Ag awal, Reena; Yada , Maneesh
A icle
Enable s o usage o he mobile walle by msmes in
u al India: Using he in e p e i e s uc u al modelling
app oach
Financial In e ne Qua e ly
P o ided in Coope a ion wi h:
Uni e si y o In o ma ion Technology and Managemen , Rzeszów
Sugges ed Ci a ion: Ag awal, Reena; Yada , Maneesh (2024) : Enable s o usage o he mobile walle
by msmes in u al India: Using he in e p e i e s uc u al modelling app oach, Financial In e ne
Qua e ly, ISSN 2719-3454, Sciendo, Wa saw, Vol. 20, Iss. 2, pp. 26-41,
h ps://doi.o g/10.2478/ iq -2024-0010
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/329871
S anda d-Nu zungsbedingungen:
Die Dokumen e au EconS o dü en zu eigenen wissenscha lichen
Zwecken und zum P i a geb auch gespeiche und kopie we den.
Sie dü en die Dokumen e nich ü ö en liche ode komme zielle
Zwecke e iel äl igen, ö en lich auss ellen, ö en lich zugänglich
machen, e eiben ode ande wei ig nu zen.
So e n die Ve asse die Dokumen e un e Open-Con en -Lizenzen
(insbesonde e CC-Lizenzen) zu Ve ügung ges ell haben soll en,
gel en abweichend on diesen Nu zungsbedingungen die in de do
genann en Lizenz gewäh en Nu zungs ech e.
Te ms o use:
Documen s in EconS o may be sa ed and copied o you pe sonal
and schola ly pu poses.
You a e no o copy documen s o public o comme cial pu poses, o
exhibi he documen s publicly, o make hem publicly a ailable on he
in e ne , o o dis ibu e o o he wise use he documen s in public.
I he documen s ha e been made a ailable unde an Open Con en
Licence (especially C ea i e Commons Licences), you may exe cise
u he usage igh s as speci ied in he indica ed licence.
h ps://c ea i ecommons.o g/licenses/by-nc-nd/3.0/
10.2478/ iq -2024-0010
Abs ac In oday`s digi al e a, i is all he mo e desi able ha all businesses, whe he gigan ic o mino in
ope a ion, should adop inancial echnology and g ow hei businesses wi hin hei egion and
ou side o he egion. The Mic o, Small & Medium En e p ises (MSME) segmen is he mains ay
o he Indian economic en i onmen hus i is c ucial ha MSME playe s adop inancial echnol-
ogy in hei ou ine comme cial ansac ions. The p esen esea ch s udy was conduc ed o in-
es iga e he enable s o usage o a Mobile Walle by MSMEs in u al a eas o India using in e -
p e i e s uc u al modeling. I aims o ca ego ize he o emos enable s and assess he ela i e
ela ions be ween he ecognized en key enable s and p oposes a hie a chical ou line o i al
enable s on MSME en ep eneu s om se e al hill s a es o India. The mos p ominen enable
ha p omo ed he use o mobile walle s among MSME en ep eneu s included isk ac o s and
pe cei ed cos . The s udy sugges ed ha mul i-dimensional de e mina ions a e equi ed o
make ce ain o he use o mobile walle s by MSME en ep eneu s so ha hey can assess maxi-
mum economic oppo uni ies and suppo he iscal expansion o he s a e and he coun y.
JEL classi ica ion: MOG
Keywo ds: Mobile Walle , Pe cei ed Cos , Risk Fac o , Me chan Suppo , Compliance
Recei ed: 01.03.2024 Accep ed: 05.04.2024
Ci e his:
Ag awal R. & Yada M. (2024). Enable s o usage o he mobile walle by MSMEs in u al India: Using he in e p e i e s uc u al modelling app o-
ach. Financial In e ne Qua e ly 20(2), pp. 26-41.
© 2024 Reena Ag awal and Maneesh Yada , published by Sciendo. This wo k is licensed unde he C ea i e Commons A ibu ion-NonComme cial-
NoDe i a i es 3.0 License.
1 Jaipu ia Ins i u e o Managemen , Lucknow, India, e-mail: eena.aga wal@jaipu ia.ac.in, ORCID: h ps://o cid.o g/0000-0002-6680-8251, Scopus
ID:57970592700.
2 Tee hanke Maha ee Uni e si y, Mo adabad, India, e-mail: mane[email p o ec ed].in, ORCID: h ps://o cid.o g/0000-0001-6328-1282.
The p ominen pilla o he Indian economy is he
MSME sec o , and i con ibu es in pa allel wi h he
ag o sec o . I is e ealed om he MSME epo o
2018 ha he sec o has shown ema kable de elop-
men in he las six yea s. The employmen oppo uni-
ies and he p omo ion o an en ep eneu ial skill se is
a posi i e ou come o he sec o wi h low in ol emen
o capi al. The MSME sec o is he backbone o hea y
indus ies and helps in he la ge economic con ibu-
ion wi h a ious p oduc s and se ices (Kau , 2021). As
pe he epo o 2018, he sec o has con ibu ed o
manu ac u ing GDP and pa icipa ed in he expo s o
he coun y. I has made a 5.50% con ibu ion o manu-
ac u ing GDP and mo e han 48% in expo s (MSME,
2021). The pa icipa ion o he MSME sec o in Indian
GDP is nea ly 28% and as mo ing upwa d. The MSME
sec o is also a p ominen employe o he u al labo
o ce. The uppe limi o in es men in he MSME man-
u ac u ing and se ices sec o is emo ed om he
June 2020 amendmen by he Indian Minis y o Fi-
nance (Rawa , 2022).
The simila i y o he M-walle wi h he physical
walle is on equal oo ing wi h he added ad an age o
online money unca ion along wi h he physical ans-
ac ion wi h secu i y measu es. The g ea e ad an age
o he M-walle is in he small ansac ions a he en-
do shop o pe son o pe son ans e o i em pu -
chase, e c. Plas ic money is also no he al e na e o an
M-walle due o ewe ea u es and being limi ed o
o ganized e ail. The use o he M-walle is also subjec
o a ious obs uc ions like disbelie , app ehension,
diso de , discomposu e, e c. These psychological ob-
s uc ions a e desc ibed as men al cos s (Cha e jee
& Bola , 2019). The da a e lec s ha he E-walle was
es ima ed a 1043 USD in he yea 2019 on he in e na-
ional le el and p edic ed o ise o 7580 by he yea
2027 (K ishna & Kuma , 2023). In he yea 2019, he
Indian popula ion o app oxima ely. 74 million used he
E-walle o inancial ansac ions. The es ima ed
g ow h o mobile ansac ions in he coun y will be
inc eased by h ee imes by he yea 2024 om 36.5
illion INR in 2019. Digi al paymen has g own wi h he
join e o s o go e nmen policies and inc ease in he
usage o he in e ne (K ishna & Kuma , 2023). In-
c eased digi al paymen e lec ed by he da a eleased
by he RBI shows ha he o e all inc ease o 500% had
been wi nessed in digi al disbu semen s by he ade s
be ween Ap il 2021 o Sep embe 2021 ela ed o Oc-
obe 2018 o Ma ch 2019 and Uni ied Paymen s In e -
ace (UPI) paymen by 1200%. The inc ease o he us-
age o digi al paymen s be ween Ma ch 2019 and
Ma ch 2022 was nea ly 216%. The da a e eals he
decline in he usage o pape money om 3.83% o
0.88% in he same pe iod in e ms o olume and
19.62% o 11.47% in alue (Gandhi, 2023). MSMEs can
In he globaliza ion o he wo ld economy and
echnological ad ancemen , FinTech is c ucial and
g owing apidly wi h huge in es men s by en u e capi-
al und manage s. The da a e eals ha i g ew om
1.8 billion o 56 billion US dolla be ween 2010-18
(Accen u e, 2019). I is also he ocused a ea o p i a e
co po a e playe s and go e nmen s explo ing new ma -
ke oppo uni ies and ad ancemen o in e na ional
inancial cen e s. The cos e ec i eness, high ou pu ,
echno- iendly, and cus ome o ien a ed Fin ech has
made he inancial sec o mo e ola ile and luc a i e
o in es men and cus ome sa is ac ion. The jou ney
o de elopmen has changed wi h he adop ion o
FinTech in low-income coun ies (Lai & Same s, 2021).
Fin ech is he esul ing p oduc o inancial inno a-
ion and o mode n echnological echniques like a i i-
cial in elligence and big da a o ca e o he needs o
inancial ins i u ions. (Financial S abili y Boa d
[FSB], 2017). Technological inno a ion is he essence o
Fin ech de elopmen and he egula usage o Fin ech
in inancial ins i u ions will lead o p oduc inno a ion
(Chen e al., 2022). The Indian go e nmen ini ia i e
“Digi al India” has a deep- oo ed connec ion wi h digi-
al echnology’s e olu ion in he mid wen ie h cen u y
by Ame ican enginee s. Mic o, Small & Medium En e -
p ises (MSMEs) ha e also come in o con ac wi h he
p o i s o digi al echnologies in hei co po a e de el-
opmen (Du a e al., 2020). I is p og essi ely adop ed
wi hin he o emos asks o ma ke ing o make he
cus ome s awa e o he launch o new p oduc s o se -
ices (Rawa e al., 2022). The adop ion o Fin ech and
inno a ion has no inc eased he popula i y o he digi-
al mode o inancial ansac ions among Indian use s.
The cash o GDP a io is lowe han he expec ed
benchma k as equalized wi h he p e ious e a o de-
mone iza ion (Ligon e al., 2019). The da a e eals ha
he use o he mobile walle has shown emendous
g ow h in he yea s 2012 o 2016. The walle ansac-
ions inc eased om 10 o 490 billion in his pe iod.
The esea ch es ima es sha ed by a leading esea ch
i m shows ha Indian walle ansac ion ma ke would
ise o 144,915039 USD by 2019 (Chak abo y & Mi a,
2018). The da a eleased by he GlobalDa a Plc has
shown ha digi al paymen s ia M-walle will inc ease
by 23% be ween he yea s 2023-2027 (Li emin , 2024).
The ema kable expansion o he echnological
space has boos ed he g ow h o M-walle s, inancial
inclusion in he economy and makeo e o he digi al
space. I has also inc eased he consume ’s depend-
ence on sma phones. (Esawe & Elwkeel, 2020) e-
ealed ha he echnological space di ac ion has c e-
a ed new ac i i ies and de elopmen s in inancial ans-
ac ions in global economies and also uled ou ha he
adi ional medium o ansac ions canno be o e uled
(Esawe, 2022).
ac u ing sec o om 16% o 25% by he yea 2022 o
a ac mo e manu ac u ing (S i as a a, 2020). The
p e ious decades ha e shown he use and inc ease in
he accep ance o inno a i e echnology in he a ea o
indus ial ou pu and he MSME sec o has adop ed he
new echnology in a ib an manne . I has boos ed he
g ow h o indus ial ou pu wi h he op imiza ion o
human e o s. (Mi a, 2013). The incessan e o s o
he go e nmen a e also ocused on making he econo-
my lean owa ds mo e digi al paymen and a cashless
socie y (Jain e al., 2020). To s imula e digi al pay-
men s, Aadhaa based e-paymen s we e used o small
businesses. I was ini ia ed o c ea e he awa eness o
digi al paymen s by he Minis y. As a esul , digi al
paymen s ansac ions we e inc eased o 92.02% in
alue and mo e han 90 % in ansac ion numbe s in
he yea 2020-21 in he Minis y and o he o ices
(Upasana & Bhawna, 2022). A s udy e eals ha 70% o
MSMEs will use he UPI paymen mode o hei e ail
sales in he nex ew yea s based on a su ey o mo e
han 1000 e aile s in India and i s da a base (ETonline,
2023). The e has been a g ow h o nea ly 33 pe cen
34 Y-o-Y in walle -based ansac ions in he las wo
yea s (Jain, 2023).
Fac o s enabling MSMEs in Ru al India o use he
Mobile Walle :
1. Secu i y Measu es: The M-walle is he as es and
secu e medium o money ans e bu wi h he ad-
ancemen o echnology, secu i y h ea s also pe -
sis like ansomwa e, phishing and unwan ed so -
wa e applica ions. Re aile s also ace simila isks i.e.
selling poin (POS), malwa e, MiTM and eplay
a acks. I also includes he isk o se ice p o ide s
like da a leaks, cloud managed p o ile hacking, e c.
(B id, 2019). Mansi and Dha mend a (2019) wo ked
on he h ea s in ol ed in he M-walle and used he
da a om se ice p o ide s ope a ing in India and
epo ed ha imp o emen s in he mobile applica-
ion enhanced he ad ancemen o he paymen
applica ion. I was sugges ed in he s udy o ind
speci ic solu ions o each isk o imp o e he con i-
dence o he use . Sa da (2016) esea ch e ealed
ha inc ease o he elecom in as uc u e and use
o sma phones has led o inc ease in mobile walle
paymen s in India. The Walle is equen ly used o
as e paymen and day o day u ili y bills o e-
cha ge and make quick paymen s (Ba acka h, 2021).
2. Awa eness and Adop ion: M-walle adop ion among
cus ome s a ies om one o ano he de e minan
such as p i acy issues, in as uc u e suppo , p od-
uc awa eness and physiological issues, e c. (Oli ei a
e al., 2016). I is obse ed in he pas esea ch ha
e en hough he M-walle s a e easie o use and
make inancial ansac ions secu e as well as s eady,
bu cus ome s a e hesi a ing in he adop ion o he
echnology g ow h s o y only wi h adop ion and usage
o Fin ech such as he mobile walle hus he p esen
s udy was aken up o seek answe s o he ollowing
esea ch ques ions:
RQ1: To iden i y key enable s o usage o he Mobile
Walle by MSME`s in u al a eas o India,
RQ2: To e alua e he con ex ual ela ionships among
iden i ied key enable s,
RQ3: To de elop hie a chical amewo k o key enable s
o usage o he Mobile Walle by MSME`s in u al
a eas o India.
The pas ew yea s ha e wi nessed digi al ansac-
ion me hods become a c ucial backbone o inancial
inclusion policies. In e na ional unding has con ibu ed
mo e han hi y billion dolla s o de elop mobile mon-
ey pla o ms e e y yea (Ligon e al., 2019; The Consul-
a i e G oup o Assis he Poo , 2023). The sha ed e-
po on mobile paymen se ices in India shows i has
inc eased emendously and he cus ome base has
been inc eased o 1183 million use s by Feb ua y 2019.
I is e iden om he da a ha India has he la ges
mobile use da a base and is posi ion hi d in he wo ld
anking. I wo ks as a boos e o inancial inclusion and
suppo e o capi al ans e o a majo sec ion o soci-
e y (Sinha & Singh, 2019). The e olu iona y change in
he accep ance o mobile se ices has inc eased he
numbe o mobile use s ac oss he globe. The expec ed
g ow h o mobile da a consump ion eached 19 GB in
2023 and is expec ed o each mo e han 68.5 GB pe
use in 2028 in India, Nepal and Bhu an (E icsson,
2023). The usage o mobile da a om 1.24 GB o mo e
han 14 GB inc eased om 2017 o 2022. B oadband
usage inc eased om 32% o 96% om 2015 o 2022. I
is lowes among he BRICS na ion wi h he eco d o
18.3 % e- ansac ions (RBI, 2017). Digi al paymen
g ow h c ossed om 0.9% o 21.5% in he pe iod o
2012-2017. The compe i i e ma ke o e-comme ce
educed he cos o mobile da a om 268.9 o 6.6/-
Indian upees om 2014 o 2022 (Chand e al., 2023).
The expec ed g ow h o mobile walle s paymen s is
23.9% om 2023 o 2027 and ansac ional alue up o
472 illion in 2027 (Li emin , 2024).
The MSME sec o has shown emendous s eng h
in gene a ing employmen , economic upli ing o socie-
y and business inno a ions. The con ibu ion o he
sec o is 45% in manu ac u ing, 40% in expo s, 28% in
GDP, 111 million jobs c ea ed wi h he help o mo e
han mo e han six y- h ee million en e p ises. The
MSME sec o compe es wi h he ag icul u e sec o in
e ms o employmen gene a ion. The ad anced ech-
nology adop ed by he sec o enhances he alue o
p oduc s and se ices. The na ional manu ac u ing poli-
cy is seeking o inc ease he pa icipa ion o he manu-
a e en i led o ge he bene i o he scheme. In an-
o he egula o y de elopmen , P epaid Paymen
Ins umen s (PPI), 2021 (“PPI Regula ions”) was is-
sued. I was also holding p o isions o he Rese e
Bank o India (Issuance and Ope a ion o P epaid
Paymen Ins umen s) Di ec ions, 2017. The mas e
ci cula by he RBI has included all he ci cula s is-
sued by he RBI du ing he pe iod o 2017-2021. The
de elopmen o he egula ing en i onmen o he
M-walle s shows he budding na u e o go e nance
in p e-paymen ins umen s,
6. Compa ibili y: The compa ibili y o he M-walle on
elec onic de ices is he majo cause o adop ion o
he walle se ice. I is e ealed om he esea ch
s udies ha compa ibili y has a di ec posi i e co-
ela ion wi h he cus ome ’s in en o use, IU, and
accep ance o echnology (Oli ei a e al., 2016). The
same con ex is applied o he M-paymen s and i
was obse ed ha compa ibili y is he s ong ac o
o adop ion o digi al paymen means (Yang e al.,
2012; Choud ie e al., 2014)
7. Digi al In as uc u e: The digi al in as uc u e o he
coun y has imp o ed in he u ban a ea bu he
g ow h o he u al a ea is no so signi ican . The
o al numbe o mobile connec ions a e nea ly 1.2
billion and mo e han 500 million in e ne use s.
E en hough in as uc u e was buil o digi al pay-
men s, ou ine p ac ice akes ime o change. The
da a e eals ha he digi al dealings ha e inc eased
by 42% om he cu en da a o 672 million o 958
million use in 2016. In he nex yea , i declined and
eached 763 million use s in 2017. The de elopmen
o he digi al in as uc u e, awa eness and educa-
ion (RBI, 2017; Tiwa i, 2019),
8. F equen ad ancemen in use in e ace: A s udy
e ealed ha he use in e ace, ea u es o he
pla o m, and display ha e a no ewo hy esul on
he use pa e n o elec onic pu chases. (Bagla
& Sanche i, 2018; Ha & S oel, 2009). The s udy e-
ealed ha adop ion o e-comme ce in India is
g ounded on he wo h and ease o he se ice
(Malik e al., 2013),
9. Pe cei ed Cos : The meaning o he pe cei ed alue
indica es he exchange alue be ween consume s
ecei ing and spending on a p oduc (Amo oso
& Magnie -Wa anabe, 2012). I is use ul in unde -
s anding he buying beha io o cus ome s in elec-
onic se ices (Ka jaluo o e al., 2019). I also e-
lec s he p o i s cus ome s assume, ecei e o p e-
dic (Kuma & Reina z, 2016) and o c ea e long-
s anding consume ela ionships in ma ke s (Shapi o
e al., 2019). I will di e om cus ome o cus om-
e , p oduc o se ices and na u e o business
(Zei haml, 1988),
10. Risk Fac o : I is e ealed om he s udy ha cus-
ome s a e awa e o E-walle ansac ions and hei
ad anced ea u es bu ha e conce ns ela ed o he
o he echnology (Wu e al., 2017). The cause o he
non-adop ion o he M-walle is due o us issues,
lack o secu i y, lack o knowledge o he echnology,
egula ea u e upda es om he se ice p o ide s
(Zhou, 2012),
3. Me chan suppo : The Indian Go e nmen has s a -
ed aking p og essi e s eps and p o iding incen i es
o encou age digi al paymen s. E en so, a ew e ail-
e s a e no using i due o lack o echnological
knowledge and despi e go e nmen incen i es
based on he ansac ion alue o he ade. Va ious
incen i es we e associa ed wi h he paymen o oll
ax, insu ance, pu chase o icke s, e c. (Da e, 2016).
The adi ional way o paymen , i.e. cash and ca ds,
a e p e alen among e aile s. Only h ee million
ade s app oxima ely a e using digi al paymen op-
ions which only cons i u es 2% app ox. o he o al
Indian ade popula ion and es a e dependen on
he adi ional me hods o ansac ions. I is e lec -
ed by he s udy ha indi ec axes like VAT and GST
ha e educed digi al paymen s due o ax a oidance
p ac ices and ax implica ions and ma gin conce ns.
(Heyda i & Bailey, 2016; Singh & Sinha, 2020),
4. E-Li e acy: Wi h he anking o he i h la ges econ-
omy in he wo ld, India has poo digi al in as uc-
u e and educa ion connec ing he u al pa o he
coun y. The undamen al sou ce o he digi al illi e -
acy and lack o ech sa y is he u al popula ion
which wo ks in he uno ganized sec o wi h no digi-
al paymen in e ace o ansac ions such as sala y
o o he bene i s and hey cons i u e a majo popu-
la ion o he coun y (Se anmade i e al., 2019).
Sma phones ha e been wo king as an enable in
he socio-economic upli ing o he use s in a mul i-
ace ed manne . In addi ion o he ad ancemen ,
he new ea u e o he paymen op ion wi h he
phone is hoped o expedi e inancial ac i i ies. (Pal
e al., 2020),
5. Compliance: The legal compliance issues ela ed o
e-walle s a e associa ed wi h he con ol o he pay-
men sys em, mone a y policy adminis a ion and
egula ions go e ning he Indian banking sys em.
The demand and supply a io main enance o he
bank ese e is he isk ac o in he walle paymen
sys em. C edi ca d epaymen issues also gene a e
c edi acili y issues. E-walle paymen s a e also
s uggling om he clea ing and se lemen o pay-
men s and liquidi y issues (Shai wal, 2021). To cu b
he p oblems o paymen and o he supe ision is-
sues, he Paymen and Se lemen Sys ems Ac o
2007 was enac ed o moni o he paymen ansac-
ions h ough plas ic ca ds, online ansac ions and
E-walle s. In his ega d, RBI has also issued a mas e
ci cula o ins umen s, i.e. “Policy Guidelines on
Issuance and Ope a ion o P e-Paid Paymen Ins u-
men s in India” which desc ibed he kinds o p e-
paymen ins umen s. I also sugges ed banks which
a iables which a e pe inen o he s udy. This can be
done h ough e iew o exis ing li e a u e o h ough
a su ey. (b) Es ablish a con ex ual linkage among he
ac o s. (c) C ea e a s uc u al sel -in e ac ion ma ix
(SSIM) (d) C ea e a eachabili y ma ix om he SSIM.
(e) Assign le els o he s udied a iables. ( ) C ea e
a dig aph buil on he inal eachabili y ma ix. (g) Con-
e he dig aph in o anmISM model by subs i u ing
elemen nodes wi h he s a emen s.
The a iables which a e pe inen o he s udy
we e iden i ied h ough an ex ensi e e iew o he ex-
is ing li e a u e. The sou ces used o he s udy we e
gene ally om leading da abases like Scopus, Web o
Science, and EBSCO. The ini ial sea ch was done using
he i le o he a icles and he abs ac . We iden i ied
se en y- h ee a icles ha we conside ed ele an o
he esea ch. We hen s udied he a icles in de ail and
emo ed he a icles ha we e no ound o be o
much ele ance. In o al, he s udy iden i ied en a ia-
bles ha we e ound o be ex emely ele an o he
s udy. We e ed he iden i ied a iable wi h he ex-
pe s and once we ecei ed hei opinion hen we
mo ed ahead o c ea e a ma ix ha we used o cap-
u e he iewpoin o ou esponden s. Gi en below is
he ma ix ha was used o he s udy (Table 1).
secu i y o ansac ions h ough e-walle s (Undale e
al., 2021; B ahmbha , 2018). The o he ac o s a e
gene al p i acy, ansac ion secu i y, expec ed pe -
o mance, ansac ional bene i s as s udied in he
esea ch (Soodan & Rana, 2020). Ano he s udy e-
eals ha he s o ed da a on he phone is also he
conce n and ac o a ec ing he use o E-walle s
(Chawla & Joshi, 2019).
In In e p e i e S uc u al Modeling (ISM) a se o
di e se di ec ly and indi ec ly linked ac o s o a ia-
bles a e o ganized in a comp ehensi e o de ly model.
The model so c ea ed ep esen s he assembly o com-
plex issues o p oblems in a sensibly designed pa e n
(Sage, 1977; Wa ield, 1974a, 1974b, 1982a, Wa son,
1978). The ISM app oach con e s an unce ain, poo ly
exp essed men al map o s uc u es in o a no iceable
and dis inc model. ISM in a sense deals wi h wha
Flood (1988) labelled as psychological in icacy in
di e en sensi i i ies o he pa icipan s (Flood, 1988).
ISM makes use o p ac ical expe ience and insigh s o
specialis s o c ea e a a ional anked s uc u e o ac-
o s explo ed in he s udy (Al-Mu ah e al., 2018; Rana
e al., 2019; Agi & Nishan , 2017; Bakshi e al., 2023).
ISM modeling includes he ollowing s eps: (a) Find he
Table 1: Expe Response Ma ix
Code Enable s E10 E9 E8 E7 E6 E5 E4 E3 E2 E1
E1 Secu i y Measu es
E2 Awa eness and Adop ion
E3 Me chan Suppo
E4 Compa ibili y
E5 Compliance
E6 Compa ibili y
E7 Digi al In as uc u e
E8 Ad ancemen in use in e ace
E9 Pe cei ed Cos
E10 Risk Fac o
Sou ce: Au ho ’s c ea ion.
we e in e iewed. These included Himachal P adesh
(3), Ladakh (3), Jammu & Kashmi (3) and U akhand
(4). Mul iple mee ings we e conduc ed in o de o un-
de s and he pe cep ion o hese MSME owne s abou
he a ious enable s chosen o he s udy. These MSME
owne s we e mos ly ope a ing e ail businesses such as
swee s shops, a el agencies, gene al s o es, mo els,
en ed ca se ice, medical s o es, e c. The annual u n-
o e o hese MSMEs anged be ween INR 1.5 hund ed
housand o INR 2.5 hund ed housand. Mos o he
esponden s we e g adua es and be ween he age
g oup o 40-50 yea s.
In his s udy MSME playe s we e loca ed ope a ing
in isola ed u al a eas o Himachal P adesh, Ladakh,
Jammu & Kashmi , and U akhand. The main eason
o choosing hese egions was o e alua e whe he
mic o small and medium en e p ises loca ed in hese
hilly egions we e using he in ech se ices o adi-
ional mode o ansac ions. As pe Rana e al. (2019)
and Kuma e al. (2016), he ideal sample size o In e -
p e i e S uc u al Modeling Technique is en o eigh -
een expe s. In o al, wen y MSME owne s we e con-
ac ed bu ou o hese six MSME owne s we e unwill-
ing o spa e he ime. Finally, ou een MSME owne s
codes we e used o ep esen he end o ela ionship
among he wo elemen s (i and j): (a) i a iable i in lu-
enced a iable j ha we deno ed by V (b) i a iable
i was in luenced by a iable j i was deno ed by A (c) i
a iable i and j in luenced on ano he , i was deno ed
by X (d) i a iables i and j a e un ela ed, i was deno ed
by O (Table 2).
A e con i ming he enable s o usage o he Mo-
bile Walle by MSME`s in u al a eas o India, a sel -
s uc u ed in e ac ion (SSIM) ma ix was made o un-
de s and he con ex ual ela ions among he pai o
enable s (P i i e al., 2023; Wa ield, 1974a). Se e al
Table 2: Sel -S uc u ed In e ac ion Ma ix (SSIM)
Code Enable s E10 E9 E8 E7 E6 E5 E4 E3 E2 E1
E1 Secu i y measu es V V O A O A A O A
E2 Awa eness and Adop ion V V V O O V V O
E3 Me chan suppo V V V O O O O
E4 Compa ibili y V O O A O A
E5 Compliance V V V O V
E6 Compa ibili y O O O O
E7 Digi al in as uc u e V V O
E8 Ad ancemen in use in e ace V V
E9 Pe cei ed cos V
E10 Risk ac o
Sou ce: Au ho c ea ion.
e s o 0. (b) In SSIM i he (i, j) en y was shown as A,
hen he (i, j) en y in IRM con e s o 0 and he (j, i)
en y con e s 1. (c) In SSIM i he (i, j) en y was shown
as X, hen he (i, j) en y in IRM con e s o 1 and he (j,
i) en y also con e s o 1. (d) In SSIM i he (i, j) en y is
O, hen he (i, j) en y in IRM con e s o 0 and he (j, i)
en y also con e s o 0.
The ollowing s ep was o make he ini ial eacha-
bili y ma ix (IRM) om SSIM. The SSIM (Table 2) is
changed o IRM (Table 3) by subs i u ing he V, A, X o
O codes in SSIM wi h 0s and 1s. The guidelines used
we e: (a) In SSIM i he (i, j) en y was shown as V, hen
he (i, j) en y in IRM al e s o 1 and he (j, i) en y con-
Table 3: Ini ial Reachabili y Ma ix (IRM)
Code Enable s E1 E2 E3 E4 E5 E6 E7 E8 E9 E10
E1 Secu i y Measu es 0 0 0 0 0 0 0 1 1
E2 Awa eness and Adop ion 1 0 1 1 0 0 1 1 1
E3 Me chan Suppo 0 0 0 0 0 0 1 1 1
E4 Compa ibili y 1 0 0 0 0 0 0 0 1
E5 Compliance 1 0 0 1 1 0 1 1 1
E6 Compa ibili y 0 0 0 0 0 0 0 0 0
E7 Digi al In as uc u e 1 0 0 1 0 0 0 1 1
E8 Ad ancemen in use in e ace 0 0 0 0 0 0 0 1 1
E9 Pe cei ed Cos 0 0 0 0 0 0 0 0 1
E10 Risk Fac o 0 0 0 0 0 0 0 0 0
Sou ce: Au ho c ea ion.
B and B leads o C hen i is belie ed ha A shall lead
oC. So, we checked o his ansi i i y among each
pai o enable s and on iden i ying such ela ionship i
he ins ance ca ied ze o i was changed o 1*. We
could ind wo such ins ances o ansi i i y and in all
such places he alue 0 was eplaced wi h 1*.
The s udy ied o iden i y he ansi i i y among
he s udied a iables and om ha c ea ed he Final
Reachabili y Ma ix (Table 4). The ansi i i y helps o
map he in e - ela ionship be ween he s udied a ia-
bles. The basic p inciple o ansi i i y is: I A leads o
Measu es’ was allo ed le el 2, ‘Compa ibili y’ and
‘Ad ancemen in use in e ace’ we e allo ed le el 3,
‘Me chan Suppo ’ and ‘Compliance’ we e allo ed
le el 4 and ‘Awa eness and Adop ion’, ‘E Li e acy’ and
‘Digi al In as uc u e’ we e allo ed le el 5 (Table 5).
We conduc ed i e i e a ions o he seg ega ing o
le els. We used a inal eachabili y ma ix o assign he
le els o he enable s o usage o he Mobile Walle by
MSME`s in u al a eas o India. Fo ins ance, ‘Pe cei ed
Cos ’ and ‘Risk Fac o ’ we e assigned le el 1, ‘Secu i y
Table 4: Final Reachabili y Ma ix (FRM)
Code Enable s E1 E2 E3 E4 E5 E6 E7 E8 E9 E10
E1 Secu i y Measu es 0 0 0 0 0 0 0 1 1
E2 Awa eness and Adop ion 1 0 1 1 1* 0 1 1 1
E3 Me chan Suppo 0 0 0 0 0 0 1 1 1
E4 Compa ibili y 1 0 0 0 0 0 0 1* 1
E5 Compliance 1 0 0 1 1 0 1 1 1
E6 Compa ibili y 0 0 0 0 0 0 0 0 0
E7 Digi al In as uc u e 1 0 0 1 0 0 0 1 1
E8 Ad ancemen in use in e ace 0 0 0 0 0 0 0 1 1
E9 Pe cei ed Cos 0 0 0 0 0 0 0 0 1
E10 Risk Fac o 0 0 0 0 0 0 0 0 0
Sou ce: Au ho c ea ion.
Table 5: I e a ions o pa i ioning o he le els
Code Enable Reachabili y Se (RS) An eceden Se (AS) In e sec ion
Se RS ∩ AS Le el
E1 Secu i y Measu es 1, 9, 10 1, 2, 4, 5, 7 1 3
E2 Awa eness and Adop ion 1, 2, 4, 5, 6, 8, 9, 10 2 2 5
E3 Me chan Suppo 3, 8, 9, 10 3 3 4
E4 Compa ibili y 1, 4, 9, 10 4, 5, 7 4 4
E5 Compliance 1, 4, 5, 6, 8, 9, 10 2, 5 5 5
E6 Compa ibili y 6 2, 5, 6 6 1
E7 Digi al In as uc u e 1, 4, 7, 9, 10 7 7 5
E8 Ad ancemen in use in e ace 8, 9, 10 2, 3, 5, 8, 8 3
E9 Pe cei ed Cos 9, 10 1, 2, 3, 4, 5, 7, 8, 9 9 2
E10 Risk Fac o 10 1, 2, 3, 4, 5, 7, 8, 9, 10 10 1
Sou ce: Au ho c ea ion.
Using he inal eachabili y ma ix, he p ima y di-
g aph is ob ained (Figu e 1). A dig aph is a isual ep e-
Table 6: Le els assigned o enable s
I e a ion Numbe Le el Enable s in Usage o Mobile Walle
1s I Pe cei ed Cos (E9)
Risk Fac o (E10)
2nd II Secu i y Measu es (E1)
3 d III Compa ibili y (E6)
Ad ancemen in use in e ace (E8)
4 h IV Me chan Suppo (E3)
Compliance (E5)
5 h
Awa eness and Adop ion (E2)
V E illi e acy (E4)
Digi al In as uc u e (E7)
sen a ion o he a iables explo ed and hei in e -
linkages.
Sou ce: Au ho c ea ion.
le el III, E3 and E4 ound hei place a le el IV, and E2,
E4 and E7 we e a le el V. This diag aph was hen used
o de elop a hie a chical amewo k o key enable s o
usage o he Mobile Walle by MSME`s in u al a eas o
India (Figu e 1).
This diag aph is a concep ual model ha shows he
placemen o he ac o s in es iga ed in he s udy and
also shows he in e linkages among hose ac o s. The
diag aph (Figu e 1) clea ly demons a es ha enable s
E9 and E10 ound hei place a Le el I, enable numbe
E1 ound i s place a le el II, E6 and E8 we e placed a
E9 E10
E1
E6 E8
E3 E4
E2 E5 E7
Figu e 1: Diag aph o Enable s o Usage o Mobile Walle by MSME in Ru al India
Sou ce: Au ho c ea ion.
els assigned o enable s we e used o de elop he same
(Figu e 2).
In he In e p e i e S uc u al Modeling (ISM) he
subsequen s ep was o build he model so Figu e 1 and
Table 6 which demons a ed he in e linkages and le -
Table ANNEX 5: I e a ion 5
Code Enable s Reachabili y Se
(RS)
An eceden Se
(AS)
In e sec ion Se
RS ∩ AS Le el
E2 Awa eness and Adop ion 2, 5 2 2 5
E5 Compliance 5 2, 5 5 5
E7 Digi al In as uc u e 7 7 7 5
Sou ce: Au ho c ea ion.
