Exposure Detection Applications Acceptance: The Case of COVID-19

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.

Evaluating the D&M IS Success Model in the Context of Accounting Information System and Sustainable Decision Making

“…Still another IS success model is user satisfaction and it is comprised of repeat visit and repeat purchase indicators. The former refers to the difference between the required information and the received information [64,65]. Generally speaking, information satisfaction stems from comparing between the information system's needs and the received performance of the system.…”

The Role of Personality and Top Management Support in Continuance Intention to Use Electronic Health Record Systems among Nurses

“…The model posits that end-user attitudes function as a determinant of their behavioral intention to use and final actual use, depending on the perceived utility (PU) and perceived ease of use (PEOU) of the IT system. The initial TAM model has been extended many times (e.g., [ 18 , 20 , 36 , 37 , 38 , 39 , 40 ]). For instance, TAM2, an extended TAM model, was tested in a longitudinal study testing four systems and examining two of each voluntary use and mandatory use.…”

“…Researchers have used various models and theories in economics and social science to examine the acceptance of information technology (IT) in the healthcare sector. They can be grouped according to the technology deployment types, such as pre- and post-adoption of technologies [ 19 , 20 ], and the healthcare professionals they target like physicians [ 21 ], nurses [ 22 ], secretaries [ 23 ], and the different evaluation dimensions and attributes they are built on.…”

Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust

“…Social influence can also aid the understanding of uncertainty reduction, as it might function as a substitute for interaction with the unknown and not-yet-available technology [ 58 ]. In other words, social influence is an active information-seeking method [ 59 ]. In this study, healthcare workers’ expectancy (performance expectancy and effort expectancy) of AI-assisted diagnosis and treatment are influenced by other healthcare workers’ attitudes, in turn influencing other healthcare workers’ attitudes toward AI-assisted diagnosis and treatment.…”