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    Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Human-Computer Studies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Human-Computer Studies, 130, 2019 DOI: 10.1016/j.ijhcs.2019.05.004

    Accepted author manuscript, 436 KB, PDF document

    Embargo ends: 16/05/20

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Do smartphone usage scales predict behavior?

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>1/10/2019
<mark>Journal</mark>International Journal of Human-Computer Studies
Volume130
Number of pages7
Pages (from-to)86-92
Publication statusPublished
Early online date16/05/19
Original languageEnglish

Abstract

Understanding how people use technology remains important, particularly when measuring the impact this might have on individuals and society. However, despite a growing body of resources that can quantify smartphone use, research within psychology and social science overwhelmingly relies on self-reported assessments. These have yet to convincingly demonstrate an ability to predict objective behavior. Here, and for the first time, we compare a variety of smartphone use and ‘addiction’ scales with objective behaviors derived from Apple’s Screen Time application. While correlations between psychometric scales and objective behavior are generally poor, single estimates and measures that attempt to frame technology use as habitual rather than ‘addictive’ correlate more favorably with subsequent behavior. We conclude that existing self-report instruments are unlikely to be sensitive enough to accurately predict basic technology use related behaviors. As a result, conclusions regarding the psychological impact of technology are unreliable when relying solely on these measures to quantify typical usage.

Bibliographic note

This is the author’s version of a work that was accepted for publication in International Journal of Human-Computer Studies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Human-Computer Studies, 130, 2019 DOI: 10.1016/j.ijhcs.2019.05.004