Understanding the Impact of Personality Traits on Mobile App Adoption – Insights from a Large-Scale Field Study

Item Type Journal paper
Abstract

The sheer amount of available apps allows users to customize smartphones to match their personality and interests. As one of the first large-scale studies, the impact of personality traits on mobile app adoption was examined through an empirical study involving 2043 Android users. A mobile app was developed to assess each smartphone user’s personality traits based on a state-of-the-art Big Five questionnaire and to collect information about her installed apps. The contributions of this work are two-fold. First, it confirms that personality traits have significant impact on the adoption of different types of mobile apps. Second, a
machine-learning model is developed to automatically determine a user's personality based on her installed apps. The predictive model is implemented in a prototype app and shows a 65% higher precision than a random guess. Additionally, the model can be deployed in a non-intrusive, low privacy-concern, and highly scalable manner as part of any mobile app.

Authors Xu, Runhua; Frey, Remo M.; Fleisch, Elgar & Ilic, Alexander
Research Team Auto-ID Lab
Journal or Publication Title Computers in Human Behavior
Language English
Subjects business studies
HSG Classification contribution to scientific community
HSG Profile Area SoM - Business Innovation
Refereed Yes
Date September 2016
Publisher Elsevier
Place of Publication Amsterdam [u.a.]
Volume 62
Page Range 244-256
Number of Pages 13
ISSN 0747-5632
ISSN-Digital 1873-7692
Publisher DOI 10.1016/j.chb.2016.04.011
Official URL http://www.sciencedirect.com/science/article/pii/S...
Depositing User Elisabeth Vetsch
Date Deposited 03 Aug 2016 08:53
Last Modified 05 Apr 2020 00:24
URI: https://www.alexandria.unisg.ch/publications/248879

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Citation

Xu, Runhua; Frey, Remo M.; Fleisch, Elgar & Ilic, Alexander (2016) Understanding the Impact of Personality Traits on Mobile App Adoption – Insights from a Large-Scale Field Study. Computers in Human Behavior, 62 244-256. ISSN 0747-5632

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https://www.alexandria.unisg.ch/id/eprint/248879
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