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Examining the Prediction of COVID-19 Contact-Tracing App Adoption Using an Integrated Model and Hybrid Approach
Ali Alkhalifah1, Umar Ali Bukar2
1Department of Information Technology, College of Computer, Qassim University, Buraidah, Saudi Arabia.
The Tawakkalna contact-tracing app adoption in Saudi Arabia was driven by ease of use and usefulness. Privacy risks and social concerns did not significantly impact user adoption of this COVID-19 tool.
Area of Science:
- Health Informatics
- Information Systems
- Public Health
Background:
- COVID-19 contact-tracing applications (CTAs) are vital for managing outbreaks.
- The Tawakkalna application is Saudi Arabia's primary CTA for COVID-19 control.
- Understanding user adoption factors is crucial for CTA effectiveness.
Purpose of the Study:
- To examine and predict factors influencing the adoption of the Tawakkalna CTA.
- To evaluate an integrated model combining TAM, PCT, and TTF for CTA adoption.
- To analyze behavioral intention towards using the Tawakkalna mobile CTA.
Main Methods:
- Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) analyses were employed.
- Survey data from 309 CTA users in Saudi Arabia were collected and analyzed.
- An integrated theoretical model was hypothesized and validated.
Main Results:
- Perceived ease of use and usefulness significantly and positively impacted behavioral intention.
- Task features and mobility positively influenced task-technology fit and behavioral intention.
- Privacy risk, social concerns, and perceived social interaction benefits were not significant predictors.
Conclusions:
- Ease of use and usefulness are key drivers for Tawakkalna app adoption.
- Task-technology fit significantly contributes to the behavioral intention of using CTAs.
- Future strategies should focus on enhancing usability and task relevance for CTAs.
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