Do smartphone usage scales predict behavior?

David A. Ellis, Brittany Davidson, Heather Shaw, Kris Geyer

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)86-92
Number of pages7
JournalInternational Journal of Human-Computer Studies
Volume130
Early online date16 May 2019
DOIs
Publication statusPublished - 1 Oct 2019

ASJC Scopus subject areas

  • Software
  • Human Factors and Ergonomics
  • Education
  • Engineering(all)
  • Human-Computer Interaction
  • Hardware and Architecture

Cite this

Do smartphone usage scales predict behavior? / Ellis, David A.; Davidson, Brittany; Shaw, Heather; Geyer, Kris.

In: International Journal of Human-Computer Studies, Vol. 130, 01.10.2019, p. 86-92.

Research output: Contribution to journalArticle

Ellis, David A. ; Davidson, Brittany ; Shaw, Heather ; Geyer, Kris. / Do smartphone usage scales predict behavior?. In: International Journal of Human-Computer Studies. 2019 ; Vol. 130. pp. 86-92.
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