Exploring individuals’ adoption of COVID-19 contact-tracing apps: a mixed-methods approach

Abstract

PurposeDuring the challenging time of lockdown and isolation due to the coronavirus disease (COVID-19), contact-tracing apps have played a critical role in health communication and preventive healthcare. This study proposed and tested an extended technology acceptance model (TAM) with key health factors (i.e. health risk perception from COVID-19, health information orientation to COVID-19 and health consciousness) to understand individuals' adoption of COVID-19 contact-tracing apps.Design/methodology/approachA two-stage online survey was conducted to collect data on US individuals’ intention and actual use of COVID-19 contact-tracing apps. The sample comprises 288 valid responses. Partial least squares structural equation modeling (PLS-SEM) and fuzzy set/qualitative comparative analysis (fsQCA) were employed as the complementary approaches.FindingsThe findings from PLS-SEM revealed that health risk perception, health information orientation and perceived usefulness have positive net effects on behavioral intention, which, in turn, affects actual use. The results from fsQCA highlighted the explanatory power of the extended TAM to COVID-19 contact-tracing app adoption.Originality/valueAlthough TAM is considerably effective in measuring technology acceptance, the phenomenon is highly context-driven. How technological and health factors simultaneously motivate the use of contact-tracing apps has not been well documented. The present study offers some implications for practitioners concerned about fostering the adoption of mobile health services in the time of COVID-19. Methodologically, this study is among the first to blend PLS-SEM and fsQCA to measure the explanatory power of a structural model.