Department of Information Technology, College of Computer, Qassim University, Buraidah, Saudi Arabia.
Department of Mathematical Sciences, Computer Science Unit, Taraba State University, Jalingo, Nigeria.
Front Public Health. 2022 May 24;10:847184. doi: 10.3389/fpubh.2022.847184. eCollection 2022.
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. The Kingdom of Saudi Arabia (KSA) is among the countries that have developed a CTA known as the Tawakkalna application, to manage the spread of COVID-19. Thus, this study aimed to examine and predict the factors affecting the adoption of Tawakkalna CTA. An integrated model which comprises the technology acceptance model (TAM), privacy calculus theory (PCT), and task-technology fit (TTF) model was hypothesized. The model is used to understand better behavioral intention toward 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. The findings revealed that perceived ease of use and usefulness has positively and significantly impacted the behavioral intention of Tawakkalna mobile CTA. Similarly, task features and mobility positively and significantly influence task-technology fit, and significantly affect 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.
COVID-19 接触者追踪应用程序 (CTA) 具有极大的潜力来减轻冠状病毒阳性病例的激增，从而帮助利益相关者监测高风险地区。沙特阿拉伯王国 (KSA) 是开发 CTA 的国家之一，称为 Tawakkalna 应用程序，以管理 COVID-19 的传播。因此，本研究旨在研究和预测影响 Tawakkalna CTA 采用的因素。假设了一个集成模型，该模型包含技术接受模型 (TAM)、隐私计算理论 (PCT) 和任务技术适配模型 (TTF)。该模型用于更好地理解对使用 Tawakkalna 移动 CTA 的行为意图。本研究使用来自沙特阿拉伯王国 309 名 CTA 用户的调查数据，进行了结构方程建模 (SEM) 分析和人工神经网络 (ANN) 分析来验证该模型。研究结果表明，感知易用性和有用性对 Tawakkalna 移动 CTA 的行为意图有积极的显著影响。同样，任务特征和移动性对任务技术适配有积极的显著影响，并且对 CTA 的行为意图有显著影响。然而，隐私风险、社会关注和社交互动的感知收益不是重要因素。研究结果提供了关于 Tawakkalna 接触者追踪应用程序行为意图的关键预测因素的相对影响的充分知识。
