Exposure Detection Applications Acceptance: The Case of COVID-19

Adi Alsyouf; Abdalwali Lutfi; Mohammad Al-Bsheish; Mu'taman Jarrar; Khalid Al-Mugheed; Mohammed Amin Almaiah; Fahad Nasser Alhazmi; Ra'ed Masa'deh; Rami J Anshasi; Abdallah Ashour

doi:10.3390/ijerph19127307

Exposure Detection Applications Acceptance: The Case of COVID-19

Int J Environ Res Public Health. 2022 Jun 14;19(12):7307. doi: 10.3390/ijerph19127307.

Authors

Affiliations

¹ Department of Managing Health Services and Hospitals, Faculty of Business Rabigh, College of Business (COB), King Abdulaziz University, Jeddah 21991, Saudi Arabia.
² Department of Accounting, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
³ Health Management Department, Batterjee Medical College, Jeddah 21442, Saudi Arabia.
⁴ Medical Education Department, King Fahd Hospital of the University, Al-Khobar 34445, Saudi Arabia.
⁵ Vice Deanship for Quality and Development, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia.
⁶ Surgical Nursing Department, Faculty of Nursing, Near East University, Nicosia 99138, Cyprus.
⁷ Department of Computer Networks, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
⁸ Department of Health Services and Hospital Administration, Faculty of Economics and Administration, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
⁹ Department of Management Information Systems, School of Business, University of Jordan, Amman 11942, Jordan.
¹⁰ Prosthodontics Department, Faculty of Dentistry, Jordan University of Science and Technology, Irbid 22110, Jordan.
¹¹ Department of Nursing, Faculty of Nursing, Philadelphia University, Amman 19392, Jordan.

Abstract

The pandemic's context is rife with numerous dangerous threats and high fear levels, influencing human decision-making. Such characteristics are identified by investigating the acceptance of exposure detection apps from the technology acceptance model (TAM) perspective. This study purposed a model to investigate protection technology acceptance, specifically exposure detection apps in the context of COVID-19. Quantitative study approach and a cross-section design targeted 586 participants from Saudi Arabia. As the study model is complex, the study hypotheses were analysed using the structural equation modelling-partial least squares (SEM-PLS3) approach. The findings support the entire model hypothesis except the link between social media awareness and exposure detection apps' intention. Mediation of COVID-19 anxiety and influence was confirmed as well. The current paper contributes to the technologies acceptance domain by developing a context-driven model comprising the major pandemic characteristics that lead to various patterns of technology acceptance. This study also fills the literature gap regarding mediating effects of social influence and COVID-19 anxiety in the relationship between trust in government and exposure detection apps implementation, and between COVID-19 anxiety and exposure detection apps implementation, respectively. The results may assist government agencies, health policymakers, and health organisations in the wide world and specifically Saudi Arabia, in their attempts to contain the COVID-19 pandemic spread.

Keywords: COVID-19; exposure detection apps; mHealth; technology acceptance model; tracing apps.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19* / epidemiology
Humans
Intention
Mobile Applications*
Pandemics
Saudi Arabia / epidemiology
Social Media*

Grants and funding

This research was funded through the annual funding track by the Deanship of Scientific Research, vice presidency for graduate studies and scientific research, King Faisal University, Saudi Arabia [GRANT788].