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    Rights statement: Final publication is available from Mary Ann Liebert, Inc., publishers http://dx.doi.org/10.1089/cyber.2016.0324

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Predicting smartphone operating system from personality and individual differences

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>7/12/2016
<mark>Journal</mark>Cyberpsychology, Behavior, and Social Networking
Issue number12
Number of pages6
Pages (from-to)727-732
Publication StatusPublished
Early online date16/11/16
<mark>Original language</mark>English


Android and iPhone devices account for over 90% of all smartphones sold world-wide. Despite being very similar in functionality, current discourse and marketing campaigns suggest that key individual differences exist between users of these two devices; however, this has never been investigated empirically. This is surprising, as smartphones continue to gain momentum across a variety of research disciplines. In this paper we consider if individual differences exist between these two distinct groups. In comparison to Android users, we found that iPhone owners are more likely to be female, younger, and increasingly concerned about their smartphone being viewed as a status object. Key differences in personality were also observed with iPhone users displaying lower levels of honesty-humility and higher levels of emotionality. Following this analysis, we were also able to build and test a model that predicted smartphone ownership at above chance level based on these individual differences. In line with extended self theory, the type of smartphone owned provides some valuable information about its owner. These findings have implications for the increasing use of smartphones within research particularly for those working within Computational Social Science and PsychoInformatics, where data is typically collected from devices and applications running a single smartphone operating system.

Bibliographic note

Final publication is available from Mary Ann Liebert, Inc., publishers http://dx.doi.org/10.1089/cyber.2016.0324