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Section:A(2009 - 11) ToyLibraryinHyderabad
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Artand
innovation
have
crossed
borders,
when
it
comes
to
educational,
mental
development
and
casual toys. Moreover, everyday innovation has become mandatory to remain in competitive
market has resulted in introduction of huge variety of toys in to the market. So, in order to
understand the consumer psyche and inclination in adopting the concept of Toy Library, thatoffersqualityandbrandedtoysonrent,chargingreasonableamountisthecoreidea,behindthis
projectstudy.Ofcourse,thisbeingabigbusinessidea,itrequiresextensiveresearchfornotonly
consumerperception,butalsotoysprice,brandpreferences,location,services,operationtiming
andbusinessstrategies.Butherewefocusonlyontotheverybasicofthisbusinessventurei.e.
DemandSurveyforToyLibrary;anditsdatacollectionandanalysis.
Factor Analysis and Discriminant Analysis, carried out over the collected data gave us some
insights about Toys Buying Behavior and Crche service adaptation propensity (which may be
includedasaseparateserviceunderToyLibrary).
Executive Summary
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Indias
urban
population
is
the
second
largest
in
the
world,
greater
than
the
combined
urban
populationsofallcountriesexceptChina,theUSandRussia.Overall,consumersspiritsarehigh,
whichhasledtosignificantgrowthinmanysectorsofconsumergoodsandcontinuedgrowthfor
thetoysandgamesmarket.Risingprices,thecostsofchildrenseducationandmedicalcare,and
thedeclineinsharepricesareneverthelessallkeyconcerns.ThetoysandgamesmarketinIndia
stoodatRs29.8billionin2007.Ontheotherhand,thevideogamemarketstoodatRs.4.8Billion
in2007.
Children
are
the
main
focus
of
Indian
families,
and
their
aspirations
in
terms
of
education
and
careerchoicesarerunningveryhigh.Theaveragefamilysize in India isdeclining:asofnow it is
almost4.3whereas inearlieryears itwasmorethan5.Withthereduction intheiraveragesize
andtheincreaseintheirincomes,Indianfamilieshavemoremoneytospend.Theirmainfocusis
their children, and they try their best to fulfill their aspirations. Children are getting more
attentionandparticipatingmoreinthedecisionmakingprocesses.
The US toymaker Mattels recall of thousands of its Chinesemade toys in 2007 had a positive
impact on the Indian market, which includes many manufacturers in the unregistered,
unregulated sector. Very little is known about the manufacturing processes, or even the
manufacturers,asthesedetailsarenotavailabletothepurchasers.Leadingtoycompaniesnow
believe that the increased awareness of quality concerns will prompt buyers to choose better
quality, branded toys. Most toy manufacturers in the unregistered, unregulated sector do not
follow these normsmuch of theoutputof the unbrandedsegment is not marked,according to
industry experts. In 2007, the toy sectors grew at 1820%, with the registered and regulated sector
growingat2225%,comparedwithalowerrateofgrowthintheunregistered,unregulatedsector.
The concept ofToyLibrary is very novice in Indian context,especially from the application andadoptabilitypointofview.Andtherestandsaroomforgrowth,duetogrowingliteratepopulation
andchangingbuyingpatterns,underanimpactofglobalization.AlreadyHyderabadiswellknown
for its Pharmaceutical and HealthCare sector growth. Also it is seen as an emerging IT hub in
southIndia,andnewlydevelopedHiTechcity(whereallITgiantsoperationsareconcentrated)is
Introduction
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Section:A(2009 - 11) ToyLibraryinHyderabad
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growingnotonly intermsofnewentrants,butalso intermsofgeneratingrevenuefrom ITand
ITESservices.Growingskilledandsalariedclasspopulationisaddingconsumerbaseofthecity.
Theoverall
objective
behind
setting
up
aToyLibraryistooffervarietyofeducationaltoys,games,
books, interactive VCDs, CDROMs and computer games to kids of the age 2 to 10 years. Here
overalldevelopmentof kids,onenhancing theirvisual, verbal, intellectual, numericalanalytical,
sensoryandmemoryskillsbyadvancedlearningsystemsandeducationalaidshaskeptonmain
focus. Interactingwitha leading ToyLibrary franchiseprovider,wecame toknow thatat initial
stageitrequiresspaceofaround150Sq.foot;andinlatercourseonecanalsoextendthebusiness
byopeningupKGschool.InthestateofMaharashtra,thismodelhasgainwidespreadpopularity.
Keeping
in
mind
above
scenario,
we
worked
over
the
project
ofToy Library in the city of
Hyderabad.Ofcoursecommenceofbusinessrequiresextensiveresearchandmarketstudy.Butat
thisjuncture, we have restricted our research and analysis to the basic demand survey for Toy
LibraryinHyderabad.
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Aswe
said
earlier,
there
are
number
of
problems,
associated
with
the
business
model;
but
since
wehaverestrictedouranalysis tothebasicdemandsurveyandanalysisourmain focus ison
understanding the toybuying behavior of the consumers in the city. In order to analyze the
feasibility of the business model, we are required to know the consumer perception about the
toys, and their buying behavior. We worked on following aspects of consumer behavior in toy
buying:
Familystructure,earningsourcesandtotalannualhouseholdincome Preferencesinbuyingtoyseducational,mentaldevelopmentandcasual Attributesoftoyscustomerlooksforwhilebuyingtoys Buyingfrequencyandannualspendingontoys Preferenceoverbrandedandnonbrandedtoys Likenessoftheconceptoftheservicesandpropensitytoadopttheservices
In order to find the correlation between the attributes (variables) we covered in our analysis, we will
perform
factor
analysis
and
so
will
form
factors
(clubbing
relevant
variables).
These
factors
will
structure
thebaseforourfurtherbusinessresearchanalysis.
Alsowemadeanattempttodiscriminatetheattributesaffectingthedecisionofavailingservicesofcrche.
SincethebusinessmodelofToyLibrary,isascalablebusiness,ourtheseanalysiswillhelpusinclassifying
customersintogroupswhomaygoforcrcheservicesandwhomaynot.
Problem Analysis
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Sinceunderstanding
the
consumer
preferences
and
forces
in
buying
toys,
is
the
main
concern
of
the project; our overall questionnaire is designed based upon that only. It has already been
discussedindetail,aboutthequestionnairedesignandmodificationsbaseduponpilotsurveys.
WhileconductingtheFactoranalysis,followingresponseascribedrespectivequestionsbuildthe
foundation:
1. Ageofakid2. Annualhouseholdincome3. Importancetotoysfor
a. Educationpurposesb. Mentaldevelopmentpurposesc. Casualpurposes
4. Influenceoftoybuyingdecisionbya. Selfb. Spousec. Kidsdemandd. Neighbore. Colleague/friendofrespondent
5. LikelinessoftheconceptofToyLibrary6. PropensityofadoptionoftheservicesofToyLibrary
While conducting the Discriminant analysis, following questions responses will built the
foundation:
1. Ageofthekid2. Familymembers(adult)3. Annualhouseholdincome4. Whethertheyusecrcheornot5. Annualspendingfortoys
Questionnaire Design
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Thissurvey
requires
the
exercise
to
be
carried
out
in
weekends,
when
people
are
relaxed,
and
visit
shopping malls and gardens. Our focus was the people having kids in the age groupof 2 to 10
years. Further to this, we targeted those areas (shopping malls and gardens) only in order to
assure availability of respondents of required category, in sufficient quantity. Due to political
agitation over the division of the state of Andhra Pradesh, we could not reach to the people
personallyinweekends.Butbaseduponourlocalcontactsinthecity,wecouldapproachpeople
workinginITandHealthCaresectortosomeextentviaemails.Hence,ourentiredatacollection
wasmadeusingtheinternettoolsandGoogleSpreadsheet.
Thisexerciseofferedusfollowingbenefits,overandabovegettinggenuineresponses:
We could approach young and married tech savvy people, who are, in real sense theoriginalconsumersoftheservices.
Preparing spreadsheet on Google Documents is a cakewalk. Thus, it saved not only ourtimeandenergy;butalsoitwascosteffectivesincewehardlyrequiredgettinganyform
printed.
We mailed the link of the spreadsheet to the respondents; so it was purely on theirconveniencetofilltheformandrespondthesurvey.So,weexpectgenuineresponses.
TheresponsesofwereautomaticallygetregisteredintheGoogleResponseSheet,inMSExcel format. Thus, it was quite convenient to convert them into code, and record the
same.Thus,hereitsavedourtimeindataentryaswell.
Field-Work and Data Collection
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FactorAnalysis
ThepurposeofFactorAnalysis inourcase istodoanexploratorystudysincewedonothaveapredefinedideaofthestructureorhowmanydimensionsareinthesetofvariableswehaveviz.
Importanceofeducation,MentalDevelopmentandCasualType,Price,QualityandBrand.
Outputanalysis:
Data Analysis
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At this stage all Variables correlate fairly well and none of the correlation coefficients are
particularly large and we cannot eliminate any of the variables. Had any of the variable been
highlycorrelatedtoothervariableswewouldhaveconvenientlydroppeditasitwouldbedifficult
forustoputitunderonefactorandsuppressingitsdependencyonothersaswell.
Nowlets
have
alook
at
the
determinantvalue.Ifthisvalueisgreaterthan0.00001,wesaymulti
colinearityisnotaproblem.Inourcaseitis0.140whichishigherthan0.00001.Sowecandeduce
thatinourcaseatthisstagethereisnoneedtodropanyvariables.
KMOandBartlettsTest
KMOstatisticvariesbetween0and1.Ifitiscloserto1ourdatacanbeeasilyclassifieddistinctly
intofactorsbecausethepatternsofcorrelationarecompactandfor0wesaythefactoranalysis
would
not
give
appropriate
results
as
the
pattern
of
correlation
is
diffused
and
hence
is
difficult
to
segregatethevariablesintofactors.
Accordingtostandards:
KMOgreaterthan0.5isAcceptable.
0.50.7Good
0.70.9Excellent
0.91.0Desired.
OurCase:
KMOandBartlett'sTest
KaiserMeyerOlkinMeasureofSamplingAdequacy..681
Bartlett's
Test
of
SphericityApprox.
Chi
Square
294.094
df 55
Sig. .000
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Inourcaseitis0.681whichisfairenoughandwhichgivesusthumbsuptogoaheadwithfactor
analysis.
IntheabovetablewecanseeBartlett'sTestofSphericitywhichisactuallyfortestingthefollowingNULLHypothesis.Ho:TheCorrelationmatrixisanIdentitymatrixi.e.thereisnocorrelationbetweenthevariables,
soeachvariableitselfisaFactor.
Since for factoranalysistoworkweshouldhavesomerelationshipsamongthevariables,and if
matrix is Identity we would have all the correlation coefficients zeroes. Hence in order to do a
FactoranalysisweshouldbeabletorejecttheNullHypothesis.
Inourcaseasseenaboveinthetablepvalue(0.000)
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Compone
nt
InitialEigenvalues
ExtractionSumsofSquared
Loadings
RotationSumsofSquared
Loadings
Total
%of
Varianc
e
Cumulati
ve% Total
%of
Varianc
e
Cumulativ
e% Total
%of
Variance
Cumulati
e%
7
.682
6.200
82.393
8 .583 5.296 87.689
9 .523 4.754 92.443
10 .491 4.463 96.906
11 .340 3.094 100.000
ExtractionMethod:PrincipalComponentAnalysis.
NowsincewehaveaskedSPSStoextractfactorswitheigenvalue>1,itpulledthefirstfourhaving
2.697,1.698,1.349and1.046astheirEigenvaluesaregreaterthan1,whichgivesusfourfactors.
The eigenvalue associated with these four factors are again displayed in the after extraction
columnas
well
dropping
all
other
insignificant
factors.
But
in
our
case
we
see
the
cumulative
%
accountstoonly61.732% i.e.theextracted factorscanonlyexplain61.732%variance inthe11
variables.
Ifwegoby theoutputofSPSSwecan reduce thecomplexityof thedatasetby using the four
componentsloosingabout38%oftheinformation.
OneofthetableswhichareofsignificanceistableofCommunalities.
Forourcaseitis:
Communalities
Initial Extraction
Age 1.000 .650
Income 1.000 .523
EducationPurpose 1.000 .688
MentalDevelopment 1.000 .853
Casual 1.000 .482
ImportantPrice
1.000
.480
ImportantQuality 1.000 .348
ImportantBrand 1.000 .623
AnnualSpendingontoys 1.000 .529
Likingoftheservices 1.000 .805
propensitytousetheservices 1.000 .810
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ExtractionMethod:PrincipalComponentAnalysis.
Thetableshowscommunalitiesbeforeandafterextraction.
The
common
variance
of
each
variable
summarized
by
the
factors,
or
the
amount
(percent)
of
eachvariablethatisexplainedbythefactors. Communalityisactuallythepercentageofvariationinvariablethatisexplainedbythefactors(inourcasefour).Wecansaythefourfactorscandefine65%variance inAgevariable.Sinceafterextractionsome factorsaredropped,thisvalue65%
hascomedownowingtolossofinformation.
Nowtodecidewhatthefourcomponentsconsistsofwefollowthebelowsteps:The
rotated
component
matrix
helps
to
determine
what
the
components
represent
i.e.
which
of
thevariablescomeunderthecomponents.
RotatedComponentMatrix(a)
Component
1 2 3 4
ImportantBrand .776 .115
Income .701 .151
ImportantPrice
.669
.144
AnnualSpendingontoys .658 .302
ImportantQuality .508 .147 .208 .156
propensitytousetheservices.899
Likingoftheservices .894
EducationPurpose .135 .141 .778 .212
Age .121 .774 .177
Casual .430 .535
MentalDevelopment .922
ExtractionMethod:
Principal
Component
Analysis.
RotationMethod:VarimaxwithKaiserNormalization.
ARotationconvergedin4iterations.
InthistableweareconsideringonlytheAbsoluteValues,sothe firstcomponent ismosthighly
correlated with ImportantBrand, Income , ImportantPrice, Annual Spending on toys and
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ImportantQuality.SinceImportantBrandisnotwellcorrelatedwith2nd
,3rd
or4th
component,
it better represents the 1st component. The second component is most highly correlated with
propensity to use the services and hence it becomes the representative of 2nd
component.
ThirdcomponentwithhighlycorrelatedwithEducationPurpose,AgeandCasual,outofthesewe
can
select
Education
Purpose
as
the
representative.
Finally
in
the
same
way
Mental
Development
weselectitastherepresentativeofthefourthcomponent.
Ifwehavetoselectonerepresentative fromeachfactorweselectImportantBrand,propensitytouse the services, EducationPurpose andMentalDevelopmentwhichwe canuse in thefutureanalysis.
Component Score Variables: Using the component coefficients given in theComponentScore
CoefficientMatrix and thevaluesofeachvariable foreachobservationwederiveanequation.
Usingthis
equation
we
calculate
Component
Score
for
each
component.
ComponentScoreCoefficientMatrix
Component
1 2 3 4
Age .123 .046 .531 .163
Income .312 .019 .159 .090
EducationPurpose .063 .095 .508 .176
MentalDevelopment .072 .044 .027 .865
Casual.111
.026
.301
.042
ImportantPrice .282 .056 .008 .115
ImportantQuality .209 .077 .069 .182
ImportantBrand .335 .000 .028 .145
AnnualSpendingontoys .272 .023 .106 .231
Likingoftheservices .003 .535 .014 .055
propensitytousetheservices .000 .537 .041 .014
ExtractionMethod:PrincipalComponentAnalysis.
RotationMethod:VarimaxwithKaiserNormalization.
Component
Scores.
Forexample:forComponentScore1= (.123)*Age+(.312)*Income+(.063)*EducationPurpose
+(.072)*MentalDevelopment+ (.111)*Casual+(.282)*ImportantPrice+(.209)*Important
Quality + (.335)* ImportantBrand+ (.272)* Annual Spending on toys + (.003)* Liking of the
services+(.000)*propensitytousetheservices.
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TheResultingfourComponentScoreVariablesarerepresentative of,andcanbeusedinplaceofelevenvariables,withalossof38%oftheinformation.Hencewehaveactuallyreducedthedatafilefrom11variablesto4componentscorevariables.
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DiscriminantAnalysis
AnalysisCaseProcessingSummary
UnweightedCases N Percent
Valid 155 .2
Excluded Missingoroutofrangegroupcodes 0 .0
Atleastonemissingdiscriminating
variable0 .0
Bothmissingoroutofrangegroupcodes
andatleastonemissingdiscriminating
variable65380 99.8
Total
65380 99.8
Total 65535 100.0
"Processed"casesarethosethatweresuccessfullyclassifiedbasedontheanalysis.Thereasons
whyanobservationmaynothavebeenprocessedarelistedhere. Here0.2%ofthecasescould
notbesuccessfullyclassified.
EigenvaluesandMultivariateTests
Eigenvalues
Function Eigenvalue %ofVariance Cumulative% CanonicalCorrelation
1 .131(a) 100.0 100.0 .340
a First1canonicaldiscriminantfunctionswereusedintheanalysis.
Herewehavegotonly2discriminatingvariable.Henceweuseonly1function. Eachfunctionacts
as projections of the data onto a dimension that best separates or discriminates between the
groups.
Eigenvalue The magnitudes of the eigenvalues are indicative of the functions' discriminating
abilities
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% of variance: This is the proportion of discriminating ability of the three continuous variables
found in a given function. This proportion is calculated as the proportion of the function's
eigenvaluetothesumofalltheeigenvalues.
Inourcasewehavegotonly1discriminatingfunction.Hence100%
.
Wilks'Lambda
TestofFunction(s) Wilks'Lambda Chisquare df Sig.
1 .884 18.681 2 .000
WilksLambda
shows
the
proportion
of
the
total
variance
(90%)
in
the
Discriminant
scores
not
explainedbydifferencesamonggroups.AsmallLambdavalue(near0)indicatesthatthegroups
meanDiscriminantscoresdiffer.Wilks'lambdaisadirectmeasureoftheproportionofvariancein
thecombinationofdependentvariablesthatisunaccountedforbytheindependentvariable(the
grouping variable or factor). If a large proportion of the variance is accounted for by the
independentvariablethen itsuggeststhatthereisaneffectfromthegroupingvariableandthat
thegroups havedifferentmeanvalues.Wilks'lambdaperforms,inthemultivariatesetting,witha
combinationofdependentvariables,thesameroleastheFtestperformsinonewayanalysisof
variance.
Chisquare This is the Chisquare statistic testing that the canonical correlation of the given
functionisequaltozero. Inotherwords,thenullhypothesisisthatthefunction,andallfunctions
thatfollow,havenodiscriminatingability. ThishypothesisistestedusingthisChisquarestatistic.
DiscriminantFunctionOutput
StandardizedCanonicalDiscriminantFunctionCoefficients
Function
1
Whoalldostaywithyoucurrently,exceptkid(s)? .836
Totalannualhouseholdincome.764
Inourcase,wefindthatannualhouseholdincomeisgreaterinmagnitudethan,whoallstaywith
kids.Henceitwillhavegreaterimpactonthediscriminantscore.
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ClassificationProcessingSummary
Processed 65535
Excluded Missingoroutofrangegroup
codes
0 Atleastonemissing
discriminatingvariable65380
UsedinOutput 155
ClassificationProcessingSummary ThisissimilartotheAnalysisCaseProcessingSummary,but
inthistable,"Processed"casesarethosethatweresuccessfullyclassifiedbasedontheanalysis.
ClassificationResults
Do
you
use
Crche
foryourkid(s)? Predicted
Group
Membership Total
1 2
Original Count 1 18 8 26
2 40 89 129
% 1 69.23076923 30.76923 100
2 31.00775194 68.99225 100
Thesearethepredictedfrequenciesofgroupsfromtheanalysis. Thenumbersgoingdowneach
column indicate how many were correctly and incorrectly classified. In our case for yes group
66%werecorrectlyclassified.And28wereincorrectlyclassified.
Original These are the frequencies of groups found in the data. Here we see form frequency
tablethatforYeswehadcountof26.Heretoowefindthat18werepredictedcorrectlyand8
werenot.
% Thisportionofthetablepresentsthepercentofobservationsoriginallyinagivengroup(listed
intherows)predictedtobeinagivengroup(listedinthecolumns). Inourcase31%ofYesgroup
is predicted to be in the No group. However 30.76% of No group is predicted to be in the Yes
group
Analysis
From thestructurematrix itcanbe interpretedthat Noofmembersstayingwiththekid isan
important variable in determining whether to have a crche or not. It has got a value of 0.667
whichishigherthananyoftheothervariables.
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AlsotheWilks lambdahere is .884whichtransforms tochisquareof18.681with2degreesof
freedomandlevelofsignificance
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1. Fourfactorsemergedoutofavailable11variables;viza. Socialstatus
i. Using these factors we can work further on Consumer behavior of aparticular class people, Location, Toys choices (Brand) and Marketing
strategies
b. Adoptionoftheservicesi. Sinceourresultssay,thosewholiketheconceptexpressedtheirwillingness
toavailtheservices.Hence,moreemphasiscanbeputonmakingtheidea
moreattractive,
offering
abroad
range
of
services
and
assuring
sufficiently
bigtoyscollection.
c. Intellectualleveli. ItshowsAgeandToyspurchaseforeducation&casualpurposearehighly
correlated with each other. So, for us its essential to form a structure of
variousagegroupsofkids,andbuytoyskeepinginmindtheirAge.
d. Mentaldevelopmenti. It shows irrespective of any age, income, price, quality or brand while
buyingtoys,Mentaldevelopmentisgivenadueimportance.
2. DiscriminantAnalysissaysfollowings:a. WemaygoforofferingCrcheservices,targeting:
i. Nuclearfamiliesii. Familieswithhighdisposableincome
b. BeforewegoformakingavailCrcheservices,weneedtoassurethedemandofthesame.ThedemandsurveytoCrcheservicemayhelpinscalingthebusinessin
furthercourse.
Conclusion
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1. http://www.toysindia.in/indiantoy_industry.html2. www.euromonitor.com/Toys_And_Games_in_India3. http://kidsgurukul.com/4. http://ww.smashits.com/video/snoop/1605/hyderabadstoylibraryisahitwithits
children.html
References