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.

The impact of environmental risk and platform trust on satisfaction with health qr code use

“…Second, environmental risks do not directly affect public health code usage satisfaction, but have an indirect effect through the mediation of perceived quality. This is generally consistent with the findings of foreign studies on risk perception and digital tracking software ( 39 , 40 ). The direct effect of environmental risk on satisfaction with health code use is not significant.…”

Examining the Prediction of COVID-19 Contact-Tracing App Adoption Using an Integrated Model and Hybrid Approach Analysis

“…The information system literature about the technology adoption model primarily predicts behavioral intention, usage intention, or adoption intention as the dependent variable. Most of the studies investigate this variable predictive power through PLS-SEM ( 10 , 34 , 39 ) or CB-SEM ( 35 – 37 , 41 ). One of the primary criteria used to examine the model's predictive capacity is the coefficient of determination ( R 2 ).…”

“…Specifically, Duan and Deng ( 37 ) employed CB-SEM with an artificial neural network (ANN) approach to investigate the CTA adoption through the lens of the unified theory of acceptance and use of technology and privacy calculus theory in Australia. In contrast, the study by Nguyen et al ( 39 ) investigated the extended technology adoption model through PLS-SEM and fuzzy set/qualitative comparative analysis (fsQCA). The summary of the review findings is presented in Table 1 .…”

Guest editorial: COVID-19 Pandemic and Health Informatics Part 2

“…The pandemic provides opportunities for advancing contact tracing and telemedicine technology. Nguyen et al (2022) explored the adoption of contact tracing apps in the US through partial least squares structural equation modelling (PLS-SEM) and fuzzy set/ qualitative comparative analysis (fsQCA). They showed that health risk perception, health information orientation and perceived usefulness of the contact tracing apps positively affected users' adoption and actual use of these apps.…”