Digital Health Innovation: Exploring Adoption of COVID-19 Digital Contact Tracing Apps

Abstract

With the outbreak of COVID-19, contact tracing is becoming a used intervention to control the spread of this highly infectious disease. This article explores an individual's intention to adopt COVID-19 digital contact tracing (DCT) apps. A conceptual framework developed for this article combines the procedural fairness theory, dual calculus theory, protection motivation theory, theory of planned behavior, and Hofstede's cultural dimension theory. The study adopts a quantitative approach collecting data from 714 respondents using a random sampling technique. The proposed model is tested using structural equation modeling. Empirical results found that the perceived effectiveness of privacy policy negatively influenced privacy concerns, whereas perceived vulnerability had a positive influence. Expected personal and community-related outcomes of sharing information positively influenced attitudes toward DCT apps, while privacy concerns had a negative effect. The intention to adopt DCT apps were positively influenced by attitude, subjective norms, and privacy self-efficacy. This article is the first to empirically test the adoption of DCT apps of the COVID-19 pandemic and contributes both theoretically and practically toward understanding factors influencing its widespread adoption