Exposure Detection Applications Acceptance: The Case of COVID-19

Area of Science:

• Public Health
• Technology Acceptance
• Behavioral Science

Background:

• The COVID-19 pandemic created high fear and influenced decision-making.
• Understanding technology acceptance is crucial for public health interventions.

Purpose of the Study:

• To propose and test a model for accepting exposure detection apps during the COVID-19 pandemic.
• To investigate the role of COVID-19 anxiety and social influence on app acceptance.

Main Methods:

• Quantitative study with a cross-sectional design.
• Structural Equation Modeling-Partial Least Squares (SEM-PLS) analysis.
• 586 participants from Saudi Arabia were surveyed.

Main Results:

• The proposed model was largely supported, except for the link between social media awareness and app intention.
• COVID-19 anxiety and social influence were confirmed as significant mediators.
• Trust in government and COVID-19 anxiety influenced exposure detection app implementation.

Conclusions:

• A context-driven model for technology acceptance during pandemics was developed.
• Findings highlight the mediating roles of anxiety and social influence in app adoption.
• Results can inform strategies for pandemic containment using technology.