Continued Use of Contact-Tracing Applications in US and UK: Insights from A Comparative Study Through the Lens of the Health Belief Model.

Abstract

To contain the spread of the COVID-19 virus, contact-tracing mobile applications were developed and deployed to identify and notify individuals who have exposure to the virus. However, the effectiveness of these apps depends not only on their adoption by the general population but also on their continued use in the long term. Limited research has investigated the facilitators and barriers to the continued use of contact tracing apps. The present study examines factors influencing the continued use intentions of contact tracing apps based on the health belief model. In addition, the differences between users and non-users and between US and UK populations were investigated. A survey was administered in both United States and the United Kingdom. Respondents included individuals who previously used CT technologies and those without experience. The structural equation modeling technique was used to validate the proposed research model and hypotheses. Analysis of data collected from 362 individuals showed that perceived benefits, self-efficacy, perceived severity, perceived susceptibility, and cues to action positively predicted continued use intentions of CT apps, while perceived barriers could reduce them. We observed few differences between the US and UK groups; the only exception was the effect of COVID-19 threat susceptibility, which was significant for the UK group but not for the US group. Lastly, we found that the only significant difference between users and non-users is related to perceived barriers, which may not influence non-users' continued use intentions but significantly reduce the experienced users' intentions. Our findings carry implications for technology design and policy. These insights can potentially help governments, tech companies, and media outlets create strategies and policies to promote app adoption for new users and sustain continued use for existing users in the long run.