Citizens’ Attitudes to Contact Tracing Apps

Citizens’ concerns about data privacy and data security breaches may reduce the adoption of COVID-19 contact tracing mobile phone applications, making them less effective. We implement a choice experiment (conjoint experiment) where participants indicate which version of two contact tracing apps the...

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Bibliographic Details
Published inJournal of experimental political science Vol. 9; no. 1; pp. 118 - 130
Main Authors Horvath, Laszlo, Banducci, Susan, James, Oliver
Format Journal Article
LanguageEnglish
Published New York, USA Cambridge University Press 02.09.2020
Subjects
Attitudes
Cellular telephones
Citizens
Contact tracing
Coronaviruses
COVID-19
Data
Data integrity
Experiments
Health services
Hypotheses
Information sharing
Partisanship
Political science
Politics
Preferences
Privacy
Public health
Research Article
Respondents
Theft
United Kingdom > UK
data breach
privacy
Digital contact tracing
conjoint experiment
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Summary:Citizens’ concerns about data privacy and data security breaches may reduce the adoption of COVID-19 contact tracing mobile phone applications, making them less effective. We implement a choice experiment (conjoint experiment) where participants indicate which version of two contact tracing apps they would install, varying the apps’ privacy-preserving attributes. Citizens do not always prioritise privacy and prefer a centralised National Health Service system over a decentralised system. In a further study asking about participants’ preference for digital-only vs human-only contact tracing, we find a mixture of digital and human contact tracing is supported. We randomly allocated a subset of participants in each study to receive a stimulus priming data breach as a concern, before asking about contact tracing. The salient threat of unauthorised access or data theft does not significantly alter preferences in either study. We suggest COVID-19 and trust in a national public health service system mitigate respondents’ concerns about privacy.
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Support for this research was provided by the Economic and Social Research Council (Award No. ES/R005133/1), the Exeter Q-Step Centre and the College of Social Sciences and International Studies at the University of Exeter. Susan Banducci’s work was supported through a Turing Fellowship, The Alan Turing Institute, UK. The authors declare no conflicts of interest. The data, code and any additional materials required to replicate all analyses in this article are available in Horvath et. al. (2020), at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at: doi:10.7910/DVN/KVKGUB.
ISSN:2052-2630
2052-2649
DOI:10.1017/XPS.2020.30