Ready or Not for Contact Tracing? Investigating the Adoption Intention of COVID-19 Contact-Tracing Technology Using an Extended Unified Theory of Acceptance and Use of Technology Model

To diminish the risk of spreading COVID-19 as society exits the lockdowns, several apps have been developed for contact tracing. These apps register which users have been in proximity of each other. If a user is diagnosed with COVID-19, app users who have been recently in proximity to this person are notified. The effectiveness of these apps highly depends on public support. Therefore, this study investigated the factors that influence app use intention, based on an extended unified theory of acceptance and use of technology model. A survey was administered in Belgium (Flanders) to 1,500 participants aged 18 to 64 years old. Structural equation modeling was used to test the relationships among the model's constructs. Our results indicated that 48.70 percent of the respondents wanted to use the app. The model explained 39 percent of the variance in app use intention. The most important predictor was performance expectancy, followed by facilitating conditions and social influence. Effort expectancy was not related to intention. Moreover, individuals' innovativeness was positively related with app use intention, whereas app-related privacy concerns negatively influenced intention. Based on the results, suggestions are made for policy makers and developers.

Automated summary

This study investigated the factors influencing the intention to use COVID-19 contact tracing apps, finding that performance expectancy, facilitating conditions, and social influence were the most important predictors. Effort expectancy was not related to intention, and innovativeness positively influenced app use intention, while app-related privacy concerns negatively affected intention. Policy makers and developers were provided with suggestions based on these results.