ArticleInternational journal of environmental research and public health2022
Exposure Detection Applications Acceptance: The Case of COVID-19.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.
- Improving Acceptability of mHealth Apps-The Use of the Technology Acceptance Model to Assess the Acceptability of mHealth Apps: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Determinants of Behavioral Intention to Use Digital Healthcare Services: A Cross-Sectional PLS-SEM Study on the Shukhee App in Bangladesh.Health science reports · 2026Article
- Preventing Ventilator-Associated Pneumonia: A Multicenter Cross-Sectional Study of Knowledge, Attitudes, and Practices of Medical Staff in Chinese Hospitals.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Factors Determining Acceptance of Internet of Things in Medical Education: Mixed Methods Study.JMIR human factors · 2025Article
- Knowledge, attitude, and practice of nurses regarding enteral feeding: a systematic review.BMC nursing · 2025Article
- Use of Digital COVID-19 Exposure Notifications at a Large Gathering: Survey Analysis of Public Health Conference Attendees.JMIR formative research · 2024Article
- Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2024Article
- Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology.Scientific reports · 2024Article
- Effect of compassion fatigue on emotional labor in female nurses: Moderating effect of self-compassion.PloS one · 2024Article
- Nurses' continuance intention to use electronic health record systems: The antecedent role of personality and organisation support.PloS one · 2024Article
- Modeling Consumer Acceptance and Usage Behaviors of m-Health: An Integrated Model of Self-Determination Theory, Task-Technology Fit, and the Technology Acceptance Model.Healthcare (Basel, Switzerland) · 2023Article
- The influence of accounting information system adoption on business performance amid COVID-19.Computers in human behavior reports · 2023Article
- The Roles of Trust and Its Antecedent Variables in Healthcare Consumers' Acceptance of Online Medical Consultation during the COVID-19 Pandemic in China.Healthcare (Basel, Switzerland) · 2023Article
- Empowering Self-Efficacy by Using Patient Empowerment among Chronic Obstructive Pulmonary Disease: Pre-Post-Test Study.Healthcare (Basel, Switzerland) · 2023Article
- The Use of a Technology Acceptance Model (TAM) to Predict Patients' Usage of a Personal Health Record System: The Role of Security, Privacy, and Usability.International journal of environmental research and public health · 2023Article
- Promoting Healthcare Workers' Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human-Computer Trust.International journal of environmental research and public health · 2022Article
- Knowledge, Practice, Compliance, and Barriers toward Ventilator-Associated Pneumonia among Critical Care Nurses in Eastern Mediterranean Region: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- The Role of Personality and Top Management Support in Continuance Intention to Use Electronic Health Record Systems among Nurses.International journal of environmental research and public health · 2022Article
- All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing.International journal of environmental research and public health · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 9 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.