consumer buying behavior online shopping (2)-report

8/12/2019 Consumer Buying Behavior Online Shopping (2)-Report

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Online Consumer

Buying BehaviourA Market Research

Ankur Mehrotra 2007B10Anshul Kapoor 2007B12

Anuja ua 2007B1!


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Table of Contents

Table of Contents..................................................................................................................2

Executive Summary...............................................................................................................3

Introduction............................................................................................................................5

Objectives..........................................................................................................................5

Primary esearc! Objective...........................................................................................5

Secondary esearc! Objectives....................................................................................5

"et!odolo#y..........................................................................................................................$

Survey %dministration........................................................................................................$

Sam&lin# ..........................................................................................................................$

'ata eduction.................................................................................................................. (

'ata %nalysis.....................................................................................................................(

)indin#s.................................................................................................................................*

COSS T%+,-%TIOS....................................................................................................*

E/ESSIO %%-0SIS...............................................................................................21

%O%............................................................................................................................ 2$

C-,STE %%-0SIS......................................................................................................2(

'ISCI"I%T IT4 C-,STE %%-0SIS.................................................................3

)%CTO %%-0SIS........................................................................................................32

Conclusions.........................................................................................................................3(

%nnexures...........................................................................................................................36

%nnexure 1a7 %##lomeration Sc!edule for Cluster %nalysis...........................................8

%nnexure 1b7 Correlation "atrix for )actor %nalysis........................................................82

%nnexure 1c7 %O% Table for e#ression %nalysis......................................................85

%nnexure 2a7 9uestionnaire for Ex&loratory esearc!....................................................8$

%nnexure 2b7 )inal 9uestionnaire....................................................................................8*

Online Buying Behavior Page 2


3/51

Executive SummaryT!e &roject focused on findin# out t!e Online +uyin# +e!aviour of consumers

bet:een t!e a#e #rou& of 1*;3 years.T!e stated objective of t!e study :as furt!er

brouency of &urc!ases= avera#e s&endin#= factors

affectin# buyin# decision &rocess etc.

T!e ex&loratory researc! :as carried out :it! 2( res&ondents :it! a set of 1 o&en

ended >uestions. T!e ex&loratory findin#s !el&ed us in determinin# t!e


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IntroductionIndia !as t!e :orlds 8 t! lar#est Internet user base= :!ic! crossed t!e 1 million marue to reduce data com&lexity by reducin# t!e

number of variables bein# studies. It !el&s identify latent or underlyin# factors from an array

of seemin#ly im&ortant variables. T!is &rocedure !el&s #ainin# insi#!t into &syc!o#ra&!ic

variables.

Findings

CROSS TA!"AT#O$S

a" Cre#it Car#$ %re&uency o' (urchase

)ull *ypothesis+ %t 65G si#nificance level= o:nin# a credit card does not !ave any

im&act on t!e fre>uency of &urc!ase.

Alternate *ypothesis7 %t 65G si#nificance level= o:nin# a credit card !as an

im&act on t!e fre>uency of &urc!ase.

Online Buying Behavior Page *


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O,nCre#itCar# - %re&o'(urchase Crossta.ulation

Count

%re&o'(urchase /otal

Oncea

Mont

h

2 $!/imes

a

Mont

h

Oncein !

Month

s

Oncein

Month

s

)ever

/rie#

Oncea

Month

O,nCre#itCa

r#

e

s

23 18 2* 11 1 ((

)o 3 2 11 1 5 31

/otal 2$ 1$ 36 21 $ 1*


10/51



11/51

As the p$value 'rom the ta.le is lesser than 0034 ,hich is our assume# level o'

signi'icance4 ,e #o not accept the null hypothesis4 that is4 'or the sample population4

o,ning a cre#it car# has an impact on the 're&uency o' purchase

." 5$.anking$%re&uency o' (urchase

)ull *ypothesis7 %t 65G si#nificance level= e;banuency of &urc!ase.

Alternate *ypothesis+%t 65G si#nificance level= e;banuency of &urc!ase.


Chi$6&uare /ests

alue #' Asymp

6ig 82$

si#e#"

(earson Chi$

6&uare

1*.222aJ 8 .1

9ikelihoo#

Ratio

1(.6$2 8 .1

9inear$.y$

9inear

Association

15.313 1 .

) o' ali#

Cases

1*

a 3 cells 3.GJ !ave ex&ected count less

t!an 5. T!e minimum ex&ected count is

1.(2.

Case (rocessing 6ummary

Cases

ali# Missing /otal

)(ercen

t )(ercen

t )(ercen

t

O,nCre#itCa

r# -

%re&o'(urcha

se

1

*

1.

G .G

1

*1.G


12/51


5:.anking - %re&o'(urchase Crossta.ulation

Count


Once

a

mont

h

2$!

times

a

mont

h

Once

in !

month

s

Once

in

month

s

)eve

r

trie#

Once

a

mont

h

5:.anki

ng

e

s

22 15 3211 *

)o 3 1 $ 1 ( 2(

/otal 25 1$ 3* 21 ( 1(


13/51


Chi$6&uare /ests

alue #' Asymp

6ig 82$

si#e#"

(earson Chi$

6&uare

33.862a

J

8 .

9ikelihoo#

Ratio

32.*85 8 .

9inear$.y$

9inear

Association

2.(3( 1 .

) o' ali#

Cases

1(

a 2 cells 2.GJ !ave ex&ected count

less t!an 5. T!e minimum ex&ected

count is 1.((.


14/51

As the p$value 'rom the ta.le is lesser than 0034 ,hich is our assume# level o'signi'icance4 ,e #o not accept the null hypothesis4 that is4 'or the sample population4

5$.anking has an impact on the 're&uency o' purchase

c" ;en#er$Amount 6pent

)ull *ypothesis7 %t 65G si#nificance level= #ender does not !ave any im&act on

t!e avera#e amount s&ent &er &urc!ase made online.

Alternate *ypothesis7 %t 65G si#nificance level= e;ban


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Chi$6&uare /ests

alue #'Asymp 6ig

82$si#e#"

(earson Chi$

6&uare

2.(*6aJ 8 .568

9ikelihoo# Ratio 2.*11 8 .56

9inear$.y$9inear

Association

.3 1 .655

) o' ali# Cases 1$

a 1 cells 1.GJ !ave ex&ected count less

t!an 5. T!e minimum ex&ected count is 8.*1.

;en#er - Amount6pent Crossta.ulation

Count

Amount6pent/ota

l

9es

s

tha

n

300

300

$

100

0

100

0 $

200

0

200

0 $

300

0

;reate

r than

3000

9ess

than

300

;en#e

r

Male 11 13 6 2( 12 (2

%emal

e

( 8 $ 6 * 38

/otal 1* 1( 15 3$ 2 1$


16/51

As the p$value 'rom the ta.le is greater than 0034 ,hich is our assume# level o'signi'icance4 ,e accept the null hypothesis4 that is4 'or the sample population< gen#er

#oes not have any impact on the average amount spent per purchase ma#e online

Online Buying Behavior Page 1$


Cases

ali# Missing /otal

) (ercent ) (ercent )(erc

ent

;en#er -

Amount6pent1$ 1.G .G 1$

1.

G


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#" ;en#er$%re&uency o' (urchase

)ull *ypothesis7 %t 65G si#nificance level= #ender does not !ave any im&act on

t!e fre>uency of &urc!ase of online &roducts and services.

Alternate *ypothesis7 %t 65G si#nificance level= #ender !as an im&act on t!e

fre>uency of &urc!ase of online &roducts and services.

Online Buying Behavior Page 1(

;en#er - %re&o'(urchase Crossta.ulation

Count


Once

a

Month

2$!

/imes

aMont

h

Once

in !

Months

Once

in

Months

)eve

r/rie#

Once

aMonth

;en#e

r

Male 2 18 2( 6 3 (3

%emal

e

8 2 13 113 33

/otal 28 1$ 8 2 $ 1$

Chi$6&uare /ests

alue #' Asymp

6ig 82$

si#e#"

(earson Chi$

6&uare

11.2(*a

J

8 .28

9ikelihoo# Ratio 11.866 8 .21

9inear$.y$9inear

Association

6.*8 1 .3

) o' ali# Cases 1$

a 3 cells 3.GJ !ave ex&ected count

less t!an 5. T!e minimum ex&ected count

is 1.*(.


Cases

ali# Missing /otal

) (ercen

t

) (ercen

t

) (ercen

t

;en#er -

%re&o'(urcha

se

1

$

1.

G

.G 1

$

1.G


18/51

As the p$value 'rom the ta.le is lesser than 0034 ,hich is our assume# level o'

signi'icance4 ,e #o not accept the null hypothesis4 that is4 'or the sample populationuency of &urc!ase of online &roducts and services.

Alternate *ypothesis7 %t 65G si#nificance level= income of res&ondents !as an

im&act on t!e fre>uency of &urc!ase of online &roducts and services.


=ncome - %re&o'(urchase Crossta.ulation

Count


Once a

Month

2$!

/imes a

Month

Once in

!

Months

Once in

Months

)ever

/rie#

Once a

Month

=ncome

9ess

than

10000

2 1 3

10000$

200001 2 5 1 6

20000$

!00002 2 11 2 1(

!0000$

300005 3 1 6

30000$

1000002 1 1 8

;reater

than

100000

1 1 2 8

/otal 13 8 16 6 1 8$


20/51


Chi$6&uare /ests

alue #'Asymp 6ig

82$si#e#"

(earson Chi$

6&uare2*.6$$aJ 2 .**

9ikelihoo# Ratio 26.(5* 2 .(8

9inear$.y$9inear

Association2.*$ 1 .68

) o' ali# Cases 8$

a 26 cells 6$.(GJ !ave ex&ected count less t!an 5.

T!e minimum ex&ected count is .(.


Cases

ali# Missing /otal

)(ercen

t)

(ercen

t)

(ercen

t

=ncome -

%re&o'(urcha

se

8$1.

G .G 8$ 1.G


21/51

As the p$value 'rom the ta.le is greater than 0034 ,hich is our assume# level o'

signi'icance4 ,e #o not accept the null hypothesis4 that is4 'or the sample populationuency of Purc!ase of

&roducts and services online= o:nin# a Credit Card= "arital Status= Education and %#e= :as

done usin# SPSS. T!e details are as belo:7

aria.les 5ntere#>Remove#8."



22/51

Mo#e

laria.les 5ntere#

aria.les

Remove#Metho#

1"aritalStatus= )re>ofPurc!ase=

Education= CreditCard= %#eaJ. Enter

2 %#e+ac


23/51

2.1*$

5#ucation ;.152 .22 ;.($ ;.(53 .858

Marital6tatus .3*8 .8$8 .6$ .*2* .81

2

8Constant" 2.*6( .612 3.1(* .2

%re&o'(urcha

se.83 .121 .331 3.323 .1

Cre#itCar# ;.(55 .35 ;.258;

2.8((.15

5#ucation ;.138 .166 ;.$( ;.$(1 .58

Marital6tatus .538 .86 .138 1.38 .16$

!

8Constant" 2.5$8 .($2 3.3$8 .1

%re&o'(urcha

se.815 .12 .381 3.8$( .1

Cre#itCar# ;.((2 .33 ;.256;

2.58(.13

Marital6tatus .5$1 .8$ .18 1.3* .1(1

?

8Constant" 3.3$$ .86$ $.(*1 .

%re&o'(urcha

se.8* .12 .335 3.363 .1

Cre#itCar# ;.**2 .268 ;.26(;

3.5.3

a 'e&endent ariable7 %mtS&ent



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25/51

%s can be seen from t!e above table= t!e inde&endent variables can be #radually removed

in t!e re#ression model as t!ey dont !ave any si#nificant im&act on t!e value of 2. T!e

value of 2is >uite lo: and so it can be said t!at t!e re#ression model does not fit into t!edata very :ell. %lso= t!e sum of s>uares of re#ression is lesser t!an t!e sum of s>uares of

residuals and t!is reiterates t!e findin#s of 2. T!is is because if t!e sum of s>uares of

re#ression is lesser t!an t!e sum of s>uares of residuals= t!en t!e inde&endent variables do

not ex&lain t!e variation in t!e de&endent variable :ell. !ile cross tabs su##est a &ositive

relations!i& bet:een multi&le &airs of factors= t!e linear correlation model= :it! all factors

to#et!er= does not fit in :it! t!e outcomes.


5clu#e# aria.les8#"

Mo#e

l

Beta =n t 6ig(artial

Correlation

Collinearity

6tatistics

/oleranc

e

/oleranc

e

/oleranc

e/olerance /olerance

2 Age .*3aJ .$6$ .8*6 .(( .$*2

!

Age .(1bJ .$$ .58$ .$( .$68

5#ucation ;.$(bJ ;.$(1 .58 ;.(8 .6$2

?

Age .122cJ 1.1$8 .28* .12( .*(8

5#ucation ;.*cJ ;.(6* .82( ;.*( .6(1

Marital6tatu

s.18cJ 1.3* .1(1 .15 .62$

a Predictors in t!e "odel7 ConstantJ= "aritalStatus= )re>ofPurc!ase= Education=

CreditCard

b Predictors in t!e "odel7 ConstantJ= "aritalStatus= )re>ofPurc!ase= CreditCard

c Predictors in t!e "odel7 ConstantJ= )re>ofPurc!ase= CreditCard

d 'e&endent ariable7 %mtS&ent


26/51


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Means (lots

Income

Greater than

100000

50000-

100000

30000-500000000-3000010000-0000!ess than

10000

"00

Mean

ofFrequency

3"00

"#0

"60

"40

"0

"00

T!e &;value from t!e %O% table is #reater t!an t!e si#nificance value of .5 assumed

by us. T!us= at t!is si#nificance level :e acce&t t!e null !y&ot!esis. So :e can conclude

t!at income does not !ave an im&act on t!e fre>uency of &urc!ase of online &roducts and

services for t!ese res&ondents. 'o remember t!at t!e same conclusion :as arrived at

:!en Cross tabulation of location and usa#e rate :as &erformed earlier.

C"!ST%R A$A"'S#S

T!e cluster analysis :as run :!ere &eo&le :ere surveyed about t!eir attitudes to:ards

internet s!o&&in#. T!e &references indicated by res&ondents :ere used to find out t!e

consumer se#ments t!at react differently to different &arameters related to online s!o&&in#.

T!e se#ments obtained :ould #ive an understandin# as to !o: t!e consumers are &laced

in terms of t!eir attitudes.

4ierarc!ical clusterin# :as done to determine t!e initial cluster solution. !ile t!e initial

cluster solution by SPSS #ives us 2 clusters= :e taual to 2.(2 form anot!er cluster.o: :e execute t!e M;"ean cluster to #et t!e

final cluster solution and t!rou#! %O% table :e #et t!at all t!e variables bear

si#nificance at 65G confidence level.

Online Buying Behavior Page 2(


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ANOVA

Cluster Error F Sig.

Mean S$uare df Mean S$uare f Mean

S$uare df

&nternet'sMall ("1)0 "515 103 5"#14 "000

!atest&nfo 1"(44 "341 103 5"116 "00#

*++esi,ilit 6"364 "536 103 11"#(4 "000

.on'enien+e ("43) "36( 103 (4"#1) "000

Sa'etime ("4#5 "60# 103 1"31 "000*n/here*ntime ")35 "55) 103 5"55 "00(

.redit.ardSafe 3"441 "61) 103 5"56 "005

Se+ifi+ateime 6"3)3 "553 103 11"554 "000

Guaranteed2ualit 4"3(# "46) 103 )"334 "000

is+ounts )"5#1 "(10 103 13"500 "000

asslefree #"64) "6)1 103 1"51# "000

.ashoneli'er 1"011 "()1 103 1"(# "#3

asFind 5"5#5 "#3# 103 6"66# "00

Fa+edPro,lems "#66 "(30 103 1"1#6 "30)

.ontinue 6"6# "#3 103 #"11 "001

ou+handFeel 5"330 "(# 103 ("30 "001

eli'erPro+ess #"#4 "4)) 103 16"61 "000

o.redit.ard 1"3# ")50 103 13"035 "000

he F tests should ,e used onl for des+riti'e uroses ,e+ause the +lusters ha'e ,een +hosen to maimi7ethe differen+es among +ases in different +lusters" he o,ser'ed signifi+an+e le'els are not +orre+ted for this andthus +annot ,e interreted as tests of the hothesis that the +luster means are e$ual"

Final Cluster Centers

Cluster

1 3&nternet'sMall 3"3# "0) 4"00

!atest&nfo 1"5# 1"(# "05

*++esi,ilit 1"3( 1")4 "1#

.on'enien+e "6 1"#1 3"#6

Sa'etime "33 1"5) "55

*n/here*ntime 1"## "0) "50

.redit.ardSafe "5 "(# 3"1#

Se+ifi+ateime "10 "# 3"00

Guaranteed2ualit ")# 3"56 3"55

is+ounts "15 "( 3"3

asslefree"54 "03 3"1#.ashoneli'er "15 "34 "50

Online Buying Behavior Page 2*


29/51

asFind "00 "63 "6#

Fa+edPro,lems "5 "#1 "5)

.ontinue "40 ")4 3"(

ou+handFeel 1"#1 "53 1")5

eli'erPro+ess "4 "66 3"45

o.redit.ard 4"0# 3"#1 "#

aria.le escription Cluster 1 832" Cluster 28!2" Cluster !822"

I prefer making a purchase from internet than using local

malls or stores

'isa#ree Stron#ly %#ree Stron#ly'isa#ree

I can get the latest information from the Internet regarding

different products/services that is not available in the market.

Stron#ly %#ree %' Stron#ly'isa#ree

I have sufficient internet accessibility to shop online. Stron#ly %#ree "ildly 'isa#ree Stron#ly

'isa#ree

Online shopping is more convenient than in-store shopping. "ildly %#ree Stron#ly %#ree Stron#ly'isa#ree

Online shopping saves time over in-store shopping. 'isa#ree Stron#ly %#ree Stron#ly'isa#ree

Online shopping allows me to shop anywhere and at anytime. Stron#ly %#ree "ildly %#ree Stron#ly'isa#ree

It is safe to use a credit card while shopping on the Internet. Stron#ly %#ree %' Stron#ly'isa#ree

Online shopping provides me with the opportunity to get the

products delivered on specific date and time anywhere asrequired.

Stron#ly %#ree "oderately %#ree Stron#ly

'isa#ree

Products purchased through the Internet are with guaranteedquality.

Stron#ly %#ree Stron#ly 'isa#ree Stron#ly'isa#ree

Internet provides regular discounts and promotional offers tome.


Internet helps me avoid hassles of shopping in stores. %' Stron#ly %#ree Stron#ly'isa#ree

ash on !elivery is a better way to pay while shopping on the

Internet.


"ometimes# I can find products online which I may not find

in-stores.

Stron#ly %#ree Stron#ly 'isa#ree Stron#ly'isa#ree

I have faced problems while shopping online. Stron#ly %#ree Stron#ly 'isa#ree %#ree

I continue shopping online despite facing problems on some

occasions.

Stron#ly %#ree "ildly 'isa#ree Stron#ly'isa#ree

It is important for me to touch and feel certain products before

I purchase them. "o I cannot buy them online.

Stron#ly %#ree Stron#ly 'isa#ree %#ree

I trust the delivery process of the shopping websites. Stron#ly %#ree %#ree Stron#ly'isa#ree



30/51

I do not shop online only because I do not own a credit card. Stron#ly 'isa#ree 'isa#ree Stron#ly%#ree

On t!e basis of t!e above scales= obtained by ratin# t!e relative results= :e can name our

clusters as7

Cluster 1 7 Con'i#ent Online Buyer

Cluster 2 7 @nsure sur'er

Cluster 3 7 Mall 6hopper

D#SCR#M#$A$T )#T* C"!ST%R A$A"'S#S

+y combinin# cluster analysis :it! 'iscriminant analysis= :e could derive a model :!ic!

could &redict cluster members!i& of t!e res&ondents on t!e basis of t!e variables analysed.

T!e cluster solution is c!an#ed :it! M "eans clusterNSave o&tion selected for Cluster

"embers!i&. T!is #ives a ne: column in t!e 'ata S!eet= s!o:in# t!e cluster members!i&

of t!e res&onses. ,sin# t!is data s!eet= 'iscriminant analysis is done over t!e cluster

members!i& column as t!e de&endent variables.



31/51

Eigenalues

a First +anoni+al dis+riminant fun+tions /ere used in the analsis"

!il"s# $am%da

est ofFun+tion8s9

Wilks:!am,da

.hi-s$uare df Sig"

1 through "0( 4#"6( 36 "000 "#) 11("40 1( "000

Canonical &iscriminant Function Coefficients

;nstandardi7ed +oeffi+ients

/he 5igenvalues are greater than 14 an# the ilkDs 9am.a is .elo, 034 this in#icatesthat the iscriminant Mo#el is a.le to eplain the #ata 'airly ,ell

T!e Canonical 'iscriminant )unction Coefficients table contains /rou&;1 number offunctionsD t!e e>uations as derived from above are7

)1 .61Internetvs"allJ ; .6 -atestInfo ; .122 %ccesibility ;.6*6 Convenience .3(

Savetime ; .22* %ny:!ere%nytime L .* CreditCardSafe .2* S&ecific'ateTime ; .$21

/uarentee9uality .85 'iscounts ; .6( 4asslefree .163 Co' ; .3* Easy)ind .26

)acedProblems ; .16* Continue ; .88( Touc!and)eel .3* 'eliveryProcess .*

oCreditCard L 3.3(2

)2 ;.3(2 Internetvs"all .21$ -atestInfo .38$ %ccesibility .(5 Convenience ; .(8(

Savetime .1*5 %ny:!ere%nytime .281 CreditCardSafe .51 S&ecific'ateTime .583

/uarentee9uality ; .232 'iscounts .1* 4assle)ree .2$2 Co' .1$2 Easy)ind .2(2


Fun+tion

igen'alue

% of


32/51

)acedProblems .1(6 Continue .$23 Touc!n)eel .2$2 'eliveryProcess ; .$6

oCreditCard L (.122

Classification Function Coefficients

.luster um,er of .ase

1 3&nternet'sMall 6"0)) "4() 5")61

!atest&nfo 10"113 10"#(1 10"#13

*++esi,ilit -4"03 -3"056 -3"04(

.on'enien+e 5"0 3"()# )"4))

Sa'etime "0(( -"(31 -"61

*n/here*ntime 1"33 "440 1"(05

.redit.ardSafe 5")04 6"6#( 6"(15

Se+ifi+ateime 4"0)0 5"135 6"0#(

Guaranteed2ualit 4"1)0 ("3 5"3#5

is+ounts 1"330 1"(0( "#5

asslefree "116 "15 ")44.ashoneli'er #"33# #"31 )"61)

asFind 1"51) 1"))) "0#(

Fa+edPro,lems 5")10 6"44 6"))4

.ontinue -"6#1 "331 -"()

ou+handFeel 6"0(6 #"#46 ("#5

eli'erPro+ess "46 "0#( 3")0)

o.redit.ard 3"#(# "501 1"551

8.onstant9 -()"#6) -#("(31 -116"5(5

Fisher:s linear dis+riminant fun+tions

he a,o'e ta,le ro'ides the linear is+riminant fun+tion /hi+h +an ,e used to =udge the mem,ershi of ea+hdata set , utting the 'alue in ea+h of them> and +hoosing the one /hi+h ro'ides the highest 'alue"

Classification 'esults(a)

.luster um,er of .ase

Predi+ted Grou Mem,ershi otal

1 3 1

?riginal .ount 1 51 1 0 5

0 3 0 3

3 1 0 1

% 1 )#"1 1") "0 100"0

"0 100"0 "0 100"0

3 4"5 "0 )5"5 100"0

a )#"1% of original groued +ases +orre+tl +lassified"

Our 'iscriminant "odel is able to ex&lain 6*.1G of t!e data set correctly= :!ic! is

si#nificantly !i#!= and indicates t!at t!is is a de&endable 'iscriminant "odel for &rediction.

FACTOR A$A"'S#S

T!e res&onses are &ut into SPSS for data reduction t!rou#! )actor %nalysis. T!e details oft!e above are &rovided belo:7



33/51

/otal ariance 5plaine#

Compone

nt

=nitial 5igenvalues5traction 6ums o' 6&uare#

9oa#ings

Rotation 6ums o' 6&uare#

9oa#ings

/otalE o'

ariance

Cumulative

E/otal

E o'

ariance

Cumulative

E/otal

E o'

ariance

Cumulative

E

1 3.*58 21.81 21.81 3.*58 21.81 21.81 2.556 18.216 18.216

2 2.1(5 12.*2 33.861 2.1(5 12.*2 33.861 2.31* 12.*(6 2(.6*

! 1.$52 6.1(5 82.$$$ 1.$52 6.1(5 82.$$$ 2.1$ 11.66( 36.65

? 1.518 *.813 51.* 1.518 *.813 51.* 1.(2( 6.563 8*.$**

3 1.2(( (.6$ 5*.1($ 1.2(( (.6$ 5*.1($ 1.822 (.61 5$.5*6

1.6* $.11 $8.2($ 1.6* $.11 $8.2($ 1.283 $.63 $3.862

7 1.$$ 5.628 (.2 1.$$ 5.628 (.2 1.2( $.(* (.2

F .*(5 8.*56 (5.56

G .((1 8.2*3 (6.382

10 .(3( 8.65 *3.83*

11 .585 3.3 *$.8$(

12 .526 2.636 *6.8(

1! .3*8 2.138 61.581

1? .3(( 2.62 63.$33

13 .35* 1.6** 65.$21

1 .263 1.$2$ 6(.28(

17 .2(* 1.58$ 6*.(63

1F .21( 1.2( 1.

Extraction "et!od7 Princi&al Com&onent %nalysis.

e see from t!e Cumulative Percenta#e column t!at t!ere !ave been seven com&onents

or factors extracted :!ic! ex&lain (.2G of t!e total variance information contained in t!e

ori#inal 1* variablesJ. T!is is an acce&table solution as #enerally= (G of t!e total variance

s!ould be ex&lained by t!e factors for t!e solution to be acce&ted.



34/51

Rotate# Component Matri8a"

Component

1 2 ! ? 3 7

=nternetvsMall .(18 ;.2(6 .186 .5$ .8$ ;.16( ;.281

9atest=n'o .66 .322 ;.18$ .*8 ;.(2 ;.125 ;.15

Accesi.ility ;.83 .15( .15* .*56 .15 .*8 .2

Convenience .(6$ .85 .22 .2$ .3 ;.(* ;.16

6avetime .(2$ .2( ;.8 ;.15 .1( ;.12 .$5

Any,hereAnytime .318 .5(1 .153 .16 .251 .25 ;.38

Cre#itCar#6a'e .23 ;.8$ .$5( ;.8( .6 ;.2(1 ;.81$

6peci'icate/ime .2(2 ;.16* .8$$ .88 ;.16( .321 .1(

;uarantee#Huality .23 .812 .$8( .1$ .(1 ;.128 .36

iscounts .$6 .(18 .2* .233 .65 ;.65 ;.15*

*assle'ree .$68 .225 ;.22 ;.153 ;.126 .32 ;.1(

Cashonelivery ;.121 ;.28 ;.22 .* .123 .**2 ;.12$

5asy%in# .13 .*$6 .1* .183 ;.83 .55 .12

%ace#(ro.lems ;.23 .2 ;.61 .*8 .(** ;.25 .86

Continue .(1 .3(6 .51* ;.8 .555 .15 ;.((

/ouchan#%eel ;.351 ;.$ ;.$ .(6 ;.58$ ;.163 ;.$5

elivery(rocess .112 .135 .(6$ .$5 ;.11 .12$ ;.56

)oCre#itCar# ;.182 ;.13 ;.8( ;.53 .*8 ;.18( .**5

Extraction "et!od7 Princi&al Com&onent %nalysis.

otation "et!od7 arimax :it! Maiser ormaliAation. a otation conver#ed in * iterations.

-oo


35/51

%actor aria.les 9a.el 'or the %actor

1 Internet over "allConvenienceSaves Time

Time;bound and comfort see


36/51

Online Buying Behavior Page 3$


37/51

Others:

Conclusionse found a stron# inter;de&endence bet:een a fe: variables affectin# online buyin#

be!aviour. )or exam&le= :e found t!at o:nin# a credit card !as a si#nificant im&act on t!efre>uency of online &urc!ases as credit card is t!e most &o&ular mode of &ayment on t!e

Internet. %&art from t!e credit card= E;+anuency of

&urc!ases. %lso= t!e income of an individual does not !ave s!o: any si#nificant relations!i&

:it! t!e fre>uency of &urc!ases. T!ese findin#s are star


38/51

+oo


39/51

Annexures



40/51

Annexure 1a: Agglomeration Schedule for Cluster Analysis

Agglomeration Sc*edule

Stage

ClusterCom%ined

.oeffi+ients

Stage ClusterFirst Appears

etStage

.luster1

.luster

.luster

.luster1

1 105 106 0"000 0 0

16 105 0"000 0 1 4

3 103 104 0"000 0 0 4

4 16 103 0"000 3 6

5 101 10 0"000 0 0 6

6 16 101 0"000 4 5 #

( )) 100 0"000 0 0 #

# 16 )) 0"000 6 ( 10

) )( )# 0"000 0 0 1010 16 )( 0"000 # ) (

11 1) #5 3"000 0 0 31

1 1 )1 4"000 0 0 53

13 #4 #6 5"000 0 0 3

14 31 4) 5"000 0 0 44

15 ) )4 6"000 0 0 0

16 6 )3 6"000 0 0 6

1( 5 #) 6"000 0 0 36

1# 43 #1 6"000 0 0 45

1) 1 65 6"000 0 0 3

0 4 ) ("000 0 15 55

1 5 )0 ("000 0 0 4#

45 46 ("000 0 0 44

3 ( 44 ("000 0 0 4)

4 (0 #( #"000 0 0 35

5 3) # #"000 0 0 46

6 6 6) #"000 16 0 3(

( 4 64 #"000 0 0 (0

# 6 5# #"000 0 0 5(

) 3( #"000 0 0 4)

30 15 33 #"000 0 0 61

31 1) 4( #"500 11 0 36

3 1 #4 )"000 1) 13 5433 30 #3 )"000 0 0 64

34 (3 (4 )"000 0 0 63

35 34 (0 )"000 0 4 54

36 1) 5 )"333 31 1( 55

3( 6 ## )"66( 6 0 50

3# 4 (# 10"000 0 0 53

3) 54 55 10"000 0 0 6(

40 1 53 10"000 0 0 6#

41 5 41 10"000 0 0 45

4 ( 0 10"000 0 0 65

43 (6 () 11"000 0 0 #3

44 31 45 11"000 14 545 5 43 11"000 41 1# (3



41/51

46 3) 11"000 0 5 5#

4( 3 3 11"000 0 0 ()

4# 5 60 11"500 1 0 63

4) ( 11"500 ) 3 6

50 6 3 11"(50 3( 0 5#

51 1# (1 1"000 0 0 )4

5 31 61 1"000 44 0 6

53 1 4 1"000 1 3# 6(

54 1 34 1"50 3 35 61

55 1) 4 1"333 36 0 6#

56 1( 6( 13"000 0 0 #(

5( 6 56 13"000 # 0 64

5# 6 13")33 50 46 (3

5) 50 63 14"000 0 0 66

60 35 4# 14"000 0 0 (0

61 15 1 14"#6 30 54 6)

6 31 14"450 4) 5 (1

63 5 (3 14"500 4# 34 (

64 6 30 14"500 5( 33 (465 ( 5) 15"000 4 0 #1

66 36 50 15"000 0 5) #6

6( 1 54 15"50 53 3) (#

6# 1 1) 15"50 40 55 (1

6) 15 3# 15"##) 61 0 ((

(0 35 4 16"000 60 ( #5

(1 1 16"5 6 6# (4

( 16 5 1("00 10 63 #4

(3 5 6 1("3(5 45 5# ((

(4 6 1("653 (1 64 (#

(5 # (5 1#"000 0 0 )5

(6 ) 40 1#"000 0 0 ##(( 5 15 1#"01( (3 6) #

(# 1 1#"45# 6( (4 #

() 3 ( 1#"500 4( 0 )0

#0 11 5( 1)"000 0 0 100

#1 ( 6 1)"333 65 0 #3

# 1 5 0"361 (# (( #5

#3 ( (6 0"(50 #1 43 #(

#4 16 51 1"(50 ( 0 #)

#5 1 35 "0) # (0 ##

#6 ) 36 "333 0 66 )

#( ( 1( "66( #3 56 )3

## 1 ) 3"(#6 #5 (6 #)

#) 1 16 4"603 ## #4 )

)0 3 #0 4"66( () 0 )3

)1 14 )6 6"000 0 0 )4

) 1 ) ("3 #) #6 )5

)3 3 ( ("50 )0 #( )(

)4 14 1# #"000 )1 51 )6

)5 1 # )"506 ) (5 )6

)6 1 14 30"(53 )5 )4 )(

)( 1 3 3"106 )6 )3 )#

)# 1 10 34"#5 )( 0 100

)) # 6# 36"000 0 0 103100 1 11 3("0# )# #0 101



42/51

101 1 )5 3#"550 100 0 10

10 1 13 43"0#) 101 0 103

103 1 # 43"63( 10 )) 104

104 1 (( 4#"43 103 0 105

105 1 66 66")14 104 0 0

Annexure 1b: Correlation Matrix for Factor Analysis

Correlation Matri

=nter

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all

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43/51

Hualit

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44/51

Conve

nience.

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45/51

el 81 8*( 3 312 (( * 3

elive

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Annexure 1c: A!"A Table for #egression Analysis

A)OA8e"

Mo#el 6um o' 6&uares #' Mean 6&uare % 6ig

1

Regression 35.22 5 (.8 8.36$ .1aJ

Resi#ual 126.(1( *1 1.$1

/otal 1$8.62 *$

2

Regression 38.82( 8 *.$( 5.8* .1bJ

Resi#ual 13.863 *2 1.561

/otal 1$8.62 *$

!

Regression 33.(11 3 11.23( (.1* .cJ

Resi#ual 131.26 *3 1.5*1

/otal 1$8.62 *$

?

Regression 3.( 2 15.35 6.$( .dJ

Resi#ual 138.216 *8 1.56*

/otal 1$8.62 *$

a Predictors7 ConstantJ= "aritalStatus= )re>ofPurc!ase= Education= CreditCard= %#e



46/51

b Predictors7 ConstantJ= "aritalStatus= )re>ofPurc!ase= Education= CreditCard

c Predictors7 ConstantJ= "aritalStatus= )re>ofPurc!ase= CreditCard

d Predictors7 ConstantJ= )re>ofPurc!ase= CreditCard

e 'e&endent ariable7 %mtS&ent

Annexure $a: %uestionnaire for Ex&loratory #esearch

4i=

e are conductin# a researc! on consumer be!avior on t!e internet. %s a &art of our

ex&loratory researc! :e re>uest you to s&are a fe: minutes in ans:erin# t!e >uestions

belo:. 'o feel free to as< us for any clarifications or doubts.

0our res&onses :ould serve as a base to our researc!D and :ould #uide us in discoverin#

im&ortant facts about t!e buyin# &rocess.

T!anuently for t!e follo:in# &ur&oses7a. E;mail7b. C!at7c. S!o&&in#7d. ?ob Searc!7

e. Social et:or


47/51

5. !ile &urc!asin# online= :!at are t!e #oods and services t!at you #enerally buyQ

$. !at are t!e &ayment met!ods you #enerally use for online &urc!asesQ

(. !at are t!e different ty&es of &ayment o&tions t!at you !ave come acrossQ

*. 'o you feel it is safe to buy onlineQ

6. !at are t!e features t!at ma


48/51

Annexure $b: Final %uestionnaire

+!%ST#O$$A#R%

'e (ould be than)ful for your coo&eration if you s&are a fe( minutes to ans(er the follo(ing*uestions:

$. !o you use the Internet regularly%

&es 'o

(. On an average# how much time )per week* do you spend while surfing the 'et%

a* +-( hours b* (-, hours

c* ,-$+ hours d* $+-$ hours

e* reater than $ hours

. !o you use 0-banking%

&es 'o

1. !o you own a credit card%

&es 'o

. I am/would be comfortable buying the following categories of products online2

a* 3ood 4 Beverages b* 5pparels

c* 0lectronic Products d* Books

e* ifts f* 6ickets

g* ar or hotel rental h* Pharmaceutical Products

5ny other product# please specify

,. 7ow frequently do you purchase products/services online%

a* Once a month b* (- times a month

Online Buying Behavior Page 8*


49/51

c* Once in months d* Once in , months

5ny other# please specify

8. 9hat is the average amount that you spend per purchase while shopping online%

a* : ;s ++ b* ;s ++-$+++

c* ;s $+++-;s (+++ d* ;s (+++-;s +++

e* < ;s +++

=. 9hich of the following web sites do you shop at%

a*www.rediff.com

b* www.indiapla>a.com

c*www.indiatimes.com

d* www.ama>on.com

e*www.makemytrip.com

f* www.yatra.com

g*www.ebay.com

5ny other# please specify

?. ;ecall your earlier online buying/shopping e@perience and please indicate you degree ofagreement with the following statements2

"trongly5gree

5gree 'either5gree nor

!isagree

!isagree "trongly!isagree

I prefer making a purchase frominternet than using local malls orstores

I can get the latest information fromthe Internet regarding differentproducts/services that is notavailable in the market.

I have sufficient internet

accessibility to shop online.

http://www.rediff.com/http://www.rediff.com/http://www.indiaplaza.com/http://www.indiatimes.com/http://www.indiatimes.com/http://www.amazon.com/http://www.makemytrip.com/http://www.makemytrip.com/http://www.yatra.com/http://www.ebay.com/http://www.rediff.com/http://www.indiaplaza.com/http://www.indiatimes.com/http://www.amazon.com/http://www.makemytrip.com/http://www.yatra.com/http://www.ebay.com/


50/51

Online shopping is more convenientthan in-store shopping.

Online shopping saves time over in-store shopping.

Online shopping allows me to shop

anywhere and at anytime.

It is safe to use a credit card whileshopping on the Internet.

Online shopping provides me withthe opportunity to get the productsdelivered on specific date and timeanywhere as required.

Products purchased through theInternet are with guaranteed quality.

Internet provides regular discountsand promotional offers to me.

Internet helps me avoid hassles ofshopping in stores.

ash on !elivery is a better way topay while shopping on the Internet.

"ometimes# I can find productsonline which I may not find in-

stores.

I have faced problems whileshopping online.

I continue shopping online despitefacing problems on some occasions.

It is important for me to touch andfeel certain products before Ipurchase them. "o I cannot buy themonline.

I trust the delivery process of theshopping websites.

I do not shop online only because Ido not own a credit card.

'ame2 ender2 Occupation2



51/51

consumer buying behavior online shopping (2)-report

Documents