Acceptance of Technological Innovations in Emergency Departments
Open accessAbstract
(1) Background: Although technology constantly evolves and revolutionising many industries in this digital age, the healthcare industry is comparatively conservative and has been slow to adopt new technologies due to concerns about patient safety. Notwithstanding the abundance of research on technology acceptance, the majority of them fail to take into account departmental variations, making it impossible to enhance technology adoption in the medical sector. (2) Methods: This study examined the factors influencing Malaysian emergency department healthcare professionals' acceptance of new medical technology by combining two external variables, which are organisational support and training, with the Technology Acceptance Model (TAM). The target population of this study consisted of emergency department healthcare professionals in Malaysian hospitals who are 25 to 60 years old and above. A total of 140 valid questionnaires were gathered after the survey was sent by email and WhatsApp to hospital emergency departments around the country. Data collected were analysed using SPSS and SmartPLS. (3) Results: Perceived usefulness and training have a significant impact on attitude toward use, whereas attitude toward use is the sole variable that directly influences behavioural intention to use and acts as a mediator in certain paths. (4) Conclusion: To encourage the successful adoption and use of technology, hospital administration should focus on the real needs of medical personnel, enhance their knowledge of it, and provide focused training.
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The Technology Acceptance Model holds significant value for technology-assisted undergraduate health treatment, as it provides a structured psychological framework to evaluate how digital interventions transition from clinical tools to accepted patient practices.
“The Technology Acceptance Model holds significant value for technology-assisted undergraduate health treatment, as it provides a structured psychological framework to evaluate how digital interventions transition from clinical tools to accepted patient practices [50,51].”
From Advancing Undergraduate Student Mental Healthcare of Social Anxiety Disorder: Evaluating the Acceptance of AR-Assisted Cognitive Behavioral Therapy Through TAM-Based Constructs. (2026) · cites this paper as [51]
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Critical Values For The Chi-Square Distribution
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Perceived organizational support, work engagement, and employee well-Being
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Review: Challenges and Obstacles of Trusted Elements for Mobile Health Records Management
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Flagged first, then by citations
Research works printed but not linked: 5
- H. Hussein, M. Jawad, and Z. Hassan, "Behavior Intention of Chronic Illness Patients in Malaysia to Use IoT-based Healthcare Services," 2023. [Online]. Available: https://www.researchgate.net/publication/372372180
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- H. S. W. Nugroho, H. B. Notobroto, and L. Rosyanti, "Acceptance model of a mandatory health information system in Indonesia," Healthc. Inform. Res., vol. 27, no. 2, pp. 127-136, 2021, doi: 10.4258/HIR.2021.27.2.127. Preprints.org (www.preprints.org) | NOT PEER-REVIEWED | Posted: 25 March 2026 doi:10.20944/preprints202603.1940.v1 · DOI
- R. Singh, S. Agarwal, and B. Pandiya, "Customer experience in diagnostic centres: an empirical study," ResearchGate, 2022, [Online]. Available: https://www.researchgate.net/publication/360484190
- Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. Preprints.org (www.preprints.org) | NOT PEER-REVIEWED | Posted: 25 March 2026 doi:10.20944/preprints202603.1940. · DOI
Cited by
Sorted by citations
Zhou, Z., Zhou, Y., Ouyang, B., Man, S. S., & Chan, A. H. S. (2026) · Healthcare · doi:10.3390/healthcare14131978
Morosan, P. A., Popa, T. O., Nedelea, P. L., Bobu, A. M., Cucu, A. I., Bouros, C., Popa, V., Hăisan, A., Grigorasi, G. R., Corlade-Andrei, M., et al. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15166420
- Authors
- Lee, A. T., Ramasamy, R. K., & Subbarao, A.
- Affiliations
- Multimedia University (MY)
- Venue
- Healthcare
- Publisher
- MDPI AG
- Published
- 2026-03-25
- Volume
- 14
- Issue
- 10
- Pages
- 1273-1273
- ISSN
- 2227-9032 (ISSN-L)
- OpenAlex
- W7160873453
- Cited by
- 2
- References
- 89
- Citations by year
- 2026: 2
- FWCI
- 14.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Healthcare (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University) (acceptedVersion)
- Funders
- Multimedia University
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- technology acceptance, emergency department, healthcare workers, technology adoption, technology acceptance model, organizational support, training, perceived usefulness, attitude toward using, behavioral intention, Malaysian hospitals
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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