TY - JOUR
T1 - What makes people install a COVID-19 contact-tracing app? Understanding the influence of app design and individual difference on contact-tracing app adoption intention
AU - Li, Tianshi
AU - Cobb, Camille
AU - Yang, Jackie (Junrui)
AU - Baviskar, Sagar
AU - Agarwal, Yuvraj
AU - Li, Beibei
AU - Bauer, Lujo
AU - Hong, Jason I.
N1 - Funding Information:
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This work is supported in part by CMU Block Center and the National Science Foundation under Grant No. 1801472.
Publisher Copyright:
© 2021 The Authors
PY - 2021/8
Y1 - 2021/8
N2 - Smartphone-based contact-tracing apps are a promising solution to help scale up the conventional contact-tracing process. However, low adoption rates have become a major issue that prevents these apps from achieving their full potential. In this paper, we present a national-scale survey experiment (N=1963) in the U.S. to investigate the effects of app design choices and individual differences on COVID-19 contact-tracing app adoption intentions. We found that individual differences such as prosocialness, COVID-19 risk perceptions, general privacy concerns, technology readiness, and demographic factors played a more important role than app design choices such as decentralized design vs. centralized design, location use, app providers, and the presentation of security risks. Certain app designs could exacerbate the different preferences in different sub-populations which may lead to an inequality of acceptance to certain app design choices (e.g., developed by state health authorities vs. a large tech company) among different groups of people (e.g., people living in rural areas vs. people living in urban areas). Our mediation analysis showed that one's perception of the public health benefits offered by the app and the adoption willingness of other people had a larger effect in explaining the observed effects of app design choices and individual differences than one's perception of the app's security and privacy risks. With these findings, we discuss practical implications on the design, marketing, and deployment of COVID-19 contact-tracing apps in the U.S.
AB - Smartphone-based contact-tracing apps are a promising solution to help scale up the conventional contact-tracing process. However, low adoption rates have become a major issue that prevents these apps from achieving their full potential. In this paper, we present a national-scale survey experiment (N=1963) in the U.S. to investigate the effects of app design choices and individual differences on COVID-19 contact-tracing app adoption intentions. We found that individual differences such as prosocialness, COVID-19 risk perceptions, general privacy concerns, technology readiness, and demographic factors played a more important role than app design choices such as decentralized design vs. centralized design, location use, app providers, and the presentation of security risks. Certain app designs could exacerbate the different preferences in different sub-populations which may lead to an inequality of acceptance to certain app design choices (e.g., developed by state health authorities vs. a large tech company) among different groups of people (e.g., people living in rural areas vs. people living in urban areas). Our mediation analysis showed that one's perception of the public health benefits offered by the app and the adoption willingness of other people had a larger effect in explaining the observed effects of app design choices and individual differences than one's perception of the app's security and privacy risks. With these findings, we discuss practical implications on the design, marketing, and deployment of COVID-19 contact-tracing apps in the U.S.
KW - Contact-tracing apps
KW - COVID-19
KW - Quantitative analysis
KW - Security and privacy
KW - Survey experiment
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U2 - 10.1016/j.pmcj.2021.101439
DO - 10.1016/j.pmcj.2021.101439
M3 - Article
AN - SCOPUS:85108376621
SN - 1574-1192
VL - 75
JO - Pervasive and Mobile Computing
JF - Pervasive and Mobile Computing
M1 - 101439
ER -