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

Abstract

Covid-19 contact-tracing applications (CTAs) offer enormous potential to mitigate the surge of positive coronavirus cases, thus helping stakeholders to monitor high-risk areas. This study hypothesized an integrated model which comprises the technology acceptance model (TAM), privacy calculus theory (PCT), and task-technology fit (TFF) model. The hypothesized is use to better understand behavioral intention towards using the Tawakkalna mobile CTA. This study performed structural equation modeling (SEM) analysis as well as artificial neural network (ANN) analysis to validate the model, using survey data from 309 users of CTAs in the Kingdom of Saudi Arabia (KSA). The findings revealed that perceived ease of use and usefulness has positively and significantly impacted behavioral intention of Tawakkalna mobile CTA. Similarly, task features and mobility positively and significantly influence task–technology fit, and significantly affecting the behavioral intention of the CTA. However, the privacy risk, social concerns, and perceived benefits of social interaction are not significant factors. The findings provide adequate knowledge of the relative impact of key predictors of the behavioral intention of the Tawakkalna contact-tracing app.