Exploring User Acceptance Determinants of COVID-19-Tracing Apps to Manage the Pandemic

Abstract

Tracing infectious individuals and clusters is a major tactic for mitigating the pandemic. This paper explores the factors impacting the intentions and actual use of COVID-19 contact tracing apps based on a technology acceptance model. A partial least squares structural equation model has been applied to understand determinants for the usage of tracing apps based on a large sample (N = 2,398) from more than 30 countries (mainly from Germany and USA). Further, the paper presents a classification of COVID-19 apps and users. Through that, the study provides insights for technologists and designers of tracing apps as well as policy makers and practitioners to work toward enhancing user acceptance. Moreover, the results are abstracted to general social participation with apps in order to manage future strategies. The theoretical contribution of this work includes the results of our acceptance model and a classification of COVID-19 tracing and tracking apps.