Moving Beyond Market Research: Demystifying Smartphone User Behavior in India

27 Pages·2017·3.26 MB·English

Moving Beyond Market Research: Demystifying Smartphone User
82
Behavior in India
AKHILMATHUR,NokiaBellLabs
LAKSHMIMANASAKALANADHABHATTA,ShivNadarUniversity
RAHULMAJETHIA,ShivNadarUniversity
FAHIMKAWSAR,NokiaBellLabs
Large-scalemobiledatastudiescanrevealvaluableinsightsintouserbehavior,whichinturncanassistsystemdesignersto
createbetteruserexperiences.Afteracarefulreviewofexistingmobiledataliterature,wefoundthattherehavebeennolarge-
scalestudiestounderstandsmartphoneusagebehaviorinIndia–thesecond-largestandfastestgrowingsmartphonemarket
intheworld.WiththegoalofunderstandingvariousfacetsofsmartphoneusageinIndia,weconductedamixed-method
longitudinaldatacollectionstudythroughanAndroidappreleasedonGooglePlay.Ourappwasinstalledby215users,and
logged11.9milliondatapointsfromthemoveraperiodof8months.Weanalyzedthisrichdatasetalongthelinesoffour
broadfacetsofsmartphonebehavior–howusersusedifferentapps,interactwithnotifications,reacttodifferentcontexts,
andchargetheirsmartphones–topaintaholisticpictureofsmartphoneusagebehaviorofIndianusers.Thisquantitative
analysiswascomplementedbyasurveywith55usersandsemi-structuredinterviewswith26userstodeeplyunderstand
theirsmartphoneusagebehavior.Whileourfirst-of-its-kindstudyuncoveredmanyinterestingfactsaboutIndiansmartphone
users,wealsofoundstrikingdifferencesinusagebehaviorcomparedtopaststudiesinothergeographicalcontexts. We
observedthatIndianusersspendsignificanttimewiththeirsmartphonesaftermidnight,continuouslychecknotifications
withoutattendingtothemandareextremelyconsciousabouttheirsmartphones’battery.Perhapsthemostdramaticfinding
isthenatureofmobileconsumerismofIndianusersasshownbyourresults.Takentogether,theseandtherestofourfindings
demonstratetheuniquecharacteristicsthatareshapingthesmartphoneusagebehaviorofIndianusers.
CCSConcepts:•Human-centeredcomputing→Empiricalstudiesinubiquitousandmobilecomputing;
GeneralTerms:UserStudy,UsageBehavior,India,SmartphoneUsage
AdditionalKeyWordsandPhrases: SmartphoneUsagePatterns, UserBehavior, India, Notifications, BatteryCharging,
ContextualUsage
ACMReferenceformat:
AkhilMathur,LakshmiManasaKalanadhabhatta,RahulMajethia,andFahimKawsar.2017.MovingBeyondMarketResearch:
Demystifying Smartphone User Behavior in India. PACM Interact. Mob. Wearable Ubiquitous Technol. 1, 3, Article 82
(September2017),27pages.
DOI:10.1145/3130947
1 INTRODUCTION
Mobilephoneshavetransformedfrombasiccommunicationtoolsintopowerfulinformation,communication,
sensingandentertainmentdevices.Itisprojectedthatbytheyear2020,5.4billionpeopleintheworldwillhave
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©2017Copyrightheldbytheowner/author(s).PublicationrightslicensedtoACM. 2474-9567/2017/9-ART82$15
DOI:10.1145/3130947
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82:2 • AkhilMathur,LakshmiManasaKalanadhabhatta,RahulMajethia,andFahimKawsar
amobilephone–morethanthoseprojectedtohaveelectricity(5.3billion),runningwater(3.5billion)orcars
(2.8billion)[2].Thisrapidgrowthinsmartphoneownership,coupledwiththeemergenceofappdistribution
channelssuchasGooglePlayandApple’sAppStore,hasmadeitpossibleforappdeveloperstoreachmillionsof
usersaroundtheworldwithvaryinggeographical,social,economicandculturalbackgrounds.
Tostudysmartphoneusagepatternsamongusers,ubicompresearchershavealsoleveragedthesameapp
distributionchannelsandconductedvariouslargescale,in-the-wildstudieswithrealsmartphoneusers.Falakiet
al.[13]conductedacomprehensivestudyofsmartphoneusagetocharacterizetheimpactofuserinteractionswith
thedeviceonnetworkandenergyconsumption.Ferreiraetal.[15]analyzedthecontextualnatureofapplication
micro-usage,andfoundthatsocialapplicationsaretheprimarytriggersforuser-initiatedmicro-usagesessions.
Otherworkshavestudiednotificationdeliveryonsmartphones[29,31,37],mobileenergyconsumption[22],
andpredictionofnextappuse[39].
Despitetheincreasedresearchactivityinthisspace,weobservethattherehavebeennomobiledatastudies
aimedatunderstandinguserbehaviorinoneofthemajorsmartphonemarketsintheworld,namelyIndia.Most
oftheexistingmobiledatastudies(e.g., [13,32])wereconductedwithusersinWesterncountriesandthustheir
findingsmaynotreflecttheuserbehaviorinIndia. Akeyreasonforthismajorgapinmobiledataliterature
isthatthesmartphonemarketinIndiahasonlymaturedinthelastfewyears.Priortothat,themobilephone
marketwasdominatedbylow-endfeaturephoneswhichhaddulypromptedanumberofHCIstudies[16,25]
ondesigningtechnologysolutionsforresource-constraineddevices.Onlyinthelastfewyearshastherebeen
massivesmartphoneadoptioninIndia,makingitcurrentlythesecondlargestsmartphonemarketandthecountry
withthehighestAndroiddeviceusagetimeintheworld[5].Thissuggeststhatsmartphoneshaveindeedbecome
ubiquitousinIndia,anditisanopportunetimetostudyhowusersinIndiaareinteractingwiththeirsmartphones.
In addition to the large user base, there is another reason that makes it interesting to study smartphone
behaviorinanIndiancontext:ononehand,IndiahasalargeurbanEnglish-speakingpopulation,manyofwhom
areemployedintheglobaltechnologyindustryandwhosesmartphoneusagepatternsmightoverlapwithglobal
usagepatterns.However,therearealsomajordifferencesintermsofinfrastructureavailability(e.g.,electricity
supply,internetspeeds)andeverydayculturalandsocialnorms,whichmightleadtouniquevariationsinuser
behaviorinthiscontext.Assuch,wearguethatitiscriticalformobiledevelopersandubicompresearchersto
obtainanin-depthunderstandingofhowusersinIndiainteractwiththeirsmartphones,soastodesignbetter
mobilesystemsandexperiencesforthefastestgrowingsmartphonemarketintheworld.
Inthispaper,wepresentamixed-methodlongitudinalstudywhichprovidesaholisticviewofthesmartphone
usagebehaviorofIndianusers.Our8-monthstudywasconductedthroughanappreleasedonGooglePlay.A
totalof215AndroidsmartphoneusersfromIndiaparticipatedinthestudy,generatingnearly11.9milliondata
pointsrelatedtosmartphoneusageandcontext.Thisquantitativedataloggingwasfollowedbyanonlinesurvey
of55usersandin-depthinterviewsof26usersfromtheoriginaluserpooltofurtherunderstandtheirmobile
usagebehavior.AsthisisthefirsteverlongitudinalubicompstudyinanIndiancontext,ourgoalinthispaperis
topaintabroadpictureofthesmartphoneusagebehaviorofIndianusersratherthanunderstandingaparticular
micro-behavioralpatternaboutthisgroup.Tothisend,ourdataanalysisfollowsahighlyexploratoryapproach,
whereinweanalyzethecollectedsmartphoneusagedatathroughfourbroadlensesthatarehighlyrelevantfor
theubicompcommunity:
• ApplicationUsageAnalysis: AccordingtothemarketresearchfirmAppAnnie[1],Indianusersrecorded
thehighestnumberofAndroidappdownloads(6.2billionapps)intheworldin2016. Weexplorethe
temporalvariationsinappusageofthisusergroup,uncoverthemotivationsbehindinstallation,usage
anduninstallationofcertainapps,andanalyzetherelationshipbetweenusageofvariousappcategories.
• NotificationAnalysis: Inthemodernsmartphoneusageparadigm,notificationsservetheverycrucialrole
ofpromotingcontentawarenessbyalertinguserstonewlyavailableinformation.Therefore,weanalyze
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thereceptivityofIndianuserstowardsmobilenotifications,andexploreifnotificationdeliverycontext
hasanyimpactonitsreceptivity.
• ContextAnalysis: Asoundunderstandingofuserbehaviorindifferentcontextscanhelppractitionersin
designingadaptableubicompsystems.Assuch,weexploretheroleofpersonalcontext,devicecontext,
andsocio-economiccontextinshapingthesmartphoneusageofIndianusers.
• ChargingBehaviorAnalysis: Human-batteryinteractionhasbeenanimportantresearchtopicinubicomp
research. By accurately understanding the battery charging preferences of users, mobile apps can
intelligentlyscheduletheirenergy-heavyoperations.Inthisvein,weanalyzethedurationandtemporal
distributionofchargingsessionsforIndianusers,aswellastheimpactofbatterylevelsonusers’decision
tochargetheirphones.
WhileuncoveringthesmartphoneusagepatternsofusersinIndiaistheprimarygoalofthispaper,ourwork
alsobuildsupontherecentmobiledataresearchinthecommunity.Inarecentcritiqueonmobiledatastudies,
Churchetal.[8]arguedthatubicompandHCIresearchcommunitiesshouldencourage“reproducingofmobile
datastudiesindifferentpartsoftheworld,withdifferentuserpopulationsandatdifferentpointsintime”asthis
willenableustocombineandcontrastthefindingsfromvariouscontextsandgeographies,andbuildabetter
andmorecompleteunderstandingofuserbehavior.Wefundamentallyagreewiththispositionandtherefore,in
thispaper,inadditiontothoroughlyanalyzingthesmartphonebehaviorforusersinIndia,wealsocontrastit
againstpublishedmobiledatastudieswhichwereconductedinothergeographicalsettings.Morespecifically,we
highlightthesimilaritiesanddifferencesinuserbehavioracrossgeographies,andshowthatifthesenuances
arenottakenintoaccountwhiledesigningdata-drivenmobilesystems,theperformanceofthesesystemscould
significantlydegradein-the-wild.
Someofourmostinterestingfindingsare:a)usersinIndiaareextremelyconsciousabouttheirsmartphone’s
batterylevel–smartphonesarechargedveryfrequentlyinordertomaintainahighbatterylevel,andnearly50%
ofthechargingsessionshappenwithin80minutesofthelastsession,b)whileusersareremarkablyquickto
glanceatincomingnotificationsontheirdevices,theattendancerateofnotificationsremainsverylow,c)the
temporalpatternsofappusageamongIndianusersareinstarkcontrastwiththefindingsofpriormobiledata
studiesinWesterncontexts,andfinallyd)wefoundevidenceinourdatathatthe‘app-only’businessmodel
pitchedbye-commerceprovidersinIndiagoesagainstthebehavioralpatternsoftheusers.
Insummary,thispapermakesthefollowingcontributionstomobiledataliterature:
• WepresentthefirsteverlongitudinaldatacollectionstudyanalyzingsmartphoneusagepatternsinIndia,
fromtheperspectiveoffourkeyfacetsofsmartphoneusage.
• OurfindingsthrowlightonseveralpreviouslyunknownaspectsofsmartphoneuserbehaviorinIndia,
andofferaholisticunderstandingofthefastestgrowingsmartphonemarketintheworld.
• Wepresentadetailedanalysisofthevariationsinsmartphoneusagepatternsacrossmultiplegeographical
regions,anddiscussitsimplicationsfortheubicompcommunity.
2 RELATEDWORK
Large-scaleMobileDataStudiesandtheirImplications. Inrecentyears,manymobiledatastudieshave
beenconductedtoassesssmartphoneusagepatternsofvarioususergroups.Bohmeretal.studiedtheapplication
life-cyclesonAndroidsmartphonesof4,125users,mainlyacrossEuropeandtheUS,overa5-monthperiod[7].
Oneoftheirkeyobservationswasthesurprisinglyshortdurationofappusagesessions. Ferreiraetal.[15]
built upon their work and found that 41.5% of all application sessions lasted less than 15 seconds. Falaki et
al.[13]evaluatedtheimpactofuserinteractionswiththedeviceonnetworkandenergyconsumption.Asimilar
9-month-longstudybyDoetal.[12],involving77Europeanparticipants,bringsoutlocality-basedapplication
usagepatterns.Theyfoundthatuserstendtousemoresynchronouscommunicationmodes(suchasvoicecalls)
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overothersinunknownornon-stationarylocations. Comparablecontextualresultshavealsobeenobserved
byRahmatiandZhongamong15-to18-year-oldsfrombelowaverageincomehouseholdsinHouston,USA
[35].Theyfoundthatparticipantsalsotendtospendmoreoftheirtime-andrecordlongersessions-inareas
withbetterWiFiconnectivity. TheinfluenceofWiFiconnectivityhasbeenfurtherdiscussedbyBaumannet
al.[6],whoseresultsshowthattheprobabilityofusersgeneratingdatatrafficonaWiFinetworkistwicethat
onacellularconnection.Theimplicationsofsuchresultsisenormous,andhastherebyledtothedevelopment
ofcomprehensivemodelsofuserbehaviorthatcanbeutilizedinordertoimproveusabilityandefficiencyof
smartphones.AnexemplificationofthesameistheMarkovstatetransitionmodelofsmartphonescreenusethat
hasbeendevelopedbyKostakosetal.asdescribedin [20].
BehaviorAnalysisonSmartphones. Studiesonsmallerscalehavealsobroughtupotherdetailsofbehavioral
patternsofsmartphoneusers.Forinstance,Jonesetal.[18]exploreapp“revisitationpatterns”usinganapplication
deployedonGooglePlay.Bystudyingtherevisitationcurvesshowinghowfrequentlyusersreturnedtoanapp,
theywereabletoconfirmseveralintuitivestructuresofusage.VanBerkeletal.[42]discoveredandreported
flawswiththeprevailingapproachofapproximatingsessions,findingthatwhenuserslockandunlocktheir
smartphoneswithinashortduration(e.g.,lessthanaminute),theyaremorelikelytobeestablishinganewsession
thancontinuingthepreviousone.Thiscounterintuitiveobservationcalledforfurtherresearchonsmartphone
sessionapproximations,perhapsalongthelinesofthecomprehensivequantificationofsmartwatchsessions
presentedbyVisurietal.in[43].
Notificationpreferencesofsmartphoneusershavealsobeenexploredinsignificantdetail.Mehrotraetal.[27]
designedsmarternotificationmechanismsbyconstructingassociationrulesusingcombinationsoftextinthe
notification titles and the user’s contextual aspects of activity, location and time. Mehrotra et al. [28] also
designed classifiers to learn the most opportune moment to deliver notifications to users, based on content,
socialrelationships,andapplicationcontext.Additionally,asimilarbehavioranalysisstudyaimedatyouthina
KoreanuniversitywasalsoconductedbyLeeetal.[21].Thisstudy,involving95studentsforaperiodof67days,
soughttoidentifysmartphoneusagepatternsofboth“high/at-risk”and“non-risk”groupsofusers,whohad
beenclassifiedbypre-trialsurveys.Sensordatafromsmartphonesisalsobeingutilizedbyresearcherstostudy
thebehaviorofusers.Tsapelietal.[41]detectthecausaleffectsofseveralfactorssuchasworking,exercising
andsocializingonthestresslevelsof48students.
Thedependencyofuserbehavioronthecontextoftheuserhasalsobeenexploredinpriorresearch. The
collectionofqualitycontextualdatahasitselfbeenanopenchallenge.Liuetal.[23]foundthattheperceived
needfordonationandtheperceivedorganizationreputationactasmainmotivatorstoencourageuserstodonate
contextualdataforstudies.Numerousaspectsofcontextualdependencehavebeeninvestigatedinpaststudies.
Forinstance,Karikoskietal.[19]studiedthecommunicationpatternsofusersbasedonparameterssuchastheir
location,mobilenetworkcellID,WLANdataetc.todeterminetheirpreferenceforlengthofvoicecalls,intensity
ofusageofemail/SMS,IMorVoIPservicesetc.Liuetal.[24]questioned267usersinChinatobuildanadoption
modelofmobilegamingwhichindicatedthatcontextwasthebiggestinfluencerandpredictorofmobilegame
adoption.Theresultsoftheabove-mentionedworksreiteratetheneedtoexaminethecontextualfactorsdriving
userpractices.
Anotherfacetofuserbehaviorthatmeritsin-depthdiscussionisthatofbatteryusage.Ferreiraetal.in [14]
investigatechargingandbatteryusagepatternsofover4,000usersover4weeks. Thestudyhighlightedthe
energywastagecausedbyusersnotunpluggingtheirphonesassoonasthechargingcyclecompleted.Moreover,
itassertsthevaluethatusersassociatewiththebatterylifeoftheirdevices,whichisreiteratedbytheresults
ofourindependentsurvey. Hosioetal.[17]haveattemptedtosystematicallymeasurethemonetaryvalueof
smartphonebatterylifeandhavefoundthepricesofthefirstandlast10%batterysegmentstodiffersubstantially.
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Table1. Facetsofsmartphoneusagestudiedinthepaper,alongwiththeassociatedresearchquestions
Facetsof
ResearchQuestionsAddressed
SmartphoneUsage
Howistheapplicationusagedistributedtemporally?
Howlongandhowfrequentareapplicationusagesessions?
ApplicationUsage Whatmotivatesthechoiceofapplicationsamongusers?
HowdoestheusageofVoIP&IPmessagingappscomparewithtraditionaltelephonyapps?
Whydousersuninstallapps?
Howquicklydousersrespondtoanotification?
Notifications Howeffectivearenotificationsinengagingtheuser?
Howdoesalertmodalityimpactanotification’sresponsetime?
Howdoestheuser’sphysicalactivitycontextaffectapplicationusage?
Howaresmartphoneusagepatternsimpactedbytheuser’slocation?
UserContext
Cantypeofnetworkconnectivityhaveaneffectonsmartphoneusagepatterns?
Howdoesthebroadersocio-economiccontextaffectsmartphoneusageofIndianusers?
Howdoesbatterychargingbehaviorvarytemporally?
ChargingBehavior
Howlongandhowfrequentarethechargingsessions?
SmartphoneUsageStudiesinIndia. Asdiscussedpreviously,smartphoneusageinIndiahasbeenreaching
newheightsintherecentyears.Accordingtoa2016reportbybusinessintelligencefirmAppAnnie[1],Indian
usersspentastaggering150billionhoursonsmartphones,arisefromaround100billionhoursin2015.Thereport
alsopredictsfurthergrowthinIndia’ssmartphonepenetration.Anotherpointtobenotedfromthereportisthat
IndialeadsmarketssuchasChina,SouthKorea,UKandtheUSintermsoftheaveragenumberofshoppingapps
installedperuser–pointingtotheadaptivityofIndianstowardsmobilee-commerceaswellastheirtendencyof
comparingserviceprovidersbeforemakingapurchase.ThisissupportedbyDeshmukhetal.[10],whodiscuss
theshiftfrome-commercetom-commerceinIndia,andanalyzethesocialfactorsthatsupportthistransition.
However,ourunderstandingofIndiansmartphoneusersisprimarilyrestrictedtomarketingreportsgenerated
bybusinessanalystfirms. Usuallythegoalofsuchreportsistostudythemarketopportunitiesandprovide
guidancetobusinesses,ratherthanlookingintothenuancesofuserbehaviorthatcouldbeofinteresttomobile
researchersanddevelopers.Whiletherehavebeensmall-scalefocusedstudiesinthemedicalliteraturewhich
havelookedataddictioninmobilephoneusageamongIndianusers[9,11],tothebestofourknowledge,no
large-scalestudyhaseverbeendonetodevelopaholisticunderstandingofsmartphoneusagebehaviorinIndia.
Inthiswork,ourgoalistogatherlarge-scalesmartphoneusagedatafromIndianusers,andsystematically
understandthevariousfacetsofsmartphoneuse. Webuilduponpriormobiledataresearchworks,andalso
highlightuniqueaspectsofsmartphoneusageamongourtargetusergroup.
3 STUDYDESCRIPTION
Inthissection,weprovidedetailsofouruserstudytocollectlarge-scalesmartphoneusagedatafromusersin
India.Webeginbyprovidingthestudyoverview,whichisfollowedbyadescriptionofourdatacollectionsystem,
studymethodology,andparticipantdemographics.Finally,wegiveasummaryofthedatalogscollectedinour
study,andouranalysisplanforthesubsequentsections.
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3.1 Overview
Thedomainofsmartphonedataanalysisisclearlyverybroad,asisevidentfromtheextensiveanddiverseresearch
inthisareaasdiscussedin§2. Inthispaper,wefocusouranalysisonfourbroadfacetsofsmartphoneusage
thatareparticularlyrelevanttotheubicompcommunity.Thesefourfacetsalongwiththeresearchquestions
exploredwithineachfacetaretabulatedinTable1andexplainedbelow:
ApplicationUsageAnalysis:Mobileapplicationsareatthecoreofthesmartphoneecosystem–in2016,atotal
of90billionappsweredownloadedfromGooglePlayandAppleAppStore[1].Whileanumberofmarketing
surveyshavebeenconductedonthegrowthandpotentialoftheIndianapp‘market’,tothebestofourknowledge,
therehasbeennolarge-scaleresearchstudyintheubicompandmobilesystemscommunitythatprovidesdetailed
insightsintotheapplicationusagebehaviorofthisusergroup.Inparticular,weexplorethetemporalpatternsof
appusage,distributionofappsessions,motivationsbehindinstallation,usageanduninstallationofcertainapps,and
relationshipbetweentheusageofvariousappcategories.
Notification Analysis: An in-depth understanding of the human-notification interaction can help mobile
developersincreatingintelligentnotificationdeliverymechanismsthatleadtohigheruserengagement.Our
workspecificallylooksatthereceptivityandeffectivenessofmobilenotificationsamongIndianusers, andthe
impactofalertmodalityonanotification’sresponsetime.
ContextAnalysis:Priorubicompstudieshaveshownthatsmartphoneusagehasastrongdependencyonthe
user context [19]. An accurate inference of the user context, combined with a sound understanding of user
behaviorinthatparticularcontext,canhelpubicomppractitionersdesignmobilesystemsthatcanbetteradapt
touserneeds.Inthispaper,weprimarilylookatfourkindsofcontextsthatmayinfluencesmartphoneusage
behavior,namelylocationcontext,physicalactivitycontext,connectivitycontext,andsocio-economiccontext.
ChargingBehaviorAnalysis:Modernsmartphoneapplicationsrunsophisticatedmobilesensing,inference
andnetworkconnectivityoperationswhichimposeamajorburdenonthesmartphonebattery,andmayrequire
userstochargethephonebatteriesatregularintervals.Byunderstandingthebatterychargingpatternsofthe
end-users,mobiledeveloperscanscheduletheirenergy-heavyoperationstoopportunemoments–forexample,
whenthebatterylevelishighorwhenauserislikelytochargethephone.Tothisend,weanalyzetheduration
andtemporaldistributionofchargingsessions,aswellastheimpactofbatterylevelsonusers’decisiontocharge
theirphones.
Whilethesefourfacetsofsmartphoneusagehavewitnessedactiveresearchintheubicompcommunity,itis
importanttoacknowledgethattherecouldbeotherinterestingaspectsofsmartphoneusagesuchasinfluenceof
thesocialnetworkonusage,datatrafficpatterns,OS-specificvariationsinusageetc.whichareoutofscopeof
thispaper,andcanbeexploredinfuturework.
3.2 Methodology
In this section, we present our data collection system and provide details on our study methodology and
participants.
DataCollectionSystem: OurdatacollectionexercisefocusedonusersofAndroidOS–currently,Android
hasa97%smartphonemarketshareinIndia[5],makingitaclearchoiceforalarge-scaleubicompstudy.We
developedanAndroidapplicationwhichrunsonAndroid5.0+anddistributedittousersviaGooglePlay.The
appisimplementedtorunasabackgroundserviceontheuser’sdeviceandpassivelyrecordsallusagesessions
onthedevicealongwithvariouscontextualinformation.
Table 2 details the five types of data points collected by the app. We used event-based Android APIs to
collectapplicationdata,screenevents,notificationeventsandcalldata.Specifically,wheneveranewdatapoint
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Table2. Listofdatacollectedfromuser’sphones.(*User’sphysicalactivitywasobtainedbyqueryingtheAndroidActivity
RecognitionAPIs)
DataType Description
Applicationdata Packagenamesofallappsinstalledonthephone,timestampsofapp open(anapp
comingintoforeground)andapp close(anappgoingintobackground).
Screenevents Timestampswhenthephonescreenisturnedon,offandunlocked.
Notificationevents Timestampsofnotificationarrival,notificationaccessordismissal,nameofapplication
whichsentthenotification.Thecontentinsidethenotificationwasnotcollectedfor
privacyreasons.
Callevents Timestamps of calls, call medium (cellular/VOIP), type of call (incom-
ing/outgoing/missed).
SensorandContext Batterylevel,celltowerID,WifiDetails(isConnected,BSSID),isHeadphoneConnected,
proximitytothephone,ambientlightintensity,ambientsoundlevel,user’sphysical
activity*.
pertainingtothesecategoriesbecomesavailable(e.g.,anewnotificationisreceived),theAndroidOSfiresan
eventwhichiscaughtbyourbackgroundserviceandtherequireddatapointsarelogged.Further,sensorand
context data items listed in the last row of Table 2 were collected by polling Android APIs at i) the start of
eachsmartphonesession(i.e.,wheneverthescreenwasturnedon),andii)onceevery2minutes.Theperiodic
collectionofsensorandcontextdatawasdonetoensurethatdataiscollectedevenduringperiodsofinactivity.
Alldatalogscollectedbytheapplicationarestoredlocallyonthephone,andareperiodicallyuploadedtoa
remoteserver.
SystemDeployment. WereleasedourdatacollectionapponGooglePlayStore,andsolicitedparticipation
inthestudybypublicizingitonsocialforumsandemaillists. Morespecifically, weadvertisedthestudyin
6universitycampusesthroughemaillistsanduniversityforums,andin7industrialorganizations(primarily
softwareandbusinessconsultingcompanies)throughemployeeforums.Thisresultedinatotalgeographical
spreadofmorethan10urbancitiesand6statesinIndia.Inaddition,wealsoadvertisedthestudythroughpersonal
socialnetworks,andparticipantswererecruitedthroughsnowballsampling. Whileparticipantrecruitment
throughsocialforumsandemaillistsiswidelydoneinmobiledataliterature[32–34,42],thereisneverthelessa
possibilityofsamplingbiasinthismethodofparticipantselection.Inourstudy,asparticipantrecruitmentwas
doneacrossmultipleorganizationsinmorethan10urbancities,wearguethattheproblemofsamplingbiasis
alleviatedtoalargeextent.However,wedonotclaimthatthesampleiscompletelyunbiasedandrepresentative
foracountrywithapopulationof1.3billion.Thisisclearlyalimitationofourstudyandwithuserstudiesin
general,andassuchwedulyacknowledgeitintheLimitationssectionin§6.
Thedatacollectionforourstudywasdoneintwophases–thefirstphaseranfromDecember2015toJuly
2016andthesecondfromJanuary2017toFebruary2017.Thesecondphasewasprimarilymotivatedbyamajor
socio-economicchangeinIndia–widelyreferredtoasDemonetization1–thattookplaceinNovember2016.
WewantedtounderstandhowsmartphoneusageinIndiaadaptstochangesinbroadersocio-economiccontext
(detailedin§4.3).
1TheIndiangovernmentannouncedonNovember8th,2016thatthetwohighest-valuecurrencynotesinthecountrywouldceasetobelegal
tenderwithimmediateeffect.Oneoftheintendedgoalsofthisdecisionwastoencouragepeopletousedigitalpaymentmechanisms[3].
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ParticipantDemographics. Intotal,theapplicationwasinstalledby215users,whowereagedbetween18
to38years. 63ofthemidentifiedthemselvesasfemales. Forouranalysis, however, weonlyincludedusers
contributingmorethanonemonthofdata.Thisfilteringstepresultedin160users(41females)agedbetween
18and38years. Whiletheagediversityinourparticipantgroupseemratherlow,itisactuallyinlinewith
priorresearchbyPewResearchCenterwhichfoundthatonly9%ofthepopulationagedover35yearsownsa
smartphoneinIndia[4].
104outofthe160participantsidentifiedthemselvesasstudents,whiletheremainingwereworkingprofession-
als.Exceptforthesebasicdemographics,wedidnotcollectanypersonalinformationfromtheusers.Duetothe
inherentanonymityinourstudy,wedonotknowtheethnicitiesoftheparticipants,assuchourfindingsshould
beinterpretedasapplicabletosmartphoneusersinanIndiancontextratherthanethnicIndianusers.Howeverfor
brevity,werefertoourparticipantsasIndiansmartphoneusersinthepaper.Finally,asanincentiveforusingthe
app,userswereenteredintoalottery(iftheyagreedtoprovidetheiremailaddress)andtwowinnerswereeach
givenawearablefitnessband.
QualitativeDataCollectionInordertocomplementourquantitativedataanalysiswithsubjectiveperceptions
ofusers,weconductedanonlinesurveywiththeparticipantsfromourstudy. Atotalof55participants(10
females) completed the survey, which comprised of 30 questions revolving around the aforementioned four
facetsofsmartphoneusageanalyzedinourstudy.Finally,weconductedaseriesofpost-studyinterviewswith
26 participants (10 females) from our quantitative study, aged between 18 to 30 years. The interviews were
semi-structured,30minuteslong,andaimedatuncoveringthesubjectivereasonsbehindthequantitativefindings
ofourstudy.Eachinterviewwasrecordedandlaterpartiallytranscribedtocompletetheobserver’snotes.No
compensationwasprovidedtotheparticipants.
3.3 DataLogsandAnalysis
Thecombineddatasetcollectedinourstudyconsistsof11.9milliondatapoints,outofwhichthereare1.7million
applicationusageevents,433,900notifications,andmorethan6millionsensorandcontextdatapoints.Intotal,
weobserved620,194smartphoneusagesessionsacrossallusers(µ =3875,σ =2100)withacombinedduration
of55,619hours. Wedidnotobserveanysignificantdifferenceintheparticipantdemographics(age,gender,
occupation)betweenthetwophasesofdatacollection.Assuch,wedecidedtocombinethedatasetsfromthe
twophaseswhilepresentingourfindings,exceptforwhenwespecificallyanalyzetheeffectsofDemonetization
onsmartphoneusage(detailedin§4.3).Theresultsofthesurveyandtheinterviewtogetherwiththequantitive
dataweextractedfromthesystemlogsarepresentedinthesubsequentsections.
4 RESULTS
Inthissection,wepresentadetailedanalysisoftherichdatasetcollectedinourstudy.Asdiscussedin§3.1,we
explorefourbroadfacetsofsmartphoneusageinIndiathatarerelevantfortheubicompcommunity,namelya)
ApplicationUsagePatterns,b)NotificationAttendanceBehavior,c)RelationshipbetweenContextandSmartphone
Usage, andd)BatteryChargingBehavior. Ouranalysisofeachofthesebroadfacetsispresentedinseparate
subsections,andisguidedbytheresearchquestionsoutlinedin§3.1andsummarizedinTable1.Ashighlighted
earlier,inadditiontouncoveringthesmartphoneusagebehaviorofIndianusers,thispaperalsoaimstocontrast
their behavior with prior mobile data studies conducted in different geographical regions. Therefore, after
analyzingtheIndianuserdataacrossthefourfacets,wepresentacomparisonbetweenfindingsfromtheIndian
contextvs.priormobiledataliteraturein§5.
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MovingBeyondMarketResearch:DemystifyingSmartphoneUserBehaviorinIndia • 82:9
4.1 UnderstandingApplicationUsage
Inthissection,westudytheapplicationusagebehaviorofusersinIndia. Ourapplicationlogsconsistofapp
usageinformationfrom2931uniqueapps,whichwereusednearly1.7milliontimes,withatotalusageduration
of51,800hours.Additionally,wecollectedsubjectivedataaboutappusagethroughasurveyandsemi-structured
interviews.Thisrichdatasetprovidesauniqueopportunitytoanswerthefollowingresearchquestionsregarding
the app usage of Indian users. We also contrast the usage behavior of Indian users with prior literature on
appusagefromothergeographicalregions,andlaterin§6,weexplaintheimplicationsofthesegeographical
variationsfortheubicompcommunity.
• Howistheapplicationusagedistributedtemporally?:Weseektounderstandthetemporalvariationsin
usageofapplicationsfromvariousappcategories–isappusageevenlydistributedthroughouttheday
oraretherecertainpeakusagetimes?
• Howlongandhowfrequentareapplicationusagesessions?:Weexploreifappusagehappensinburstsof
shortandfrequentsessions,orareusersmoreinclinedtowardslessfrequentbutlongersessions?
• Whatmotivatesthechoiceofapplicationsamongusers:Withthepresenceofbothaboominglocalstartup
ecosystemandglobale-commerceandtransportcompanies,Indianusershavemultipleappstochoose
fromtoavailanygivenservice. Weseektounderstandhowusersmanagethis‘dilemma’ofchoice–
whatstrategiesdotheyadoptforchoosingaservice?
• HowdoestheusageofVoIPandIPmessagingappscomparewithtraditionaltelephonyapps?:Weexplore
userpreferenceswithregardstocommunicationapps–specifically,weaimtounderstandhowVoIPand
IPmessagingappsco-existwithtraditionaltelephonyserviceslikeGSMcallsandSMS.
• Whydousersuninstallapps?:Westudytheunderlyingsubjectivereasonsthatcauseuserstouninstall
appsfromtheirphone.Thisinformationisparticularlyimportantforappdevelopers,whomaywantto
adapttheirmobilesystemstomeetend-userexpectations.
Howistheapplicationusagedistributedtemporally?InFigure1,weplotthetemporaldistributionofapp
usagebycategory,i.e.,whenareappsfromvariouscategorieslaunched.Quitesurprisingly,weobservethatthe
highestvolumeofappusagetakesplacebetween12am-4amformostoftheappcategories,whichaccountsfor
roughly23.94%ofallappusage. Inparticular,appsinCommunication,Photography,Weather,andFoodand
Drinkscategorieshavetheirpeakusageatthesetimes.Further,thehoursbetween8am-12amseetheleastapp
usageinourdataset(2.62%).Thisobservationisremarkablydifferentfrompriorstudies(e.g.,[7]whichfound
thatforAmericanusers,morninghoursbetween8am-12amcontributetoasignificantpercentageofappusage
(16.17%).
Wefurtherinvestigatedthecauseofthesedifferencesthroughoursurveyandinterviewsandfoundthatusers
refrainfromusingtheirsmartphonesinthemorninghourswhichtendtobethestartinghoursofworkorschool.
Moreover,amajorityoftheparticipants(n =19)mentionedthattheytypicallysleepwellaftermidnight,and
spendasignificanttimeontheirphonesduringlatenighthours.Oneintervieweesaid,
”Ioftenstayuptill2amworking,afterwhichIscrollaimlesslythroughmysocialmediafeedswhile
lyinginbed.”
WesuspectthatthisbehaviorcouldbeduetotheagedemographicsofIndiansmartphoneusers[4](also
reflectedinourparticipants)whichisskewedtowardsyoungerusers.
Next,weanalyzetheco-occurrenceprobabilitiesofthetop20appcategorieswithinasmartphoneusagesession
(i.e.,thetimefromscreenunlocktoscreenoff).Theco-occurrencematrixinFigure2isbestinterpretedrow-wise,
witheachrowrepresentingtheprobabilityofacategoryonthex-axisco-occurringwiththerowcategoryonthey-
axisinthesamesession.Moreformally,co-occurrenceprobabilityiscomputedasP(x,y) =count(x,y)/count(x)
whereP istheco-occurrenceprobabilityofcategoriesx andy,andcount(x,y)representsthenumberofusage
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82:10 • AkhilMathur,LakshmiManasaKalanadhabhatta,RahulMajethia,andFahimKawsar
Fig.1. Category-wiseDiurnalSessionDistribution
sessionswherebothx andycategoryappswerepresent.Forexample,whenaBrowserappisusedonthephone,
thechancesofalsousingaCommunicationandaSocialappinthesamesessionare0.42and0.22respectively,
and of using another Browser app (diagonal entry) is only 0.02. From Figure 2, we observe heavy usage of
Communicationappsalongwithothercategories–forallappcategories, thereisnearly30%chancethata
Communicationappwillbeusedinthesamesession.Userresponsessuggestthatthisisbecausetheytendto
engageindiscussionswiththeirfriendsorcolleaguesabouttheiractivitiesonotherapplications.Forexample,
oneoftherespondentsnoted,
”IusuallyuseWhatsApptosharescreenshotsofmysocialmediafeedwithmyfriendsifIcome
acrosssomethinginteresting”.
Participants(n =9)alsoreportedusingCommunicationappstogettheirfriends’opinionswhenpurchasing
something,ortoreachaconsensuswhileorderingfoodormakingplansforagroupofpeople.
Howlongandhowfrequentareapplicationusagesessions? InFigure3,weplottheCDFofappusage
durationsforthetopapplicationcategoriesbyusage.Asexpected,appsundertheGamescategorytendtohave
thelongestusagetime,withhalfoftheusagesessionslastingformorethan90seconds(meanduration=195
seconds).ThisisfollowedbyShopping(mean=101seconds)andSocialapps(mean=95seconds),whileEmail
appshavethelowestmeansessiondurationof37seconds.WealsoobservethatMusicappshavesurprisingly
lowsessiondurations(mean=38.1seconds),whichcanbeattributedtothefactthatMusicappsaremostlyused
inthebackgroundandassuch,theirforegroundtimesarerathershort.Overall,thesessiondurationswerefound
tobesignificantlylongerthanthoseofAmericanusersreportedbyChurchetal.in[8],whereover48%ofall
applicationusageswerereportedtolast15secondsorless,andapproximately56%tolast22.5secondsorless.
PACMonInteractive,Mobile,WearableandUbiquitousTechnologies,Vol.1,No.3,Article82.Publicationdate:September2017.