Understanding People's Receptivity to Mobile Notifications

12 Pages·2016·1.88 MB·English

My Phone and Me: Understanding People’s Receptivity
to Mobile Notifications
AbhinavMehrotra VeljkoPejovic JoVermeulen
UniversityofBirmingham UniversityofLjubljana UniversityofCalgary
UniversityCollegeLondon Slovenia Canada
UnitedKingdom [email protected] UniversityofBirmingham
[email protected] UnitedKingdom
[email protected]
RobertHendley MircoMusolesi
UniversityofBirmingham UniversityCollegeLondon
UnitedKingdom UnitedKingdom
[email protected] [email protected]
ABSTRACT varietyofinformationsuchasemailmessages,socialnetwork
Notifications are extremely beneficial to users, but they of- events and birthday reminders. Notifications are at the core
tendemandtheirattentionatinappropriatemoments. Inthis of this information awareness, as they use audio, visual and
paper we present an in-situ study of mobile interruptibility hapticsignalstosteertheuser’sattentiontowardsthenewly-
focusing on the effect of cognitive and physical factors on arrivedinformation.
the response time and the disruption perceived from a noti-
Notificationsareextremelybeneficialtotheusers: however,
fication. Throughamixedmethodofautomatedsmartphone
at the same time, they are a cause of potential disruption,
loggingandexperiencesamplingwecollected10372in-the-
since they often require users’ attention at inopportune mo-
wild notificationsand474questionnaireresponsesonnotifi-
ments. Indeed,previousstudieshavefoundthatinterruptions
cationperceptionfrom20users. Wefoundthattheresponse
atinopportunemomentscanadverselyaffecttaskcompletion
time and the perceived disruption from a notification can be
time[11, 12, 25], leadtohightaskerrorrate[8]andimpact
influencedbyitspresentation,alerttype,sender-recipientre-
theemotionalandaffectivestateoftheuser[5,7].Also,users
lationshipaswellasthetype,completionlevelandcomplex-
mightgetannoyedwhentheyreceivenotificationspresenting
ity of the task in which the user is engaged. We found that
informationthatisnotusefulorrelevanttotheminthecurrent
even a notification that contains important or useful content
context[13]. Atthesametime,studieshaveshownthatusers
cancausedisruption. Finally,weobservethesubstantialrole
cannotignoretheirsmartphonesforalongtime,becausethey
ofthepsychologicaltraitsoftheindividualsontheresponse
startfeelingstressedandanxiousaboutmissingimportantin-
timeandthedisruptionperceivedfromanotification.
formationuntiltheyfinallypickupthephonetocheckforany
newnotifications[26].Thistensionisexacerbatedbythefact
AuthorKeywords
that individuals deal with hundreds of notifications in a day,
MobileSensing;Notifications,Interruptibility,
someofwhicharedisruptive[23]
Context-awareComputing.
Previous studies have investigated the user’s receptivity to
ACMClassificationKeywords mobilenotifications[15,29,32]. AsdefinedbyFischer[15],
H.1.2.ModelsandPrinciples:User/MachineSystems;H.5.2. receptivity encompasses a user’s reaction to an interruption
InformationInterfacesandPresentation(e.g. HCI):UserIn- and their subjective experience of it. For instance, users
terfaces might quickly respond to a notification when they are idle,
but they can still get annoyed because of the content of the
INTRODUCTION notification. Previous studies have shown that the user’s re-
Smartphones enable a new form of effortless information ceptivitytoanotificationisdeterminedby: (i)howinterest-
awareness. Throughouttheday,asmartphoneuserreceivesa ing,entertaining,relevantandactionableitscontentisforthe
user [15]; (ii) the type of application that triggers it – com-
munication applications are considered as the most impor-
Permissiontomakedigitalorhardcopiesofallorpartofthisworkforpersonalor tant[32];(iii)timecriticalityandsocialpressure[29]. Atthe
classroomuseisgrantedwithoutfeeprovidedthatcopiesarenotmadeordistributed
forprofitorcommercialadvantageandthatcopiesbearthisnoticeandthefullcita- sametime,somestudieshaveproposedvariousmechanisms
tiononthefirstpage. Copyrightsforcomponentsofthisworkownedbyothersthan to infer opportune moments, i.e., moments in which a user
ACMmustbehonored.Abstractingwithcreditispermitted.Tocopyotherwise,orre-
quicklyand/orfavorablyreactstoanotification[14,23,28].
publish,topostonserversortoredistributetolists,requirespriorspecificpermission
and/[email protected]. In order to infer interruptibility these studies have used ma-
CHI’16,May07-12,2016,SanJose,CA,USA. chine learning classifiers provided with different contextual
©2016ACM.ISBN978-1-4503-3362-7/16/05...$15.00.
DOI:http://dx.doi.org/10.1145/2858036.2858566
factors including user’s transitions between activities [18],
engagementwithamobiledevice[14],timeofday,location
andactivity[28]aswellasnotificationcontent[23].
However, none of these studies have deployed the proposed
mechanisms in a real world scenario with in-the-wild notifi-
(a) (b)
cations of a regularly used application. The key reason be-
hindthisisthefactthattheaccuracyofthesemechanismsis
still lower than the user’s expectations. In a real world sce-
nario,theuserswouldnotacceptasystemthatmightdeferor
stop an important notification. Previous studies have shown
thatusersarewillingtotoleratesomeinterruption,inorderto
notmissanyimportantinformation[20]. Webelievethatin-
terruptibilitymanagementsystemsfailtoachieveaveryhigh
accuracyinpredictingtheopportunemomentbecausethereis
stillalackofunderstandingconcerningthefactorsinfluenc-
ing the user’s receptivity to mobile notifications in different
physicalandcognitivesituations. (c) (d)
In order to bridge this gap, in this work we conduct an in-
situ study to collect objective and subjective data about mo-
bilenotifications. WedesignedanddevelopedMyPhoneand
Me (Figure 1), an application that uses a novel experience
samplingmethod(ESM)approachtouncoverthefactorsand Figure1. MyPhoneandMeapplication: (a)mainscreen, (b)phone
motivations impacting the user’s reaction and sentiment to- usagestatistics,(c)applicationusagestatistics,(d)dailynotifications.
wards a notification. Through My Phone and Me, we col-
lected10372notifications,474responsesfortheESMques-
tionnairesand11personalitytestresultsfrom20users.Using userstendtoclickhighlydisruptivenotificationsiftheycon-
this data, we investigate users’ interaction with mobile noti- tainvaluableinformation. Whileusersareawareofnotifica-
fications in different physical and cognitive contexts. More tions even when their phone is in silent mode, our analysis
specifically,thekeycontributionsofthisworkaretheinves- showsthatthealertmodalityhasasignificantimpactonthe
tigationof: time taken by the users to view the notification. Finally, we
observe the substantial role of psychological traits on how a
the impact of a notification’s alert modality on the user’s personreactstoamobilenotification,callingforhighlyper-
•
abilitytoperceiveanotificationalert; sonalizedinteractionbetweenasmartphoneanditsuser.
theimpactofthealertmodality, sender-recipientrelation-
•
ship, presentationofanotification, theongoingtasktype,
REASONING ABOUT USERS’ RECEPTIVITY TO MOBILE
completionlevelandtaskcomplexityontheresponsetime;
NOTIFICATIONS
theimpactofthesender-recipientrelationship,andtheon- An interruption tries to steer a user from an ongoing task
•
goingtask’stype,completionlevelandcomplexityonthe to the secondary task signaled by it [8]. As suggested by
perceiveddisruption; Clark [10], users can respond to an interruption in four pos-
the role of the sender-recipient relationship, notification sibleways: (i)handleitimmediately;(ii)acknowledgeitand
• contentandtheperceiveddisruptionontheuser’sdecision agree to handle it later; (iii) decline it (explicitly refusing to
toacceptordismissanotification; handleit);(iv)withdrawit(implicitlyrefusingtohandleit).
the impact of the user’s personality on the perceived dis- A user can respond to mobile notifications in a fairly
•
ruptionandresponsetimetoanotification. different way as compared to an in-person interruption.
For communication-related interruptions, for example, users
The findings of our study are wide-ranging, and may have
might perceive more disruption from an in-person interrup-
a direct impact on the way future notification management
tionthanfromamobilenotificationsbecauseofthepresence
mechanismsareconstructed. First,weobservethatasender-
ofaninterrupterintheformercase. Mobilenotificationsen-
recipient relationship, notification priority and an ongoing
ableflexibilityinthewayaninterruptionishandledbecause
task’s type and complexity influence the response time for
ofthelackofthephysicalpresenceofthesenderandtheasyn-
the notification, but there is no impact of the ongoing task
chronousnatureofmobilemessagingcommunication1.Thus,
completion level on the response time. Moreover, our re-
theexactmomentofhandlinganinterruptioncanbenegoti-
sultsshowthattherecipient’srelationshipwiththesenderof
atedandtherecipientcandecidewhenandhowtoattendtoa
a notification, the ongoing task’s type, completion level and
notification.
complexity influence the perceived disruption. Our findings
implythatthehigherthelevelofdisruptionperceivedbythe
1Certainsocialnormsandexpectationsfromthesenderside,how-
user the higher the probability of the notification being dis- ever, constrain the flexibility that the receiver has in reacting to a
missed. Fromourresults,wealsoobservethat,nevertheless, message[30].
(c1)
Seen Decision
Time Time
(c2)
(a) (b)
Figure2. ThethreetimemeasurementsofanotificationcapturedbytheMyPhoneandMeapplication. Thetimeofnotificationarrival(a),thetime
whenanotificationisseen(b),andthetimewhentheuseraccepted(c1)ordismissed(c2)anotification.Thetimedifferencebetween(a)and(b)isseen
timeandthetimedifferencebetween(b)and(c1orc2)isthedecisiontime.
However,thisflexibilityintroducesmanyotherissues. First, notificationsinthenotificationbarareseenwhentheuserun-
notifications can go unnoticed when a user does not register locksthephone. Incaseanotificationarriveswhentheuser
analert. Second,usuallynon-persistentnotificationsmaybe is already using the phone (i.e., the phone is unlocked), the
forgottenabout–auserridingabicycle,mightdecidetoat- seentimeofthisnotificationwouldbecomputedaszero. We
tend to a notification once they arrive at the destination, yet term the time from the notification arrival until the moment
forgettodoso. Finally,althoughdesignedtosignalaninter- thenotificationwasreacteduponastheresponsetimeforthe
ruptionbutnotinterruptthemselves,mobilenotificationscan notification.Forouranalysis,webreaktheresponsetimeinto
still induce unnecessary disruption to a user’s routine. For twointervals:
instance,thedisruptioncanhappenwhenauserdecidestoat-
Seentime(ST)–timefromthenotificationarrivaluntilthe
tendtoanotificationimmediately,despitebeinginthemiddle •
timethenotificationwasseenbytheuser.
of another task, only to find that the notification is about an
unrelatedpromotionaloffer.Moreover,adisruptionmayhap- Decisiontime(DT)–timefromthemomentausersawa
•
penevenifanotificationisnotattendedto,asthethoughtof notification until the time they acted upon it (by clicking,
a lingering notification may interfere with the user’s current launchingitscorrespondingapporswipingtodismiss).
taskperformance[33].
Weexaminethewayinterruptiontiming, withrespecttothe
Inthisstudy,weinvestigatethefactorsinfluencingtheuser’s primary task, determines the user’s response to the notifica-
response to a mobile notification, where the response is de- tion. Moreover,weareinterestedinthewayuserstriagedis-
finedbythetimetakentoregisterandreacttoanotification, ruptive notifications. Can users quickly discern when noti-
andthewayinwhichthenotificationishandled(i.e.,clicked fications are disruptive? We hypothesize that humans might
ordismissed).Moreover,weinvestigatetheuser’smotivation still attend to a notification, even if they know that the pri-
for being self-disruptive by clicking the disruptive notifica- mary task is going to be disrupted. For example, in their
tions. studyofWhatsAppnotifications,Pielotetal. [30]showhow,
due to an inner pressure raised by social expectations, users
Ourassumptionisthattheresponsetimeforanotificationand
quicklyrespondtoinstantmessaging(IM)communicationor
thedisruptionperceivedbytheuserareinfluencedbythedif-
frequentlychecktheirphones,inducingselfinterruptionsjust
ferentaspectsofthenotificationaswellastheuser’scontext.
inordertosatisfythesocialexpectations.Inourwork,weare
TocapturethismeasurewedevelopedanAndroidexperience
lookingbeyondjustIMnotificationsandinvestigatetheway
samplingmethod(ESM)applicationthatmonitorstheactual
anydisruptivemessageishandled. ThroughourESMstudy
notificationsusersreceiveontheirphone,recordstheirreac-
weidentifythemotivationbehindreactingtoadisruptiveno-
tiontonotificationsandthenqueriestheuserstoidentifytheir
tificationandthereasoningandtheexternalfactorsthatlead
rating of the disruption caused by the notification. We aug-
totheexactreaction.
mentthiswithquestionsaboutthemotivationforhandlinga
notification in a particular way. Further, our ESM question- We aim for a comprehensive investigation of interruptibility
naires ask the user to provide data on the type, complexity fromauserperspective,thuscomparingtheeffectofdifferent
and the completion level of the ongoing task and the user’s aspects of a notification on its response time and disruptive-
relationshipwiththesender. Finally,wecollectparticipants’ ness. Finally, we investigate the potential role of individual
personalitytraitmeasuresattheendoftheexperiment. psychological traits on how users perceive and react to dis-
ruptivenotifications.
First, we investigate the ability of users to adjust their re-
sponsetimestoanotification, andseehowquicklytheycan
triagedifferentnotificationsindifferentsituations. Asshown DATACOLLECTION
inFigure2,wetakethreetimemeasurementsforeachnotifi- In order to investigate the nature of disruptive notifications
cation: thetimeofnotificationarrival(a),thetimewhenthe andfactorsthatdeterminetheuser’sreceptivitytomobileno-
notificationisseen(b), andthetimewhentheuseraccepted tifications in different physical and cognitive situations, we
(c1) or dismissed (c2) the notification. Note that in order to conductedanin-situfieldstudy. Morespecifically,wedevel-
detectthemomentatwhichanotificationisseen,weusethe opedanAndroidappcalledMyPhoneandMe–anAndroid
unlockeventofthephoneandassumethatallnewlyavailable experiencesamplingmethod(ESM)applicationthatcollects
informationaboutin-the-wildnotifications,users’interaction
Group Features Question Options
Time Arrival,seenandtheremovaltimeofanotification. Did you notice the alert
(i)Yes,andIdecidedtocheckmyphoneim-
Notification (e.g., vibration, sound,
Whetherthenotificationwasclickedordismissed. mediately. (ii) Yes, but I was already using
response flashing LED) for this
myphone. (iii)Yes, butIignoredthealert.
Notification notification when it first
Senderapplicationandthetitleofanotification. (iv)No,Ididn’tnoticethealert.
details arrived?
Signalsusedbyanotificationtoalerttheuser: sound, vi- (i)Idecidedtoimmediatelyclickit.(ii)Ide-
Alerttype
brate,andflashingLED. cided to dismiss it because it didn’t require
How did you handle the
Physicalactivity, location, presenceofsurroundingsound, anyfurtheraction. (iii)Idecidedtodismiss
notificationwhenyoufirst
Context WiFiconnectivity,proximitytothephone,surroundinglight itbecauseitwasnotrelevantoruseful. (iv)I
sawit?
data intensity. Thisdataiscollectedonarrivalandremovalofa decidedtoreturntoitlater.(v)Other(descrip-
notificationfromthenotificationbar. tive).
Table1.DescriptionoffeaturesfromtheMyPhoneandMedataset. (i)Thesenderisimportant.(ii)Thecontentis
important.(iii)Thecontentisurgent.(iv)The
Select all factors that
content is useful. (v) I was waiting for this
made you decide to
notification.(vi)Theactiondemandedbythe
click/dismiss the notifica-
senderdoesnotrequirealotofeffort.(vii)At
with them in natural situations (while they are performing tion.
thismoment,Iwasfree.(viii)Other(descrip-
their day-to-day activities), and the physical and cognitive
tive).
contextdetails. (i) Partner (ii) Immediate family (chil-
dren, parents) (iii) Extended family
The My Phone and Me application uses Android’s Notifica- (nieces/nephews, cousins, aunts/uncles)
tionListenerService[1]toaccessnotificationsandGoogle’s What best describes your (iv) Friend (v) Acquaintance (vi) Superior
ActivityRecognitionAPI [3]andESSensorManager[22]to relationshiptothesender? at work (vii) Colleague (viii) Subordinate
at work (ix) Client (x) Service provider
obtain the context information. Table 1 lists the groups of
(xi) Sender is not a person (xii) Other
features captured by the application. The collected context
relationship(descriptive).
data has not been explored for the analysis presented in this Please describe what the
Descriptiveresponse.
paper. To infer the user’s response to a notification, the My notificationwasabout.
PhoneandMeapplicationcheckswhethertheapplicationthat Pleasedescribewhatactiv-
ityyouwereinvolvedwith
triggeredthenotificationwaslaunchedaftertheremovaltime Descriptiveresponse.
whenyoureceivedtheno-
ofthatnotification. Weareawarethatsomenotificationsare
tification.
dismissedbecausetheydonotrequireanyfurtheraction. For (i) Starting a new task/activity. (ii) In the
When the notification ar-
this reason, we capture seen time and use the difference be- middle of a task/activity. (iii) Finishing a
rived,Iwas:
tweenseentimeandremovaltimetounderstandhowlongit task/activity.(iv)Notdoinganything.
Thetask/activityIwasdo-
takesfortheusertoreadandreacttoanotification. Five-level Likert scale rating between
ing when the notification
"stronglydisagree"and"stronglyagree".
Tocollectsubjectivedatafromusers,theMyPhoneandMe arrivedwascomplex.
I found the notification Five-level Likert scale rating between
applicationtriggersfourquestionnairesinaday. Aquestion-
disruptive. "stronglydisagree"and"stronglyagree".
naireistriggeredonlywhenanotificationishandled;itcon-
Table2.Questionsandtheiroptionsfromquestionnairetriggeredbythe
tainsquestionsaboutwhythenotificationwasclickedordis-
MyPhoneandMeapp.
missed by presenting a screenshot of that notification. The
application triggers a questionnaire for a randomly selected
notification in every four hours time window between 8.00
veloped by Goldberg [16]. A notification to take this test is
amand8.00pmandthelastquestionnaireatarandomtime
triggered once the user has responded to 28 questionnaires.
between8.00pmand10.00pm. Theapplicationdidnottrig-
A user can also take the test at any time by clicking on the
ger any questionnaire after 10pm so that the participants do
personalitytestbuttonpresentintheapplication’sactionbar.
not feel annoyed at responding to the surveys late at night.
The application automatically used the local time zones be-
cause it relies on the phone’s time. Moreover, if the user is
busy,thequestionnairecanbedismissedbysimplyswipingit RecruitmentoftheParticipants
fromthenotificationbarandnoquestionnaireisshowntothe The My Phone and Me application was published on the
userforthenext30minutes. Google Play Store from 12th August 2015. It was installed
by74participantswithoutanymonetaryincentive. Asshown
A questionnaire comprises seven multiple-choice and two
inFigure1,MyPhoneandMetellstheusersabouttheirad-
free-response questions. The list of questions and their op-
diction to the phone. It allows users to check statistics on
tions are shown in Table 2. Since we ask the users to enter
their phone usage and interruptions. The application visual-
thefreeformtextfortwoquestions,itcouldincreasetimeto
izesauser’sphoneactivitiesbasedondifferentcriteria,such
respond to a questionnaire and may become a source of an-
astheirhourlyphoneusage(Figure1C),hourlyusageofin-
noyance. Therefore, the application allows the users to dic-
dividualapplications(Figure1D)andhowmuchtheyinter-
tatetheresponsestothesequestions. Theseanswersarethen
actwithnotifications(Figure1B).Webelievethatdisplaying
convertedtotextusingAndroid’sSpeechRecognizerAPI[2].
this information has a minimal interference with users’ ac-
Additionally,theMyPhoneandMeapplicationaskstheusers tual behavior for interacting with notifications, but provides
totakeapersonalitytestbasedonthe50itemBig-FiveFac- a valuable functionality in order to make the users keep the
torMarkersfromtheInternationalPersonalityItemPool,de- applicationinstalledontheirphones.
Inordertoensureprivacycompliance,theMyPhoneandMe
application goes through a two-level user agreement to ac- Thekeyfindingsofthissectionare:
cess the user’s notifications. Firstly, the user has to give ex-
Users are aware of the notification alerts even when
plicit permission as required by the Android operating sys- •
the phone is in silent mode. However, seen time is
tem. Secondly, the application shows a list of information
fastestwhenthephoneisinvibratemodeandslowest
that is collected and asks for user consent. Moreover, we
forsilentnode.
show the original content of a notification to the user along
Notificationsareseenfastestwhentheuseriscommut-
withthequestionnaireinordertoavoidanyrecallbiasinthe
•
ingandslowestwhenidle.
databutwedonotcollectthenotificationcontentforprivacy
reasons. User’sattentivenessincreases(reducingtheseentime)
•
withtheincreaseinthecomplexityofanongoingtask.
DATASET The decision time is higher for the notifications from
The data collection was carried out for around two months, • lessfrequentlycontactedsenders.
duringwhichwecollected19494notifications,611responses
High-prioritynotificationsgetquickerresponse.
forthequestionnaire(comprisingasetofninequestionslisted •
inTable2)and11personalitytestresults(50item-basedBig-
Five Factor Markers by Goldberg [16]) from 74 users who
The Role of Alert Modality in Perceiving a Notification
installed the My Phone and Me application. Many users
stoppedrespondingtothequestionnairesafterafewdaysand Alert
some did not respond at all. Therefore, we select a subset Anotificationcanalerttheuserbymeansofvibration,sound
of the data for the analysis and include data of users who and/or flashing LED. In order to investigate how users per-
responded to at least 14 questionnaires. There are 20 users ceive alerts with different alert modalities, we used the re-
whosatisfiedthisconstraint. So, ourfinaldatasetcomprises sponsesprovidedbytheusersforQ1(Didyounoticethealert
of 10372 notifications, 474 questionnaire responses and 11 (e.g., vibration, sound, flashing LED) for this notification
personalitytestresults. Additionally, duringthesetupphase whenitfirstarrived?).Accordingtoourdataset,whentheno-
we asked participants to enter their age and gender: in our tifications (with which the questionnaires were linked) were
datasetthereare11maleand9femalesagedbetween19and triggeredtheuser’sphonewasfor25.54%ofthetimesinthe
50 years old. However, we do not ask them to provide any silentmode,21.50%vibratemode,41.94%soundmodeand
otherdemographicinformation. 11.03%soundwithvibrate.
As we are primarily using the questionnaire responses, we Usersreportedthattheymissednotificationalertsfor14.63%,
comparedtheclickrate(i.e., percentageofnotificationsthat 15.38%,23.75%,21.05%oftimestheirphonewasinsilent,
are clicked out of total notifications) of the overall notifica- vibrate, sound, and sound with vibrate mode respectively.
tionswiththenotificationsthatwerelinkedtoquestionnaires. Thisprovidesevidencethatwhenthephoneisinsilentmode
Theclickrateforoverallnotificationsis62.52%,andforno- usersarestillawareofthenotificationalerts.
tifications linked with questionnaires is 70.04%. Note that
a notification is considered to be clicked either when it is WhatFactorsInfluencetheSeenTime?
clickedonthenotificationbarorwhenitscorrespondingap- Weinvestigatetheroleofalertmodality,senderandtheongo-
plicationislauncheddirectly. ingtasktype,complexityandcompletionlevel,ininfluencing
theseentimeofanotification.
We are aware that our dataset has potential limitations that
stem from the inherent nature of an in-the-wild study. The TheImpactofAlertModalityonSeenTime
dataset remains unbalanced because it is not possible to ob- In order to perform this analysis, from our dataset of 10372
tain an equal number of questionnaire responses from all notificationsweuseallthenotificationsofthe20userswho
users for each test category. For instance, there is a very respondedtoatleast14questionnaires. However,weignore
smallchancethatourapplicationtriggersaquestionnairefor the notifications that arrived when the user was already en-
eachtypeofsenderfromtherecipientssocialcircle. Further- gagedwiththephonebecausewecouldnotcalculatetheseen
more,inpractice,ausermightnotevenreceivenotifications timeofthesenotifications. Thisleavesuswith4929notifica-
fromeachofthesendertypesduringtheperiodofthestudy. tions. Aone-wayAnalysisofVariance(ANOVA)oftheseen
Therefore, we use the data from 20 users who responded to timewascarriedoutforeachalertmodality.Theresultsshow
atleast14questionnaires,i.e.,theminimumnumberofques- thatthealertmodalityhasanimpactontheseentimeofnoti-
tionnaires that were answered by users in this set. We also fications,withF(3,4925)=26.41, p<0.001. ATukeypost-
makeanhypothesisofdataindependence(i.e.,datainstances hoctest(bysettingthe↵=0.05)revealedthattheseentime
are obtained from different users) which might not hold in isstatisticallysignificantlyhigherforsilentnotifications(av-
realityandcanbetestedonlyinacontrolledsetting. erage 7.332 mins). The seen time for the notifications alert-
ing withvibrate onlymode is thelowest (average 3minutes
UNDERSTANDINGRESPONSETIME and21second). Soundonlyandsoundwithvibratenotifica-
Inthissectionweinvestigatetheeffectofdifferentfactorson tionsarethesecond(average5minutesand57seconds)and
theseenanddecisiontimeofanotification. third (average 4 minutes and 50 seconds) most quickly seen
byusers. Quiteinterestingly,arecent15-userstudybyPielot
etal.[29]alsofoundthatnotificationstendtobeseenfaster WhatFactorsInfluencetheDecisionTime?
whenthephoneisinthevibratemode. Here,weconfirmthis Weanalyzetheeffectofthetype,complexityandcompletion
finding, but also point to the above missed notification per- level of the ongoing task, and the sender type on the time a
centageinthesilentmode(14.63%)andshowthatsettingthe usertakestodecidehowtoreacttoanotification.Wefindthat
phonetosilentdoesnothelpinescapinginterruptions. neither of these factors have a statistically significant effect
onthedecisiontimeofnotificationswiththeexceptionofthe
TheImpactofOngoingTaskTypeonSeenTime sender.
To investigate the impact of the ongoing task on the no-
A one-way repeated measures ANOVA of the decision time
tification’s seen time, we require the type of task that
wascarriedoutforeachsendertype.Theresultsshowthatthe
the users were involved with when the notification arrived.
sender type has an impact on the seen time of notifications
We classified the information users provided through ESM
with F(10,212) = 2.429, p = 0.00936. A Tukey post-hoc
questionnaires about the ongoing task into the following
test(bysettingthe↵=0.05)revealedthatoutofthe11sender
six categories: work, communication, traveling, mainte-
types(showninTable2),notificationsfrompartnerleadtothe
nance/personal,leisureandidle. Theclassificationwasdone
fastestdecisiontime(meanDTis3.315s),followedbyimme-
manually, by two coders who initially disagreed on five en-
diatefamilymemberswithanaveragedecisiontimeof4.891
tries.Twocommonlabelsforthesewerefoundafteradiscus-
seconds. Ontheotherhand,notificationsfromextendedfam-
sionwith threeother coders. Note thatourapp allowsusers
ily members and service providers have the largest decision
toskipthestepofprovidingtheinformationonthequestion
time, 11.93 and 8.146 seconds respectively. There was no
about their current task by selecting the “Prefer not to say"
statistically significant difference in the decision time of the
option. Insuchcases,wediscardtheentryfromouranalysis
notifications from other senders. These results demonstrate
oftheeffectoftheongoingtaskoninterruptibility.
that notifications are quickly handled when they are sent by
A one-way ANOVA of the seen time is carried out for each thecloserelativesoftheuser. Inothercasesuserstakemore
task type. The results show that the ongoing task type has time in reading the content before deciding how to handle
animpactontheseentimeofnotifications,withF(5,217) = it. Wehypothesizethatthisbehaviorstemsfromthecontent
2.963, p = 0.013. ATukeypost-hoctest(bysettingthe↵as ofnotificationsfromclosefriendsorfamilymembers,which
0.05) reveals that the seen time is the lowest when the noti- mightbemorepredictable,andapartofadailyroutine(e.g.,
ficationsarrivewhiletheuseriscommunicating(average47 “pickkidsfromschool").Ontheotherhand,theusershaveto
seconds)andhighestwhiletheuserisidle(average9minutes spendmoretimeonthenotificationsfromlessfrequentlycon-
and30seconds). Othertasktypesdonothaveastatistically tactedsources,asthecontentmaybelessfamiliartothem.
significant effect on the seen time of notifications and have
anaverageseentimeof5minutes45seconds. Asshownin
arecentstudy[31]notificationsaremorewelcomewhenre- TheRoleofNotificationPresentation
cipientsarebored. However,ourresultsshowthatwhilethe Inourdataset,2953(outof7795)notificationswerereceived
usersmightbewillingtoacceptmorenotificationswhenidle, whentheuserwasengagedwiththephone.Outofthese2953
thetimeneededtoattendtosuchnotificationsmightbehigher notifications,860areso-called“low-priority"while2093are
comparedtothetimeneededtoattendtoanotificationwhile “high-priority”notifications[4]. Here,ahigh-prioritynotifi-
auserisbusy. cationisaforegroundnotificationthatgetsinthewayofthe
user’s ongoing activity and the user cannot perform any ac-
tiontogetitoutofthewaywithoutclickingordismissingit
TheImpactofOngoingTaskComplexityonSeenTime
To analyze the effect of ongoing task complexity, we first (e.g. Vibermessages). Alow-priorityonesimplyappearson
encode the reported task complexity, which was reported the notification bar without getting in the way of the user’s
as a value on the Likert scale (Strongly disagree=1, Some- ongoingactivity(e.g. Gmailnotifications).
whatdisagree=2,Neutral=3,Somewhatagree=4andStrongly
We investigate the effect of the notification presentation on
agree=5)tothequestion“Thetask/activityIwasdoingwhen
the response time (i.e., the sum of seen time and decision
thenotificationarrivedwascomplex". TheSpearman’srank
time)ofanotification.Theresultofatwosamplet-testshows
correlation coefficient is computed to evaluate the relation-
thatthereisastatisticallysignificanteffectofnotificationpri-
shipbetweenthecomplexityofanongoingtaskandtheseen ority on the response time, t(2951) = 17.694, p < 0.001,
timeofanotification. Theresultsshowthatthereisaweak,
withhigh-prioritynotificationsgettingquickerresponsethan
negative correlation between the two variables, ⇢ = 0.183,
low-prioritynotifications. Themeanresponsetimeforhigh-
�
p = 0.005. Thus, the increase in the seen time of notifi-
prioritynotificationsis11.94sversus25.91sforlow-priority
cations is correlated with the decrease in rating of ongoing
notifications.
task’scomplexity. Webelievethatthiscorrelationexistsbe-
cause the users become more alert while performing a com-
plextaskandthus,quicklyperceivetheinterruptions. Onthe WHYANOTIFICATIONBECOMESDISRUPTIVE
other hand, when the users are not performing any complex In this section we investigate the effect of different factors
task,theybecomelessattentivetotheinterruptions. Finally, on the perceived disruption. Since the perceived disruption
wefoundthatfactorssuchasthecompletionleveloftheon- wasmeasuredwitha5-pointLikertscale,weencodethere-
goingtaskandthesendertypedonothaveastatisticallysig- sponsesas:Stronglydisagree=1,Somewhatdisagree=2,Neu-
nificanteffectontheseentimeofnotifications. tral=3,Somewhatagree=4andStronglyagree=5.
comingfromtheirfamilyandfriends[15],andthatthemore
Thekeyfindingsofthissectionare: “distant" the sender is, the less likely it is that a notification
willbeclickedon[23]. Resultsfromourstudycomplement
Perceiveddisruptionincreaseswiththeincreaseinthe
• thiswiththefindingthattheperceiveddisruptionvarieswith
complexityofanongoingtask.
thesenderofanotification.
Notifications are perceived as most disruptive if they
•
arrivewhentheuserisinthemiddleoforfinishinga
TheRoleofOngoingTaskType
task,andleastdisruptiveiftheuserisidleorstartinga
Aone-wayANOVAofthereporteddisruptioniscarriedout
newtask.
for each type of ongoing task (see Table 2). The results
Messages from subordinates and system messages show that the type of task that the user is engaged with on
•
(where the sender is not a person) are considered as the arrival of a notification has a significant impact on the
most disruptive. Whereas, extended family members disruption the user perceives when the notification arrives,
areconsideredastheleastdisruptive. F(5,380) = 13.03, p < 0.001. A Tukey post-hoc test (by
setting↵=0.05)revealedthattheperceiveddisruptionisthe
highest when the user is working and the lowest while the
TheRoleofOngoingTaskComplexity user is idle. After work, traveling and then leisure are the
We investigate whether the complexity of an ongoing task tasks where the users perceive the highest level of disrup-
is associated with the perceived disruption reported by the tion. When the users are not idle, they perceive least dis-
users. AKendall’sTaucorrelationcoefficientwascomputed ruptionwhilecommunicatinganddoingapersonalormain-
toassesstherelationshipbetweentheongoingtaskcomplex- tenancetask. Sincethecommunicationcaninvolvenotifica-
ityandperceiveddisruption. Wefoundastrong,positivecor- tions themselves, e.g. two mobile users exchanging What-
relation between the two variables, R⌧ = 0.477, p < 0.001. sAppmessages,theaboveresultisnotsurprising. Asshown
This demonstrates that the users are likely to get more dis- inarecentstudy[31],usersarereceptivetoinformationwhen
rupted by a notification that arrives when they are engaged theyarebored. Ourresultsareinlinewiththesefindingsin
inanintricatetaskandlessdisruptedwhentheyareperform- showingthatperceiveddisruptionislowestwhentheuseris
ing a simple task. In our preliminary analysis [27] we have idle.
foundthatwhenusersareengagedincomplextaskstheyalso
expressmoreofanegativesentimenttowardsinterruptions. UNDERSTANDING THE ACCEPTANCE OF NOTIFICA-
TIONS
TheRoleofOngoingTaskCompletionLevel Inthissectionweinvestigatethefactorsthatmaketheusers
Aone-wayANOVAofthereporteddisruptionwascarriedout accept(click)ordismissanotification.
foreachclassoftaskcompletionlevel(starting,inthemiddle,
finishingandnotdoinganything). Theresultsshowthatthe
completionlevelofanongoingtaskhasasignificantimpact Thekeyfindingsofthissectionare:
on the disruption perceived by the users from the notifica-
Likelihood of the acceptance of a notification de-
tions, F(3,451) = 19.43, p < 0.001. A Tukey post-hoc test •
creaseswiththeincreaseintheperceiveddisruption.
(bysetting↵as0.05)revealsthattheperceiveddisruptionis
thehighestwhentheuseriscurrentlyinvolvedinatask. The Disruptivenotificationsareacceptedbecausetheycon-
•
perceiveddisruptionisthelowestwhentheuserisstartinga tainusefulinformation.
taskoridleandthereisnostatisticallysignificantdifference
betweenthesegroups. Theseresultsshowthattheperceived
Procedure
disruption when the user is highly engaged in a task is very
Throughthequestionnaires,weaskedtheusersthereasonfor
highnotonlyfromthedesktopnotification, asdiscussedfor
clicking/dismissinganotification(seeTable2). Ifanotifica-
examplein[13,24],butalsofromthemobilenotifications.
tion(linkedwiththequestionnaire)isclickedbytheuser,we
askthemtoselectallfactorsthatmadethemdecidetoclick
TheRoleofSender
the notification, otherwise, we ask them to select the factors
Wecomputeaone-wayANOVAofthereporteddisruptionfor
that made them decide to dismiss the notification. We pro-
each type of sender (see Table 2). According to the results,
vide a predefined list of seven and six options for clicking
F(10,444) = 3.987, p < 0.001,thetypeofsenderhasasig-
(seeTable3)anddismissing(seeTable4)thenotification. In
nificantimpactonthedisruptionperceivedbytheusersfrom
addition,thereisaboxforopen-endedanswersincaseusers
the notifications. A Tukey post-hoc test (by setting the ↵ as
donotfindanappropriateanswerintheprovidedlist.
0.05)revealsthattheperceiveddisruptionishighestwhenthe
sender is not a person or is a subordinate at work (no statis- In Table 3 and Table 4 we calculate the percentage of times
tically significant difference between these two groups) and eachfactorwasreportedasareasonforclickinganddismiss-
the lowest when the sender is an extended family member. ing the notifications. Since, users may select more than one
Moreover, colleagues and service providers are the second option,thetotalcountpercentageinthetableaddsuptomore
mostdisruptivesendergroups. Thereisnosignificantdiffer- than 100%. According to these responses, the users mostly
encebetweentheothergroups. Previousstudiesshowedthat accept notifications when they are free, but also the impor-
usersexpressanegativesentiment towardsthemessagesnot tance of the sender and the usefulness of the content make
Option Count(%) Option Count(%)
Senderisimportant 31.546 Senderisimportant 25.926
Thecontentisimportant 27.129 Thecontentisimportant 33.333
Thecontentisurgent 14.511 Thecontentisurgent 20.370
Thecontentisuseful 31.546 Thecontentisuseful 35.185
Iwaswaitingforthisnotification 15.773 Iwaswaitingforthisnotification 11.111
Theactiondemandedbythesenderdoesnotrequire Theactiondemandedbythesenderdoesnotrequire
20.189 16.667
alotofeffort alotofeffort
Atthismoment,Iwasfree 37.224 Atthismoment,Iwasfree 18.519
Table3.Userresponseaboutwhytheyaccept(click)notifications. Table5.Userresponseaboutwhytheyacceptdisruptivenotifications.
Option Count(%) Estimated
Variable Std.Error tvalue pvalue
Senderisnotimportant 19.565 Coefficient
Thecontentisnotimportant 40.580 Extroversion 0.017481 0.005694 3.070 0.0278*
Thecontentisnoturgent 43.478 Agreeableness -0.012833 0.005387 -2.382 0.0630
Thecontentisnotuseful 38.406 Conscientiousness 0.005942 0.004420 1.344 0.2366
Theactiondemandedbythesenderdoesrequirea Neuroticism 0.008659 0.004042 2.142 0.0851
3.623
lotofeffort Openness -0.003114 0.005369 -0.580 0.5870
Atthismoment,Iwasbusy 19.565 N=11
Table4.Userresponseaboutwhytheydismissnotifications. R2=0.737
F(5,5)=2.802(p=0.01413)
Table6. Resultsoflinearregressionwiththeaveragedisruptionasa
themacceptanotification. Ontheotherhand,usersavoidat- dependentvariableandthepersonalitytraitsasindependentvariables.
tendingtonotificationsthatdonotcontainimportant,urgent
orusefulcontent. Theseresponsesdemonstratethatthevalue
showsthat104outof474notifications(withwhichtheques-
ofcontentisusedfordecidingwhethertoclickordismissa
tionnaires were linked) were reported as disruptive. These
notification. Moreover, the users very rarely state that they
arethenotificationsforwhichtheusersomewhatandstrongly
werebusyandthushadtodismissanotification. Thiscould
agreedthattheyperceiveddisruptionfromthesenotifications.
indicate that the users give precedence to a notification over
theprimarytask,butonlyifthecontentisvaluable. However,54%ofthesedisruptivenotificationswereaccepted
(clicked)bytheusers,regardlessofthefactthattheycaused
DisruptiveNotificationsareLikelytobeDismissed disruption. To investigate the reason for this, we checked
We examine the impact of the disruption caused by the no- users’responsesaboutthefactorsthatmadethemclickthese
tifications on their likelihood of being accepted. In order to notifications. Table5showsthepercentageoftimeseachfac-
quantifythis,weencodedtheresponseforperceiveddisrup- torwasreportedbytheusersforacceptingthedisruptiveno-
tion with the following values: Strongly disagree=1, Some- tifications. Asuserswereallowedtoselectmorethanoneop-
whatdisagree=2,Neutral=3,Somewhatagree=4andStrongly tion,thesumofthepercentagesinthetableaddsuptomore
agree=5. In order to detect the acceptance of a notification, than100. "Contentisimportant"and"Contentisuseful"are
we check whether it was clicked by the user. In case it was themostdominantreasonprovidedbytheusersforclicking
dismissed,wecross-validatetheuser’sresponsefortheques- thedisruptivenotifications. Thistellsusthateventhenotifi-
tion How did you handle the notification? If the user re- cationscontainingimportantorusefulcontentcancausedis-
spondedthatIdecidedtodismissitbecauseitdidn’trequire ruption.Wesuspectthatthesenotificationsmaycontainvalu-
anyfurtheraction,wemarkthisnotificationasaccepted. Fi- ableinformation,buttheywerenotrelevantatthemomentof
nally, we use 0 to indicate that the notification is dismissed delivery. However, our study remains limited to make such
and1foranacceptednotification. conclusions and provide an understanding about why users
perceivedisruptionfromusefulnotifications.
We fit a logistic regression model to estimate the effect of
perceived disruption on the likelihood of the acceptance of
notifications. ThemodelwasstatisticallysignificantX2(1) = DOESPERSONALITYMATTER
In this section we investigate the role of personality on the
48.3, p < 0.001. The results indicate the likelihood of the
reported disruption, seen time and decision time of notifica-
acceptance of a notification decreases by 0.581 times (95%
tions. We computed the score for the five personality traits
confidence interval limits for the slope were [0.497, 0.675])
(i.e., the so-called Big Five: Extroversion, Agreeableness,
for a unit increase in the perceived disruption (based on the
Conscientiousness, Neuroticism and Openness) for each of
5-pointLikertscale).
the 11 users who fully completed the exit questionnaire that
However,thecoefficientofdeterminationforthefittedmodel includesthe50questionsrelatedtopersonalitytraits.Forthis
isnothigh(R2 =0.1434),whichimpliesthatnotonlythedis- computation,weusedthescoringinstructionsthatcomewith
ruptionperceivedbytheuser,butalsootherfactorsinfluence thepersonalitytest[16].
theuser’sdecisiontoacceptanotification.
ImpactonReportedDisruption
Whyaredisruptivenotificationsaccepted? Toquantifytherelationshipbetweenthefivepersonalitytraits
As discussed above, the disruption perceived by the user and thedisruption perceived bythe users fromnotifications,
makesanotificationmorelikelytobedismissed. Ourdataset we fit a linear regression model with the average disruption
Estimated Estimated
Variable Std.Error tvalue pvalue Variable Std.Error tvalue pvalue
Coefficient Coefficient
Extroversion 0.44042 0.14926 2.951 0.0319* Extroversion 0.45350 0.13446 3.373 0.0198*
Agreeableness 0.25281 0.14122 1.790 0.1334 Agreeableness 0.29340 0.12722 2.306 0.0692
Conscientiousness -0.42050 0.11586 -3.629 0.0151* Conscientiousness -0.26102 0.10438 -2.501 0.0544
Neuroticism -0.39663 0.10597 -3.743 0.0134* Neuroticism -0.36975 0.09547 -3.873 0.0117*
Openness -0.05059 0.14074 -0.359 0.7339 Openness 0.08584 0.12679 0.677 0.5284
N=11 N=11
R2=0.9007 R2=0.9035
F(5,5)=9.073(p=0.01511) F(5,5)=9.366(p=0.01411)
Table 7. Results of linear regression with the average seen time as a Table8.Resultsoflinearregressionwiththeaveragedecisiontimeasa
dependentvariableandthepersonalitytraitsasindependentvariables. dependentvariableandfivepersonalitytraitsasindependentvariables.
eralizedacrossgroupsofuserswhosharethesamepersonal-
as a dependent variable, and the five personality traits as in-
itytraits.
dependent variables. Here, the average disruption is com-
putedasameanofdisruptionreportedbytheuserthroughthe
IMPLICATIONS
questionnaires. Allresponseswereencodedwiththefollow-
ingvaluesStronglydisagree=1, Somewhatdisagree=2, Neu- DeferringNotifications
tral=3, Somewhat agree=4, and Strongly agree=5. Table 6 Previous studies show that users perceive more disruption
shows the parameters of the fitted linear regression model. fromnotificationswhenengagedinintricatetasksandforthe
Theresultsshowthattheextroversionpersonalitytraitsignif- firsttimeweconfirmthisformobilesettingsinaquantitative
icantlyaffectstheaverageperceiveddisruptionandthatextro- way.Thus,inordertobenefitusers,theOSshouldoffermore
vertsaremoreinclinedtobedisruptedbyanotification. The flexibilitytothemforsettingthebusymomentssothatonly
high value for the coefficient of determination (R2 = 0.737) time-criticalnotificationscouldbetriggered. Thiswouldpo-
shows that the value of average disruption reported by the tentially allow interruptibility management (IM) systems to
users is highly influenced by the personality of users, albeit learnpatternstopredicttheuser’sengagementwithcomplex
thiscouldbeaconsequenceofoursmallsamplesize. tasksandprioritizeinterruptionsaccordingly.
ImprovingNotificationPresentation
ImpactonNotification’sSeenTimeandDecisionTime
Wefoundthatusersbecomemoreattentiveatbusymoments,
We then investigate whether the personality traits influence
butarelikelytoperceivemostnotificationsasdisruptiveand
theseenanddecisiontime. Inordertoperformtheseanaly-
dismiss them. However, disruptive notifications tend to be
ses,wecompute:
accepted if they contain useful content. The presentation of
1. the average seen time of notifications for each user: the notificationsummariescouldbeadaptedtohelpusersquickly
average time taken by a user to view a notification. It is decidewhethertoclickordismissnotifications,e.g. byhigh-
computed as the mean of the seen time of all notification lighting notifications from priority contacts or with priority
receivedbytheuser. contentthatislearntovertimebyanIMsystem.
2. theaveragedecisiontimeofnotificationsforeachuser:the BuildingaPersonality-DependentInterruptibilityModel
averagetimetakenbyausertoclick/dismissafterviewing We observed that the perceived disruption, seen and deci-
anotification. Itiscomputedasthemeanofdecisiontime siontimeareinfluencedbytheuser’spersonalitytraits. This
ofallnotificationsreceivedbytheuser. demonstrates the potential to take the personality trait into
account in interruption models and, for example, generalise
We first fit a linear regression model with the average (per
interruptibility models across groups of users who share the
user)seentimeasadependentvariable,andthefiveperson-
same personality traits. These findings can be exploited to
alitytraitsasindependentvariables.Theparametersofthefit-
designmoreeffectivemachinelearningalgorithmsforintel-
tedlinearregressionmodelareshowninTable7. Theresults
ligentnotifications.
demonstrate that the time in which a notification is viewed
bytheusersissignificantlyinfluencedbytheirExtroversion,
RELATEDWORK
Conscientiousness and Neuroticism personality traits. We
Multitasking is fundamental in workplaces, where task
fitanotherlinearregressionmodelwiththeaveragedecision
switchinghappenseveryfewminutes[17],butalsointhepri-
timeasadependentvariableandthefivepersonalitytraitsas
vatesphere,whereanincreasingnumberofpersonalcomput-
independentvariables. Theparametersofthefittedlinearre-
ing devices mediate the flow of data, be it of entertainment,
gression model are shown in Table 8, and demonstrate that
social or informative nature. Unfortunately, multitasking is
thedecisiontimeforanotificationissignificantlyinfluenced
seldom seamless, since the limited amount of human atten-
bytheuser’sExtroversionandNeuroticismpersonalitytraits.
tion is sought by a range of competing tasks/themes. The
Theneedforindividualmodelsofhumaninterruptibilityhas disruptiveness of interruptions was analyzed by Miata and
beenidentifiedearlier,bothinthedesktopofficesetting[19], Norman [24], who were among the first to note and explain
aswellaswithmobilesmartphoneusers[23]. Theabovere- its variability with the context and particularly their align-
sultsshowpotentialfortheinterruptibilitymodelstobegen- ment with respect to the primary task a user is working on.
Toexplainwhytheinterruptionsaredisruptive,Altmannand behigheveniftheperceiveddisruptionlevelishigh. Onthe
Trafton propose the Memory for Goals model that explains otherhand, thisselfperceptionmightbethemostimportant
how users’ intention move the necessary mental state of the factorthatdeterminestheuser’slongtermsentimenttowards
problembetweentheforegroundandthebackgroundoftheir notifications. When it comes to our ESM sampling, despite
attention and how such state deteriorates when kept in the beingaslightaspossible(weaskonlyuptofourESMques-
background [6]. The importance of the problem state held tionnairesperdayfromeachuser),theyincreasethenumber
inthememorycorrespondstothecomplexityoftheprimary ofnotificationsauserseesduringthedatacollectionperiod.
task and, consequently, recovering after a complex task is Thedensityofnotificationsnegativelyimpactsthesentiment
moredemandingthanifaroutinetaskisinterrupted[9]. towards individual notifications [28]. However, we believe
that in our case the impact is equally distributed among no-
In this paper, to the best of our knowledge we are the first
tifications, andconsequently, thatthefindingsabouttherole
toinvestigatetheroleofthetaskcomplexityoninterruptibil-
of different factors still hold. Finally, while the work is the
ity in the mobile context. Our results confirm the theory of
first to our knowledge to uncover the role of individual psy-
AltmannandTraftonandwefindthatinterruptingacomplex
chological traits on mobile interruptibility, it is important to
task remains disruptive in the mobile setting. However, the
notethatwerelatedthetraitswiththereported interruptibil-
natureofinterruptionsinourstudyisfundamentallydifferent
ity. Therefore, our finding that extrovert people report to be
fromtheabovework–ourusersreceivenotificationssignal-
more disturbed by notifications than others, should be inter-
ing interruptions, and are not “forced" into the interruption,
pretedunderthefactthatgeneralreportingaboutoneselfmost
perse. Thisallowsustoinvestigatemoresubtlephenomena,
likelydependsontheperson’slevelofextroversion.
suchastherelationshipbetweentheprimarytaskandthetime
toregisteranotification. Oneofourkeyfindingsisthatusers
CONCLUSIONS
workingonmorementallydemandingtasksneedlesstimeto
In this paper we have presented a study of mobile interrupt-
notice a notification. We hypothesize that the “high alert"
ibility,specificallyconcentratingontheidentificationoffac-
stateinwhichauseriswhenworkingonacomplextask[21]
tors that make an interruption disruptive and the impact on
alsoleadstoamoreagilereactiontoanotification.
theresponsetimetoanotification. Thecontributionsofthis
Multitasking theories are build upon data acquired in highly study are threefold. First, we have confirmed the validity of
controlled environments. In reality, however, mobile users somepastdesktopinterruptibilitystudiesinamobilesetting.
engage in unconstrained communication with their friends Second,forthefirsttime,wehaveinvestigatedtheroleofno-
andfamily,moveaboutindifferentsurroundingsandgetno- tification presentation, sender-recipient relationship and per-
tifications from a range of applications. Shirazi et al. [32] sonalityformodellinginterruptibility. Finally,ourworkcon-
show that there is a high variation in the way a notification firmsfindingsfromrecentinterruptibilitystudies.
is handled depending on the application with which it is as-
Through a mixed method of automated smartphone logging
sociated. Personalcommunicationapplications,forexample,
andESMsamplingwehaveobtainedadatasetofin-the-wild
are preferred and attended to faster, than applications asso-
notificationsandESMreportsonnotificationperceptionfrom
ciated with utilities and tools. Personal interests, relevance
20users.Wehaveanalysedthedatatoshowthattheresponse
and actionability of the content are additional qualifiers that
time of a notification in the mobile environment is not only
impact a user’s reaction to a notification [15]. In our recent
influenced by an ongoing task’s type, completion level and
workweuncoverthatthecontentandthesender-recipientre-
task complexity, but also by the notification’s alert modal-
lationship play a significant role in the decision to accept a
ity,presentationandsender-recipientrelationship.Ourresults
notification [23]. In this study, we further refine the role of
haveshownthatthepresentationofanotificationanditsalert
the sender and find that the messages from extended family
type, as well as the type, completion level and complexity
membersareperceivedastheleastdisruptive. Moreover,the
ofataskwithwhichtheuserisengaged,allimpacttheseen
contentisoneofthemainreasonsforacceptingthenotifica-
time. Moreover, the relationship with the sender influences
tionseventhoughtheyinterferewiththeuser’scurrenttask.
the user’s decision on accepting a notification or not. The
data also reveals how the sentiment (i.e., perceived disrup-
LIMITATIONS tion) towards a notification varies with the type, completion
Most limitations of the work presented in this paper stem level and complexity of an ongoing task and the recipient’s
from our decision to collect data in the wild, with the min- relationshipwiththesender. Finally,differentpeopleexhibit
imum amount of intervention from our users. For example, different reactions and we observe a substantial role of the
whenitcomestothecomputationoftheseentimeofanoti- individual psychological traits on how a person reacts to a
fication, remotely, we can only detect if a user unlocked the mobilenotification.
phoneandassumethatallnotificationswereseen. Wecannot
detecttheprecisetimewhenauserstartsreadingasummary ACKNOWLEDGEMENTS
of a message from the notification bar. Moreover, in case This work was supported through the EPSRC Grants
a notification arrives when the user is already engaged with “MACACO:Mobilecontext-AdaptiveCAchingforContent-
thephone,weassumethattheuserhasseenthenotification. Centric networking” (EP/L018829/2) and “UBhave:
Further, since our users are not confined to a laboratory, we ubiquitous and social computing for positive behaviour
are limited to self-reported level of disruption from a notifi- change”(EP/I032673/1).
cation. In reality, the impact on the primary task need not