Evidence map›Paper›PMID 40343062›Full record

ArticleDigital health

Predicting digital contact tracing tool adoption during COVID-19 from the perspective of TAM: The role of trust, fear, privacy, anxiety, and social media.

Adi Alsyouf, Ala'a M Al-Momani, Nizar Alsubahi, Abdalwali Lutfi, Khalid Anwer Al-Mugheed, Mohammed Amin Almaiah, Rami J Anshasi, Main Naser Alolayyan, Abdallah Alsaad, Mahmaod Alrawad

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Adi AlsyoufDepartment of Managing Health Services & Hospitals, College of Business, Faculty of Business Rabigh, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0002-4672-8991
Ala'a M Al-MomaniDepartment of Management Information Systems, Faculty of Business, Amman Arab University, Amman, Jordan.ORCID https://orcid.org/0000-0003-4642-1129
Nizar AlsubahiDepartment of Health Service and Hospital Administration, Faculty of Economics and Administration, King Abdul Aziz University, Jeddah, Saudi Arabia.ORCID https://orcid.org/0000-0001-9978-1721
Abdalwali LutfiCollege of Business Administration, University of Kalba, Kalba, United Arab Emirates.ORCID https://orcid.org/0000-0002-3928-7497
Khalid Anwer Al-MugheedCollege of Nursing, Riyadh Elm University, Riyadh, Saudi Arabia.ORCID https://orcid.org/0000-0002-8437-977X
Mohammed Amin AlmaiahDepartment of Computer Science, King Abdullah the II IT School, The University of Jordan, Amman, Jordan.ORCID https://orcid.org/0009-0008-9785-485X
Rami J AnshasiProsthodontics Department, Faculty of Dentistry, Jordan University of Science and Technology, Irbid, Jordan.ORCID https://orcid.org/0000-0001-5122-2875
Main Naser AlolayyanHealth Management and Policy Department, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.ORCID https://orcid.org/0000-0001-8020-6153
Abdallah AlsaadDepartment of Management Information System, College of Business, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia.ORCID https://orcid.org/0000-0003-1286-6170
Mahmaod AlrawadCollege of Business Administration and Economics, Al-Hussein Bin Talal University, Ma'an, Jordan.ORCID https://orcid.org/0000-0002-8871-3392

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The emergence of more contagious SARS-CoV-2 variants, such as EG.5 (Eris), has heightened the urgency of assessing associated risks and managing the spread of infections. Digital Contact Tracing (DCT) tools have been widely adopted to mitigate these risks, although the factors driving their acceptance are complex and multifaceted. However, there is a significant lack of research on the application of DCT within Saudi Arabia, despite its proactive use of such technologies in public health strategies. This study investigates the key determinants of DCT adoption and acceptance by integrating the Technology Acceptance Model (TAM) with psychological, social, and regulatory factors related to the context of the study. Methods: Using a quantitative, cross-sectional design, data were collected from Saudi participants through an online survey and analysed using Structural Equation Modeling (SEM) with SmartPLS4. Results: The results supported all the hypotheses except for the relationship between social media awareness and DCT tool usage. The findings revealed that COVID-19-induced anxiety significantly influenced technology acceptance, with social influence playing a mediating role. This study introduces a novel, context-specific model contributing to the technology acceptance field by exploring how pandemic-related factors, such as anxiety and social influence, affect DCT tool adoption. It also addresses a critical gap in the previous literature by examining the mediating role of social impact in the association between privacy and event-related fear and the moderating effect of COVID-19 anxiety on social media awareness and DCT usage. The findings offer valuable insights for governmental interventions, health institutions, and legislators in managing pandemics globally and within the Kingdom of Saudi Arabia. Conclusion: We introduce a novel, context-specific model for understanding how pandemic-related psychological and social factors influence DCT adoption in this study. Those results provide insight into how policymakers, health institutions, and legislators can use DCT tools to manage pandemics globally and in Saudi Arabia.

Indexed as

anxietycontact tracingCOVID-19feargovernment surveillance technologyhealth surveillance technologymobile applicationsPrivacySaudi Arabiasocial media

Identifiers

PMID40343062
PMCPMC12059423

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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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.