).
4. Da a P esen a ion and Analysis
This sub-sec ion mi o s da a collec ed by adminis e ing ques ionnai es o esponden s, and he compu a ion is p o ided in Table 2.
The In luence o Online T ansac ions on Cus ome s Deposi and Ope a ing Expenses o License Deposi Money Banks
(DMBs) In Nige ia
IJSSHR, Volume 08 Issue 10 Oc obe 2025 www.ijssh .in Page 7750
Table 2: Ques ionnai e Dis ibu ion and Response Ra e
Sou ce: Field Wo k (2024).
The dis ibu ion and esponse a e o he ques ionnai es o he s udy comp ised 422 ques ionnai es, 417 o which (98.8%) we e
e u ned. Among he e u ned ques ionnai es, 15(3.6%) we e ejec ed o emo ed. Consequen ly, 402(95.2%) we e e ained and
deemed usable o analysis. This high esponse a e and e en ion o usable ques ionnai es indica e a obus sample size, enhancing
he eliabili y and alidi y o he s udy's indings. This is in andem wi h Ryu (2020) and Ojeleye, Abu-Abdissamad, Uma , and
Usman (2022) asse ha a consensus on a minimum accep able esponse a e is lacking. Malho a and G o e sugges ed ha a 50%
esponse a e is su icien o da a p ocessing and p esen a ion.
4.1 Analysis o Missing Values
Table 3 p esen s he analysis o he missing alues.
Table 3: Compu a ion o Missing Values
La en Va iables
No. o Missing Values
Global Paymen Sys em
6
Co po a e In e ne Banking
5
Mobile Banking Sys em
7
Pay Di ec Sys em
5
Remi a
3
Financial Pe o mance
8
To al
34 ou o 12,060 da a poin s
Pe cen age o missing alues: 0.28%
Sou ce: Resea che ’s Compila ion (2024).
No e: The missing alue pe cen age is de i ed by di iding he numbe o andomly missing alues o he en i e da a se by he o al numbe o da a poin s mul iplied by 100.
Table 3 shows ha only 34(0.28%) da a poin s om he ini ial da a se we e andomly missing. Howe e , no "golden ule" exis s
o an accep able pe cen age o missing alues o alid s a is ical in e ence in a da a se (Aliyu, 2020). Resea che s ha e es ablished
ha missing da a o less han 5% o he sample and 10% o he a iables a e gene ally accep able and ag eeable o any impu a ion
s a egy (Hai e al., 2014). Since he missing da a was less han 10%, he mean subs i u ion impu a ion app oach eplaced he
missing da a. Fu he mo e, expe s belie e ha mean subs i u ion is he simples echnique o eplacing missing da a wi h a
p obabili y o less han 5% (Tabachnick & Fidell, 2012).
4.2 No mali y Tes
S udies (We zels, Odeke ken-Sch öde , & Van Oppen, 2009; along wi h Reina z, Haenlein, & Hensele , 2009) ha e es ablished ha
SEM-PLS unc ions e ec i ely wi h non-no mally dis ibu ed da a. Simila ly, Hai e al. (2019) posi ed ha highly skewed da a
ele a es boo s ap s anda d e o , pe haps esul ing in an unde es ima ion o he s a is ical signi icance o ou e coe icien s.
The e o e, his s udy employed mul i a ia e no mali y o analyse he da a dis ibu ion by u ilising Ku osis and Skewness o assess
no malcy. Table 4 p esen s he a ings o Skewness and Ku osis o he esea ch a iables.
Table 4: No mali y Tes : Skewness and Ku osis S a is ics (n=412)
N S a .
Min.
Max.
Mean
S d. De
Skewness
S d. E o
Ku osis
S d. E o
CIB
402
1.235
5.000
3.743
0.484
1.031
0.122
0.332
0.244
GPS
402
1.342
5.000
3.943
0.348
0.238
0.122
-1.363
0.244
MBS
402
0.987
4.958
3.854
0.389
0.680
0.122
-0.520
0.244
PDS
402
1.123
4.896
3.847
0.386
0.398
0.122
-1.014
0.244
FP
402
1.286
5.000
2.765
0.904
0.702
0.122
-0.725
0.244
RMT
402
1.243
5.000
2.515
0.612
0.322
0.122
-0.459
0.244
Sou ce: SPSS Ve sion 27, (2024).
Ques ionnai e
F equency
Ra e%
Dis ibu ed
422
100
Un e u ned
5
1.2
Re u ned
417
98.8
Rejec ed
15
3.6
Re ained
402
95.2
The In luence o Online T ansac ions on Cus ome s Deposi and Ope a ing Expenses o License Deposi Money Banks
(DMBs) In Nige ia
IJSSHR, Volume 08 Issue 10 Oc obe 2025 www.ijssh .in Page 7751
Based on he analysis in Table 4, i is de e mined ha he da a is no mal because he absolu e alues o Skewness and Ku osis o
all i ems in his esea ch a e wi hin he pe mi ed anges o < 2 and < 7, espec i ely. Skewness le els o less han 2 and Ku osis
alues o ewe han 7 a e ad oca ed (Cu an & Blackbu n, 2001). Kline (2016) con ends ha absolu e Skewness alues exceeding
3 and Ku osis alues su passing 10 may indica e a conce n, howe e alues o e 20 may deno e a mo e se ious issue.
4.3 Mul icollinea i y Tes
Two (2) echniques a e used in his wo k o de ec mul icollinea i y. The co ela ion ma ix o he exogenous la en cons uc s
unc ioned as he ini ial app oach. A co ela ion coe icien o 0.90 o abo e signi ies mul icollinea i y among exogenous la en
a iables (Hai e al., 2019). Table 5 demons a es ha he co ela ions among all exogenous a iables we e benea h he h eshold
(i.e., = 0.90). The co ela ion ma ix demons a es he lack o mul icollinea i y among hese a iables. Secondly, he esea che
looked a all he exogenous la en cons uc s' ole ance le els and a iance in la ion ac o (VIF) alues.
Hai e al. (2014) and Field (2017) ad oca ed ha he commonly ecognised le el o mul icollinea i y o a VIF alue ≤ 10 indica es
no mul icollinea i y, bu a alue mo e han 10 indica es se e e mul icollinea i y. While a ole ance le el o ≥ 0.1 indica es no
mul icollinea i y, a numbe less han 0.1 indica es subs an ial mul icollinea i y. Howe e , Kock (2015) a gued ha he p esence o
VIF mo e han 3.3 and less han 0.303 ole ance le el is a sign o pa hological collinea i y and common me hod bias.
Table 5: Co ela ion Ma ix (n=402).
Cons uc
GPS
CIB
MBS
PDS
RMT
GPS
1
CIB
.031
1
MBS
.681**
0.021
1
PDS
.485**
0.021
.732**
1
RMT
.812**
0.000
.553**
.553**
1
Sou ce: SPSS Ve sion 27 (2024). **. Co ela ion is signi ican a he 0.01 le el (2- ailed).
Table 5 shows ha mul icollinea i y is absen among he exogenous la en componen s since he VIF alues a e smalle han he
0.303 h eshold Kock (2015) indica ed. As a esul , mul icollinea i y is no a conce n in ou in es iga ion.
Table 6: Tole ance Le el and Va iance In la ion Fac o (VIF) Values
Collinea i y S a is ics
Endogenous Va iable
La en Cons uc s
Tole ance le el
VIF
GPS
0.836
1.196
FP
CIB
0.996
1.004
MBS
0.502
1.992
PDS
0.389
2.574
RMT
0.485
2.062
Sou ce: SPSS Ve sion 27 (2024).
Table 6 shows he VIF alues o i e la en cons uc s, assessing collinea i y. A highe ole ance le el (>0.2) and VIF alues below
5 indica e low mul icollinea i y, sugges ing eliable da a o eg ession analysis. GPS (Tole ance: 0.836, VIF: 1.196) and CIB
(Tole ance: 0.996, VIF: 1.004) ha e e y low VIF alues, indica ing minimal mul icollinea i y.
4.4 Assessmen o SEM-PLS Pa h Modelling
The s a is ical ou comes o he a ious analyses, such as he eliabili y o indi idual i ems assessing each la en concep , in e nal
consis ency, and disc iminan alidi y, among o he ac o s, we e examined. The e o e, he PLS algo i hm was used o assess
indi idual i em eliabili y and o he measu emen model c i e ia, wi h ou e loadings indica ing he eliabili y o e lec i e
cons uc s. Acco ding o Hai e al. (2014), an ou e loading o 0.70 is ideal o es ablished scales, bu i ems wi h loadings be ween
0.40 and 0.70 may be e ained i lea ing hem imp o es he AVE and CR. Following Hulland’s (1999) guideline, loadings below
0.5 should be excluded. Consequen ly, i e i ems we e emo ed, imp o ing he AVE and CR, lea ing he emaining i ems sui able
o u he analysis as shown in Figu e 1 and Table 6.
The In luence o Online T ansac ions on Cus ome s Deposi and Ope a ing Expenses o License Deposi Money Banks
(DMBs) In Nige ia
IJSSHR, Volume 08 Issue 10 Oc obe 2025 www.ijssh .in Page 7752
Figu e 1: Measu emen Model
Sou ce: Sma PLS e sion 3.2.8 (2024).
Figu e 1 and Table 6 collec i ely p esen he measu emen model's assessmen , showcasing he e alua ion o bo h i em eliabili y
and he o e all cons uc alidi y and eliabili y o he e lec i e cons uc s examined in he s udy. The indica o loadings o each
cons uc me he minimum h eshold o accep abili y, indica ing su icien indi idual i em eliabili y o mos i ems. Howe e , i e
i ems—FP3, FP5, FP8, GPS3, and RMT1— ell below he accep able loading c i e ia and we e emo ed o enhance he model's
quali y. Elimina ing hese i ems imp o ed AVE and CR me ics, s eng hening cons uc alidi y. The emaining i ems, all o which
me o exceeded he equi ed loading h eshold, we e deemed eliable and alid o con inued analysis in he s udy. This e inemen
ensu es ha he measu emen model aligns wi h es ablished academic s anda ds o eliabili y and alidi y, enabling mo e obus
and accu a e s uc u al model e alua ions in subsequen analyses.
Table 7: I em Loadings, In e nal Consis ency, and AVE.
Cons uc s
Indica o s
Loadings
C onbach's
Alpha
Composi e Reliabili y
AVE
Co po a e In e ne Banking
CIB1
0.803
0.781
0.858
0.603
CIB2
0.692
CIB3
0.779
CIB4
0.826
Global Pay Sys em
GPS1
0.807
0.715
0.812
0.521
GPS2
0.642
GPS4
0.683
GPS5
0.745
Mobile Banking Sys em
MBS1
0.778
0.827
0.877
0.588
MBS2
0.748
MBS3
0.801
MBS4
0.839
MBS5
0.657
Pay Di ec Sys em
PDS1
0.879
0.762
0.847
0.585
PDS2
0.830
PDS3
0.711
PDS4
0.609
The In luence o Online T ansac ions on Cus ome s Deposi and Ope a ing Expenses o License Deposi Money Banks
(DMBs) In Nige ia
IJSSHR, Volume 08 Issue 10 Oc obe 2025 www.ijssh .in Page 7753
Financial Pe o mance
FP1
0.724
0.785
0.854
0.540
FP2
0.721
FP4
0.805
FP6
0.655
FP7
0.759
Remi a
RMT2
0.738
0.705
0.765
0.526
RMT3
0.838
RMT4
0.576
Sou ce: Resea che ’s Composi ion (2024).
4.5 Assessmen o he S uc u al Model
This sec ion p esen ed he SEM o da a analysis on di ec and media ed ela ionships h ough boo s ap analysis. A ypical
boo s apping me hod was employed, u ilising 5000 boo s ap samples om 402 examples o examine he impo ance o he ou e
coe icien s in he di ec ela ionship (Hai e al., 2014; Hai e al., 2021).
Figu e 2: S uc u al Model
Sou ce: SEM-PLS Ve sion 3.2.8 (2024).
Table 8: Pa h Coe icien (Di ec E ec )
Hypo heses
R.Ship
Be a
S d. De .
T S a is ics
P Values
Decision
H01
GPS -> FP
0.374
0.038
9.842
0.000
Rejec ed
H02
CIB -> FP
0.189
0.037
5.031
0.000
Rejec ed
H03
MBS -> FP
0.096
0.044
2.188
0.029
Rejec ed
H04
PDS -> FP
0.336
0.031
10.683
0.000
Rejec ed
H05
RMT -> FP
0.011
0.040
0.289
0.773
Accep ed
Sou ce: SPSS Ve sion 27 (2024). ***p<0.01, **p<0.05.
Table 8 p esen s pa h coe icien s ep esen ing he di ec e ec s on licensed DMBs' pe o mance in Nige ia. The i s hypo hesis
(H01) posi s a ela ionship be ween Global Pay Sys em (GPS) and inancial pe o mance (FP), wi h a be a coe icien o 0.374. The
s anda d de ia ion (STDEV) associa ed wi h his coe icien is 0.038, yielding a T s a is ic o 9.842 and a p- alue o 0.000 a a 1%
(p<0.01) signi icance le el. The null hypo hesis is ejec ed, sugges ing ha GPS signi ican ly in luences he pe o mance o License
DMBs in Nige ia. The second hypo hesis (H02), which examines he associa ion be ween Co po a e In e ne Banking (CIB) and FP,
exhibi s a be a coe icien o 0.189. The associa ed STDEV is 0.037, esul ing in a T s a is ic o 5.031 and a p- alue o 0.000.
Consequen ly, H02 is ejec ed, indica ing a signi ican impac o CIB on he FP o licensed DMBs in Nige ia.
The hi d hypo hesis (H03) ocuses on he Mobile Banking Sys em (MBS) and i s e ec on FP, e ealing a coe icien o 0.096, a
s anda d e o o 0.044, a -s a is ic o 2.188, and a p- alue o 0.029. These igu es indica e signi icance a he p<0.05 le el.
The In luence o Online T ansac ions on Cus ome s Deposi and Ope a ing Expenses o License Deposi Money Banks
(DMBs) In Nige ia
IJSSHR, Volume 08 Issue 10 Oc obe 2025 www.ijssh .in Page 7754
The e o e, H03 is ejec ed, sugges ing a signi ican nexus be ween MBS and he FP o licensed DMBs in Nige ia. Hypo hesis ou
(H04) explo es he in luence o he Pay-di ec sys em (PDS) on FP, yielding a be a coe icien o 0.336. The associa ed STDEV is
0.031, esul ing in a T s a is ic o 10.683 and a p- alue o 0.000. Wi h a signi icance le el o p<0.01, H04 is ejec ed, indica ing a
signi ican impac o PDS on he pe o mance o license DMBs in Nige ia. Hypo hesis i e (H05) examines he ela ionship be ween
emi a (RMT) and FP, e ealing a be a coe icien o 0.011. The STDEV associa ed wi h his coe icien is 0.040, yielding a T
s a is ic o 0.289 and a p- alue o 0.773, ailing o ejec he null hypo hesis. This sugges s ha RMT does no signi ican ly in luence
he inancial pe o mance o DMBs in Nige ia. Fu he mo e, he compu a ion o he coe icien o de e mina ion (R2) is p esen ed
in Table 9.
Table 9: Coe icien o De e mina ion (R2).
Cons uc
R2
Pe o mance
0.570
Sou ce: SPSS Ve sion 27, (2024).
An R-squa e sco e o 0.570 signi ies ha oughly 57% o he a iance in he dependen a iable is elucida ed by he independen
a iables inco po a ed in he model. In o he wo ds, ac o s such as Global Pay Sys em and Co po a e In e ne Banking accoun o
abou 57% o he a iabili y obse ed in he inancial pe o mance o licensed DMBs in Nige ia. The e o e, while he model p o ides
aluable insigh s in o he ac o s a ec ing DMBs' pe o mance, i does no cap u e he en i e complexi y o he phenomenon.
Acco ding o Chin's (1998) asse ion, R2 alues o 0.67, 0.33, and 0.19 should be ega ded as subs an ial, mode a e, and weak,
espec i ely. The R2 alue o 0.570, a ibu ed o ex e nal la en a iables a ec ing he a ge endogenous la en a iable, is modes .
4.6 Assessmen o E ec Size ( 2)
E ec size ( 2) p o ides a measu e o how s ong he ela ionship is be ween a iables in a s a is ical analysis. I goes beyond jus
de e mining i he ela ionship is s a is ically signi ican , helping esea che s unde s and he p ac ical impo ance o hei indings
(Ojeleye e al., 2023). A la ge e ec size indica es a s onge ela ionship be ween a iables, while a smalle one sugges s a weake
connec ion. By conside ing e ec size alongside signi icance es ing, esea che s can be e assess he eal-wo ld signi icance o
hei esul s and unde s and he impac o independen a iables on he dependen a iable (Haie e al., 2010). Essen ially, e ec
size helps esea che s gauge he ac ual s eng h o ela ionships in hei da a, making hei in e p e a ions mo e meaning ul and
applicable o eal-li e si ua ions. Thus, he compu a ion o he e ec size is achie ed using Cohen’s o mula (Cohen, 1988; Hai e
al., 2021) gi en as:
Whe e:
2 = he F-squa ed alue ha speci ies he e ec size o an exogenous a iable's in luence on an endogenous a iable.
R2Included = is he R2 alue o he endogenous a iable be o e omi ing a pa icula exogenous cons uc .
R2excluded = ep esen s he changes in he R2 alue o he endogenous a iable a e excluding a pa icula exogenous a iable
om a model.
The 2 alues o 0.02, 0.15, and 0.35 co espond o small, medium, and la ge e ec s, espec i ely (Cohen, 1988). In a manne akin
o indi ec ela ionship hypo hesis es ing, he al e a ion in R2 alue upon he exclusion o a speci ic exogenous a iable om he
model se es o assess whe he an omi ed a iable has a signi ican impac on he la en endogenous a iable (Hai e al., 2014).
Table 10 p esen s he esul s o he e ec size analysis.
Table 10: Assessmen o E ec Size ( 2)
Cons uc s
FP
E ec Size
CIB
0.047
Small
GPS
0.176
Medium
MBS
0.010
Small
PDS
0.220
Medium
RMT
0.000
Nill
Sou ce: SEM-PLS Ve sion 3.2.8 (2024).
Table 10 assesses he e ec size ( 2) o a ious cons uc s in he analysis. The e ec sizes indica e he magni ude o each cons uc 's
impac on he FP o license DMBs in Nige ia. CIB's e ec size is 0.047, which is a small e ec , sugges ing a modes in luence on
