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I'll be there for you: Quantifying Attentiveness towards Mobile Messaging

Published: 24 August 2015 Publication History

Abstract

Social norm has it that people are expected to respond to mobile phone messages quickly. We investigate how attentive people really are and how timely they actually check and triage new messages throughout the day. By collecting more than 55,000 messages from 42 mobile phone users over the course of two weeks, we were able to predict people's attentiveness through their mobile phone usage with close to 80% accuracy. We found that people were attentive to messages 12.1 hours a day, i.e. 84.8 hours per week, and provide statistical evidence how very short people's inattentiveness lasts: in 75% of the cases mobile phone users return to their attentive state within 5 minutes. In this paper, we present a comprehensive analysis of attentiveness throughout each hour of the day and show that intelligent notification delivery services, such as bounded deferral, can assume that inattentiveness will be rare and subside quickly.

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Cited By

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  • (2024)Pinning, Sorting, and Categorizing Notifications: A Mixed-methods Usage and Experience Study of Mobile Notification-management FeaturesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785798:3(1-27)Online publication date: 9-Sep-2024
  • (2024)Investigating User-perceived Impacts of Contextual Factors on Opportune MomentsProceedings of the ACM on Human-Computer Interaction10.1145/36765148:MHCI(1-28)Online publication date: 24-Sep-2024
  • (2024)"I Want Lower Tone for Work-Related Notifications": Exploring the Effectiveness of User-Assigned Notification Alerts in Improving User Speculation of and Attendance to Mobile NotificationsProceedings of the ACM on Human-Computer Interaction10.1145/36765128:MHCI(1-25)Online publication date: 24-Sep-2024
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    cover image ACM Conferences
    MobileHCI '15: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services
    August 2015
    611 pages
    ISBN:9781450336529
    DOI:10.1145/2785830
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 24 August 2015

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    Author Tags

    1. Attentiveness
    2. Availability
    3. Bounded Deferral
    4. Interruptibility
    5. Mobile Devices
    6. Responsiveness

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    Overall Acceptance Rate 202 of 906 submissions, 22%

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    Cited By

    View all
    • (2024)Pinning, Sorting, and Categorizing Notifications: A Mixed-methods Usage and Experience Study of Mobile Notification-management FeaturesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785798:3(1-27)Online publication date: 9-Sep-2024
    • (2024)Investigating User-perceived Impacts of Contextual Factors on Opportune MomentsProceedings of the ACM on Human-Computer Interaction10.1145/36765148:MHCI(1-28)Online publication date: 24-Sep-2024
    • (2024)"I Want Lower Tone for Work-Related Notifications": Exploring the Effectiveness of User-Assigned Notification Alerts in Improving User Speculation of and Attendance to Mobile NotificationsProceedings of the ACM on Human-Computer Interaction10.1145/36765128:MHCI(1-25)Online publication date: 24-Sep-2024
    • (2024)Exploring freedom in mobile connectivity: a moderated mediation model linking mobile social media modes, availability pressure, and media habitsChinese Journal of Communication10.1080/17544750.2024.2358250(1-17)Online publication date: 25-May-2024
    • (2024)A Digital Nudge-Based Intervention to Interrupt Instagram UsageEuropean Journal of Health Psychology10.1027/2512-8442/a00015031:3(128-140)Online publication date: Sep-2024
    • (2024)Social media use is predictable from app sequencesComputers in Human Behavior10.1016/j.chb.2024.108381161:COnline publication date: 1-Dec-2024
    • (2023)Scanning or Simply Unengaged in Reading? Opportune Moments for Pushed News Notifications and Their Relationship with Smartphone Users' Choice of News-reading ModesProceedings of the ACM on Human-Computer Interaction10.1145/36042687:MHCI(1-26)Online publication date: 13-Sep-2023
    • (2023)Multiple Device Users’ Actual and Ideal Cross-Device Usage for Multi-Stage Notification-Interactions: An ESM Study Addressing the Usage Gap and Impacts of Device ContextProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580731(1-15)Online publication date: 19-Apr-2023
    • (2023)Are You Killing Time? Predicting Smartphone Users’ Time-killing Moments via Fusion of Smartphone Sensor Data and ScreenshotsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580689(1-19)Online publication date: 19-Apr-2023
    • (2023)Alert Now or Never: Understanding and Predicting Notification Preferences of Smartphone UsersACM Transactions on Computer-Human Interaction10.1145/347886829:5(1-33)Online publication date: 6-Jan-2023
    • Show More Cited By

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