ArticleVaccines2023
Acceptance of a Text Messaging Vaccination Reminder and Recall System in Malaysia's Healthcare Sector: Extending the Technology Acceptance Model.
Article in Vaccines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Acceptance of Technological Innovations in Emergency Departments: An Empirical Study Based on an Extended TAM.Healthcare (Basel, Switzerland) · 2026Article
- Understanding Psychosocial Barriers to Healthcare Technology Adoption: A Review of TAM Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology and UTAUT Frameworks.Healthcare (Basel, Switzerland) · 2025Review
- Article
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Authors and funding
4 authors.
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Abstract
Malaysian healthcare institutions still use ineffective paper-based vaccination systems to manage childhood immunization schedules. This may lead to missed appointments, incomplete vaccinations, and outbreaks of preventable diseases among infants. To address this issue, a text messaging vaccination reminder and recall system named Virtual Health Connect (VHC) was studied. VHC simplifies and accelerates immunization administration for nurses, which may result in improving the completion and timeliness of immunizations among infants. Considering the limited research on the acceptance of these systems in the healthcare sector, we examined the factors influencing nurses' attitudes and intentions to use VHC using the extended technology acceptance model (TAM). The novelty of the conceptual model is the incorporation of new predictors of attitude, namely, perceived compatibility and perceived privacy and security issues. We conducted a survey among 121 nurses in Malaysian government hospitals and clinics to test the model. We analyzed the collected data using partial least squares structural equation modeling (PLS-SEM) to examine the significant factors influencing nurses' attitudes and intentions to use VHC. Moreover, we applied an artificial neural network (ANN) to determine the most significant factors of acceptance with higher accuracy. Therefore, we could offer more accurate insights to decision-makers in the healthcare sector for the advancement of health services. Our results highlighted that the compatibility of VHC with the current work setting of nurses developed their positive perspectives on the system. Moreover, the nurses felt optimistic about the system when they considered it useful and easy to use in the workplace. Finally, their attitude toward using VHC played a pivotal role in increasing their intention to use it. Based on the ANN models, we also found that perceived compatibility was the most significant factor influencing nurses' attitudes towards using VHC, followed by perceived ease of use and perceived usefulness.
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