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RESEARCH PAPER ON
MOBILE WALLETSUBMITTED TO DR! SH"ETA DI#ITSUBMITTED B$
ANAND AGRAWAL 101
KAUSHIK CHAKRAVARTHY 111
SURBHI GARG 121
JANHAVI KOTHARI 131
ABHISHEK NIRGUDKAR 141
SHWETAK SETHI 151
PRANAV TURAKIA 161
Mo%ile &allets are essentiall' di(ital )ersions of traditional &allets
that someone &o*ld carr' in their +oc,et! "hile there are man'
)ariations- *s*all' the' can hold di(ital information a%o*t credit and
de%it cards for ma,in( +a'ments- store co*+ons and lo'alt'
+ro(rams- s+eci.c information a%o*t +ersonal identit' and more so
&e are chec,in( &hat are the main factors that are act*all'contri%*tin( to the +*rchase of mo%ile &allet!
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/0! 1 Usa(e of mo%ile &allet )s occ*+ation
23!43
55!64
32!2
1 USA7E )s Occ*+ation
3 to 8 times 9ess than 2 times More than 8 times
Res*lt The maxim*m *sers are st*dent &ho *se mo%ile &allet
/2! 1 Usa(e )s the ann*al income
23!43
55!64
32!2
1 USA7E )s Ann*al salar'
3 to 8 times 9ess than 2 times More than 8 times
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Res*lt Maxim*m *sers are &hose ann*al salar' is %et&een 8:06 la,h
of income
/3! 1 Usa(e )s! A(e
23!43
55!64
32!2
1 USA7E )s A(e
3 to 8 times 9ess than 2 times More than 8 times
Res*lt The maxim*m *sers are in a(e (ro*+ %et&een 0;:28 i!e! 'o*n(
+eo+le &ho *ses mo%ile &allet
/5! 1 Usa(e )s the fre<*enc' of *sa(e in a month
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23!43
55!64
32!2
1 USA7E )s A(e
3 to 8 times 9ess than 2 times More than 8 times
Res*lt Mostl' +eo+le do online transaction less than 2 times a month
FACTOR ANALYSIS
Through factor anal!"! #$ rat$ th$ factor! #h"ch cu!to%$r!
con!"&$r #h"l$ u!"ng %o'"l$ #all$t!( Th$ follo#"ng #$r$
th$ r$!ult! o'ta"n$& "n S)SS(
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .635
Bartletts !est of Sp"ericity Appro#. $"i-Square 5%.&%6
df 6
Sig. .'''
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A! th$ ,MO -alu$ .0(/3+ "! gr$at$r than 0(/ #$ can go ah$a&
#"th factor anal!"!( Fro% th$ Scr$$ lot #$ o'!$r-$
th$r$ "! onl on$ co%on$nt #ho!$ $"g$n -alu$ "!
gr$at$r than 1( Th"! co%on$nt conta"n! th$ $l$%$nt!
#h"ch ar$ "nt$r&$$n&$nt( Fro% th$ co%on$nt %atr"
#$ o'!$r-$ that Ea!$ of )a%$nt an& ,no#l$&g$ of
4!"ng ar$ th$ &o%"nat"ng factor! a! th$"r contr"'ut"ng
-alu$ "! gr$at$r than 0(5( Saf$t an& Fr$6u$nc of 4!"ng
ar$ l$!! contr"'ut"ng factor!(
C7I S84ARE TEST9
Ch" !6uar$ t$!t #a! carr"$& out to :n& "f th$r$ "! a r$lat"on!h"
'$t#$$n a $r!on;! 6ual":cat"on an& h"! r$f$r$nc$ for
a%$nt !!t$%( Th$!$ a%$nt !!t$% ar$
<$'"t=Cr$&"t car& N$t 'an>"ng CO< an& <"g"tal #all$t(
7o 9 Th$r$ "! no r$lat"on!h" '$t#$$n 6ual":cat"on an&
a%$nt !!t$% u!$&(
719 Th$r$ "! a r$lat"on!h" '$t#$$n 6ual":cat"on an& a%$nt
!!t$% u!$&(
Ch" S6uar$ r$!ult!9
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Chi-Square Tests
1alue df
Asymp. Sig. 2/-
sided
+earson $"i-Square (&.(4%a ) .'46
ikeli"ood atio (&./(( ) .'46
inear-7y-inear Association 3.55& ( .'5)
8 of 1alid $ases ((%
a. (' cells 26/.59 "a:e e#pected count less t"an 5. !"e minimum
e#pected count is ./%.
A! #$ !$$ th$ !"gn":canc$ -alu$ "! 0(0*/ #h"ch "! l$!! than
0(0+( Th$r$for$ #$ r$?$ct 70( Th$r$for$ th$r$ "! ar$lat"on!h" '$t#$$n a $r!on;! 6ual":cat"on an& th$
a%$nt !!t$% u!$&(
ANNOVA
Q3. W !" #$$%&# #$#'(")" *% +)$, *- '#*)%$"-)/ *$ *- !"# %+ ,)*,)*
#, #"- % %)' #''*.
H07 T- #&# %+ #'' *- )" "#
H17 T- #&# %+ #* '#"* *% )" ,)++$*
A$"7
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ANOVA
sage
Sum of Squares df Mean Square Sig.
Bet,een ;roups 6%.3)( 3 //.&)& ()).35& .'''
<it"in ;roups (/.%'& ((/ .((4
!otal %(.()% ((5
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Multiple Comparisons
sage
Sc"effe
2=
family>incom
e
2?
family>incom
e
Mean
@ifference
2=-?
Std.
*rror Sig.
)59 $onfidence =nter:al
o,er
Bound pper Bound
5 lak" to ('
lak"
/ lak" to 5
lak"
-.()355 .'%')/ .(33 -.4/3/ .'36(
a7o:e ('
lak"
-(.46%&5 .'%'/' .''' -(.6)64 -(./4((
7elo, / lak" -/.''''' .('&)6 .''' -/.3'64 -(.6)36
/ lak" to 5
lak"
5 lak" to ('
lak"
.()355 .'%')/ .(33 -.'36( .4/3/
a7o:e ('
lak"
-(./&5/' .'%5// .%% -(.5(&( -(.'333
7elo, / lak" -(.%'645 .(((&4 .)& -/.(/36 -(.4%)3
a7o:e ('
lak"
5 lak" to ('
lak"
(.46%&5 .'%'/' .''' (./4(( (.6)64
/ lak" to 5
lak"
(./&5/' .'%5// .&''. (.'333 (.5(&(
7elo, / lak" -.53(/5
.(((// .6%' -.%46) -./(56
7elo, / lak" 5 lak" to ('
lak"
/.''''' .('&)6 .''' (.6)36 /.3'64
/ lak" to 5
lak"
(.%'645 .(((&4 .&'' (.4%)3 /.(/36
a7o:e ('
lak"
.53(/5 .(((// .6' ./(56 .%46)
. !"e mean difference is significant at t"e '.'5 le:el.
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C7II S84ARE TEST
8+( E$ct of "nco%$ occuat"on g$n&$r an& ag$ on onl"n$
!ho"ng fr$6u$nc!
Inco%$
N*ll H'+othesis H6 There is no relationshi+ %et&een income and
fre<*enc' of online sho++in(!
Alternate H'+othesis H0 Online sho++in( fre<*enc' is si(ni.cantl'
in=*enced %' income of the sho++er!
Th$ follo#"ng #$r$ th$ r$!ult! o'ta"n$& "n S)SS9
Crossta
$ount
=ncome !otal
/ 3 4 (
S"opping>req
/ (5 (& (6 6 54
( (( (3 % % 4'
3 5 (' % ( /4
!otal 3( 4' 3/ (5 ((%
Chi-Square Tests
1alue df Asymp. Sig. 2/-
sided
+earson $"i-Square 5./5&a 6 .5((
ikeli"ood atio 5.545 6 .4&6
8 of 1alid $ases ((%
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>rom the Chi:s<*are test &e can see that the Pearson Chi:s<*are
)al*e?2 sided@ is (reater than !628 ?2@ so &e cannot reect
the n*ll h'+othesis!
Occuat"on
N*ll H'+othesis H6 There is no relationshi+ %et&een occ*+ation and
fre<*enc' of online sho++in(!
Alternate H'+othesis H0 Online sho++in( fre<*enc' is si(ni.cantl'
in=*enced %' occ*+ation of the sho++er!
Th$ follo#"ng #$r$ th$ r$!ult! o'ta"n$& "n S)SS9
Crossta
$ount
Occupation !otal
7ussiness / 3 (
S"opping>req
/ ' /( / 3( 54
( ( (( 4 /4 4'
3 ' (4 ( ) /4
!otal ( 46 & 64 ((%
Chi-Square Tests
1alue df Asymp. Sig. 2/-
sided
+earson $"i-Square %.%%&a 6 .(%'
ikeli"ood atio %.)%4 6 .(&5
8 of 1alid $ases ((%
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>rom the Chi:s<*are test &e can see that the Pearson Chi:s<*are
)al*e?2 sided@ is (reater than !628 ?2@ so &e cannot reect
the n*ll h'+othesis!
$n&$r
N*ll H'+othesis H6 There is no relationshi+ %et&een (ender and
fre<*enc' of online sho++in(!
Alternate H'+othesis H0 Online sho++in( fre<*enc' is si(ni.cantl'
in=*enced %' (ender of the sho++er!
Th$ follo#"ng #$r$ th$ r$!ult!
o'ta"n$& "n S)SS9 Crossta
$ount
;ender !otal
/ (
S"opping>req
/ /3 3( 54
( (4 /6 4'
3 (' (4 /4
!otal 4& &( ((%
Chi-Square Tests
1alue df Asymp. Sig. 2/-
sided
+earson $"i-Square .5)5a / .&43
ikeli"ood atio .6'' / .&4(
8 of 1alid $ases ((%
>rom the Chi:s<*are test &e can see that the Pearson Chi:s<*are
)al*e?2 sided@ is (retare than !628 ?2@ so &e can not reect
the n*ll h'+othesis!
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Ag$
N*ll H'+othesis H6 There is no relationshi+ %et&een a(e and
fre<*enc' of online sho++in(!
Alternate H'+othesis H0 Online sho++in( fre<*enc' is si(ni.cantl'in=*enced %' a(e of the sho++er!
Th$ follo#"ng #$r$ th$ r$!ult! o'ta"n$& "n S)SS9
Crossta
$ount
Age !otal
( / 3 4
S"opping>req
/ 45 & / ' 54
( 3/ & ( ' 4'
3 (5 4 3 / /4
!otal )/ (% 6 / ((%
Chi-Square Tests
1alue df Asymp. Sig. 2/-
sided
+earson $"i-Square (/.463a 6 .'5/
ikeli"ood atio ('.43) 6 .('&
8 of 1alid $ases ((%
>rom the Chi:s<*are test &e can see that the Pearson Chi:s<*are
)al*e?2 sided@ is (retare than !628 ?2@ so &e can not reect
the n*ll h'+othesis!
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FACTOR ANALYSIS
8/ Through factor anal!"! #$ rat$ th$ factor! !af$t $a!$ of
a%$nt >no#l$&g$ of u!"ng an& fr$6u$nc of u!"ngD
#h"ch cu!to%$r! con!"&$r #h"l$ u!"ng %o'"l$ #all$t!(
Th$ follo#"ng #$r$ th$ r$!ult! o'ta"n$& "n S)SS9
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .635
Bartletts !est of Sp"ericity Appro#. $"i-Square 5%.&%6
df 6
Sig. .'''
Component Matrixa
$omponent
(
Safety .5)(
*aseOf+ayment .&5)
Kno,ledgeOfsing .%(%
requencyOfsing .5/3
*#traction Met"od0 +rincipal
$omponent Analysis.
a. ( components e#tracted.
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Ch" S6uar$ r$!ult!9
Chi-Square Tests
1alue df
Asymp. Sig. 2/-
sided
+earson $"i-Square (&.(4%a ) .'46
ikeli"ood atio (&./(( ) .'46
inear-7y-inear Association 3.55& ( .'5)
8 of 1alid $ases ((%
a. (' cells 26/.59 "a:e e#pected count less t"an 5. !"e minimum
e#pected count is ./%.
A! #$ !$$ th$ !"gn":canc$ -alu$ "! 0(0*/ #h"ch "! l$!! than
0(0+( Th$r$for$ #$ r$?$ct 70( Th$r$for$ th$r$ "! a
r$lat"on!h" '$t#$$n a $r!on;! 6ual":cat"on an& th$
a%$nt !!t$% u!$&(
85 Through factor anal!"! #$ rat$ th$ factor! l">$ ca!h 'ac>
&"!count cr$&"t o"nt fr$$'"$! an& oth$r!D #h"ch
"nu$nc$ th$ u!$r! cho"c$ of %o'"l$ #all$t(
Th$ follo#"ng #$r$ th$ r$!ult! o'ta"n$& "n S)SS9
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .6%4
Bartletts !est of Sp"ericity
Appro#. $"i-Square /'5.436
df ('
Sig. .'''
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Communalities
=nitial *#traction
$as">7ack (.''' .&)5
@iscount (.''' .&&4
$redit>point (.''' .&45
ree7ies (.''' .&%'
Ot"er (.''' .&65
*#traction Met"od0 +rincipal $omponent
Analysis.
Total Varian!e "xplained
$omponent =nitial *igen:alues *#traction Sums of Squared oadings
!otal 9 of 1ariance $umulati:e 9 !otal 9 of 1ariance $umulati:e 9
( /.5)3 5(.%66 5(.%66 /.5)3 5(.%66 5(.%66
/ (./66 /5.3/' &&.(%& (./66 /5.3/' &&.(%&
3 .4&& ).544 %6.&3'
4 .3)4 &.%%) )4.6()
5 ./6) 5.3%( (''.'''
*#traction Met"od0 +rincipal $omponent Analysis.
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Component Matrixa
$omponent
+romotional
offers
Ot"ers
$as">7ack .6&5 -.5%/
@iscount .6)6 -.53%
$redit>point .%44 .(%(
ree7ies .%/% .3'&
Ot"er .5'5 .&(5
*#traction Met"od0 +rincipal
$omponent Analysis.
a. / components e#tracted.
>rom the MO anal'sis &e can see that the aiser:Me'er:
Olin Meas*re of Sam+lin( Ade<*ac' is 6!F;5 i!e! (reater than
6!8! Also from the comm*nalities ta%le &e can see that all theextraction )al*es are (reater than 6!F i!e! si(ni.cant! So &e can
+roceed for factor anal'sis! >rom the Total Gariance Ex+lained
ta%le and Scree +lot &e can see that 44!0;41 of the
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information can %e ex+lained %' 2 extracti)e factors and the
res*lt can %e cl*%%ed to 2 s*%sets! >rom the com+onent matrix
&e can name the com+onents as 1( )ro%ot"onal o$r!
Cash %ac,- disco*nt- credit +oint and free%ies! Amon( these
cash %ac,- credit +oint and free%ies ha)e )al*es (reater than
6!; i!e! these are more in=*ential! Disco*nt has )al*e less than
6!;- so this is less in=*ential!
2( Oth$r! others!
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