The Role of Transparency, Trust and Social Influence on Uncertainty Reduction in Times of Pandemics: An Empirical Study on the Adoption of COVID-19 Tracing Apps

Oldeweme A, Märtins J, Westmattelmann D, Schewe G

Abstract

Background: Contact tracing apps are an essential component of an effective COVID-19 testing strategy to counteract the spread of the pandemic and thereby avoid overburdening the health care system. As the adoption rates in several regions are undesirable, governments must significantly increase the acceptance of COVID-19 tracing apps in these times of enormous uncertainty. Objective: Building on Uncertainty Reduction Theory (URT), we investigated how uncertainty reduction measures foster the adoption of COVID-19 tracing apps and how their usage affects the perception of different risks. Methods: Representative survey data were gathered at two measurement points (before and after the release) and analyzed by performing covariance-based structural equation modelling (n=1003). Results: We found that uncertainty reduction measures in the form of the transparency dimensions disclosure and accuracy, as well as social influence and trust in government, foster the adoption process. The use of the COVID-19 tracing app in turn reduces the perceived privacy and performance risks but does not reduce social risks and health-related COVID-19 concerns. Conclusions: This work contributes to mass-adoption of healthcare technology and URT research by integrating interactive communication measures and transparency as a multi-dimensional concept to reduce different types of uncertainty over time. Furthermore, our results help to derive communication strategies to promote the mass-adoption of COVID-19 tracing apps, thus detecting infection chains and allowing intelligent COVID-19 testing.