TY  - JOUR
AU  - Oldeweme, Andreas
AU  - Märtins, Julian
AU  - Westmattelmann, Daniel
AU  - Schewe, Gerhard
PY  - 2021
DA  - 2021/2/8
TI  - The Role of Transparency, Trust, and Social Influence on Uncertainty Reduction in Times of Pandemics: Empirical Study on the Adoption of COVID-19 Tracing Apps
JO  - J Med Internet Res
SP  - e25893
VL  - 23
IS  - 2
KW  - Uncertainty Reduction Theory
KW  - URT
KW  - COVID-19
KW  - tracing app
KW  - mobile health care adoption
KW  - DCA-transparency
KW  - social influence
KW  - initial trust
KW  - public health
KW  - eHealth
KW  - communication
KW  - trust
KW  - surveillance
KW  - monitoring
KW  - app
KW  - empirical
KW  - risk
KW  - use
AB  - 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 increase the acceptance of COVID-19 tracing apps in these times of uncertainty. Objective: Building on the Uncertainty Reduction Theory (URT), this study aims to investigate how uncertainty reduction measures foster the adoption of COVID-19 tracing apps and how their use affects the perception of different risks. Methods: Representative survey data were gathered at two measurement points (before and after the app’s release) and analyzed by performing covariance-based structural equation modeling (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 reduced the perceived privacy and performance risks but did not reduce social risks and health-related COVID-19 concerns. Conclusions: This study contributes to the mass adoption of health care technology and URT research by integrating interactive communication measures and transparency as a multidimensional 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2021/2/e25893
UR  - https://doi.org/10.2196/25893
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33465036
DO  - 10.2196/25893
ID  - info:doi/10.2196/25893
ER  -