Literature DB >> 27849366

Predicting Smartphone Operating System from Personality and Individual Differences.

Heather Shaw1, David A Ellis2, Libby-Rae Kendrick1, Fenja Ziegler1, Richard Wiseman3.   

Abstract

Android and iPhone devices account for over 90 percent of all smartphones sold worldwide. 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 article, 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 are typically collected from devices and applications running a single smartphone operating system.

Entities:  

Keywords:  brands; extended self; personality; smartphones

Mesh:

Year:  2016        PMID: 27849366     DOI: 10.1089/cyber.2016.0324

Source DB:  PubMed          Journal:  Cyberpsychol Behav Soc Netw        ISSN: 2152-2715


  7 in total

1.  Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions.

Authors:  Tjeerd W Boonstra; Jennifer Nicholas; Quincy Jj Wong; Frances Shaw; Samuel Townsend; Helen Christensen
Journal:  J Med Internet Res       Date:  2018-07-30       Impact factor: 5.428

2.  Predictors of problematic smartphone use among university students.

Authors:  Paulo Guirro Laurence; Yuri Busin; Helena Scoz da Cunha Lima; Elizeu Coutinho Macedo
Journal:  Psicol Reflex Crit       Date:  2020-05-19

3.  Are Austrian practitioners ready to use medical apps? Results of a validation study.

Authors:  Fanni Hofer; Daniela Haluza
Journal:  BMC Med Inform Decis Mak       Date:  2019-04-24       Impact factor: 2.796

4.  Contextual Predictors of Engagement in a Tailored mHealth Intervention for Adolescent and Young Adult Cancer Survivors.

Authors:  Alexandra M Psihogios; Sara King-Dowling; Bridget O'Hagan; Katie Darabos; Laurie Maurer; Jordyn Young; Linda Fleisher; Lamia P Barakat; Dava Szalda; Christine E Hill-Kayser; Lisa A Schwartz
Journal:  Ann Behav Med       Date:  2021-11-18

5.  Timing rather than user traits mediates mood sampling on smartphones.

Authors:  Beryl Noë; Liam D Turner; David E J Linden; Stuart M Allen; Gregory R Maio; Roger M Whitaker
Journal:  BMC Res Notes       Date:  2017-09-16

6.  Smartphone and medical application use among dentists in China.

Authors:  Chao Zhang; Lin Fan; Zhaowu Chai; Cong Yu; Jinlin Song
Journal:  BMC Med Inform Decis Mak       Date:  2020-09-07       Impact factor: 2.796

7.  Process Evaluation of the 'No Money No Time' Healthy Eating Website Promoted Using Social Marketing Principles. A Case Study.

Authors:  Lee M Ashton; Megan E Rollo; Marc Adam; Tracy Burrows; Vanessa A Shrewsbury; Clare E Collins
Journal:  Int J Environ Res Public Health       Date:  2021-03-30       Impact factor: 3.390

  7 in total

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