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My Phone and Me: Understanding Peoples Receptivity to Mobile - - PowerPoint PPT Presentation
My Phone and Me: Understanding Peoples Receptivity to Mobile - - PowerPoint PPT Presentation
My Phone and Me: Understanding Peoples Receptivity to Mobile Notifications Abhinav Mehrotra Veljko Pejovic Jo Vermeulen Robert Hendley Mirco Musolesi My Phone and Me: Understanding Peoples Receptivity to Mobile Notifications Abhinav
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My Phone and Me: Understanding People’s Receptivity to Mobile Notifications
Abhinav Mehrotra Veljko Pejovic Jo Vermeulen Robert Hendley Mirco Musolesi
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Anticipatory Mobile Computing
[Veljko Pejovic and Mirco Musolesi. Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges. In ACM Computing Surveys. Volume 47. Issue 3. ACM Press. April 2015.]
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Anticipatory Mobile Computing
Problem: sending the right information at the right time without annoying the user through notifications, maximizing user’s receptivity.
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Anticipatory Mobile Computing
Applications: ads, marketing, but also positive behaviour intervention.
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Notifications are Beneficial
System, Tools and Others Online Social Networks Communication
Notifications provide an effortless way to be aware of newly available information in real-time.
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Issues with Notifications
Ongoing Task Emotional State On arriving at inopportune moments, notifications adversely affect:
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Understanding Interruptibility: State-of-the-art
Users’ receptivity to a notification is determined by:
- their subjective experience in the notification;
- the type of application that triggers it;
- its time criticality and social pressure.
Limitation Cognitive context has not been considered.
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Inferring Interruptibility: State-of-the-art
- context data
- notification content
Limitation Cognitive context has not been considered. Different approaches for predicting interruptibility by using:
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Bridging the Gap
We present the first study to collect objective and subjective data about real-world mobile notifications. We investigate users’ interaction with mobile notifications in different physical and cognitive contexts.
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“My Phone and Me” App
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Dataset
Participants: 20 Minimum questionnaires per participant: 14 Size of the notification sample: 10372 Questionnaire responses: 474
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Results
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Understanding Response Time
Response Time = Seen Time + Decision Time
Seen Time (a) (b) (c1) Decision Time (c2) Click Dismiss
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Understanding Response Time
Seen Time = time from the notification arrival until the notification was seen by the user
Seen Time (a) (b) (c1) Decision Time (c2) Click Dismiss
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Understanding Response Time
Decision Time = time from the moment a user saw a notification until the time they acted upon it (click or dismiss)
Seen Time (a) (b) (c1) Decision Time (c2) Click Dismiss
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Alert Modality Task Type Task Complexity Task Completion Level Relationship with Sender
Impact on Seen Time
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The Impact of Alert Modality
- n Seen Time
2 4 6 8 Silent Vibrate Sound Sound with vibrate
Note 1: we ignored notifications that arrived when the user was already engaged with the phone.
Seen Time (mins)
F = 26.41, p < 0.001 Note 2: similar observations are reported in Pielot et al. MobileHCI’14.
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The Impact of Ongoing Task Type
- n Seen Time
F = 2.963, p = 0.013
2 4 6 8 10 Seen Time (mins)
Note: the information users provided about the ongoing task was manually classified by two coders into six categories.
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The Impact of Ongoing Task Complexity on Seen Time
Spearman’s rank correlation coefficient = -0.18 [p < 0.005]. User’s attentiveness increases (reducing the seen time) with the increase in the complexity of an ongoing task. We encoded the reported task complexity values as ordinal numbers. Strongly disagree =1 Somewhat disagree =2 Neutral =3 Somewhat agree =4 Strongly agree =5
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Alert Modality Task Type Task Complexity Task Completion Level Relationship with Sender
Impact on Decision Time
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The Impact of Sender-Recipient Relationship on Decision Time
F = 2.429, p = 0.009
2 4 6 8 10 12 14 Seen Time (seconds)
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Alert Modality Task Type Task Complexity Task Completion Level Relationship with Sender
Why Do Notifications Become Disruptive?
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The Role of Ongoing Task Type
F = 13.03, p < 0.001 Work Traveling Leisure Communicating and Maintenance Idle Highest Lowest
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The Role of Ongoing Task Completion Level
F = 19.43, p = 0.013 Middle of a task Finishing a task Starting a task or Idle Highest Lowest
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The Role of Sender
F = 3.987, p = 0.013 Sender is not a person and subordinate at work Colleagues and service providers Other senders Extended family Highest Lowest
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Frequent and Not Frequent Senders
- Quite interestingly, the decision time is higher for
the notifications from less frequently contacted senders.
- Conjecture: maybe the content less familiar to
users?
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The Role of Ongoing Task Complexity
Spearman’s rank correlation coefficient = 0.477 [p < 0.001]. Perceived disruption increases with the increase in the complexity of an ongoing task.
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- How did you handle the notification when
you first saw it?
- Select all factors that made you decide to
click/dismiss the notification.
Understanding the Acceptance of Notifications
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Why do Users Accept (click) Notifications?
Option Accept Accept (Disruptive notifications)
Sender is important 31.546 25.926 The content is important 27.129 33.333 The content is urgent 14.511 20.370 The content is useful 31.546 35.185 I was waiting for this notification 15.773 11.111 The action demanded by the sender does not require a lot of effort 20.189 16.667 At this moment, I was free 37.224 18.519
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Why Do Users Dismiss Notifications?
Option Dismiss
Sender is not important 19.565 The content is not important 40.580 The content is not urgent 43.478 The content is not useful 38.406 The action demanded by the sender does require a lot of effort 3.623 I was busy 19.565
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Does Personality Matter?
Linear regression model with the five personality traits as independent variables and reported disruption, seen time and decision time of notifications as dependent variables.
- R2 = 0.737 [for reported disruption]
- R2 =0.9007 [for seen time]
- R2 =0.9035 [for decision time]
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Implications
- It is a good idea to defer non-useful notifications at busy
moments.
- It is possible to improve notification presentation by
displaying useful content.
- We demonstrate that we can build a personality-
dependent interruptibility model.
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Questions?
Mirco Musolesi
University College London E: m.musolesi@ucl.ac.uk W: http://www.ucl.ac.uk/~musolesm T: @mircomusolesi
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Questionnaire I
Did you notice the alert (e.g., vibration, sound, flashing LED) for this notification when it first arrived? How did you handle the notification when you first saw it? Factors that made you to decide to click/dismiss the notification. What best describes your relationships to the sender.
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