Obstacles to the adoption of radio frequency identification technology in the emergency rooms of hospitals
Abstract
The Emergency Room (ER) receives patients in critical conditions. The operation of many emergency service chains is hampered because the required medical equipment is not always conveniently available and patient vital signs are manually monitored, but not automatically tracked on a real-time basis. This has resulted in medical errors, increased stress levels of medical teams, and poor utilisation of staff and equipment. Our research investigates factors that contribute to the adoption of Radio Frequency Identification (RFID) technologies by the medical team in ERs. We propose a theoretical framework to address this issue based upon the Unified Theory of Acceptance and Use of Technology (UTAUT) theory.
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In one reviewed study, the relationship between behavioral intentions and actual use was significant.
“In one study, the relationship was significant [110].”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [110]
Only two reviewed studies tested the relationship between behavioral intentions and actual use.
“Only two studies tested the relationship between behavioral intentions and actual use [102,110].”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [110]
One reviewed study tested TAM2, two tested UTAUT, and two tested a hybrid of UTAUT, TAM, and TPB.
“One study tested TAM2 [97], two tested UTAUT [108, 110], and two tested some hybrid of UTAUT, TAM and TPB [99, 105].”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [110]
Five reviewed studies collected data from a mix of physicians, nurses, pharmacists, and medical technicians.
“Five studies collected data from a mix of physicians, nurses, pharmacists, and medical technicians [106-110].”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [110]
Among the reviewed studies, voluntary IT use was more common in studies with non-significant SN effects, while mandatory IT use was more common in studies with significant SN effects.
“In opposition to that hypothesis, in three of four studies with non-significant SN effects, IT use was voluntary [49,97] or was perceived to be voluntary [108], and IT use was mandatory in two of four studies with significant SN effects [102,110].”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [110]
Reviewed studies were set in Taiwan, Hong Kong, the UK, Australia, the mainland US, Hawaii, Belgium, Finland, and Quebec.
“Several studies were set in Taiwan [103, 109, 110], Hong Kong [32, 48, 49], the UK [96, 104], Australia [102, 111], and the mainland US [98, 99, 106, 107] and Hawaii [97]. Others were set in Belgium [108], Finland [100], and Quebec [101].”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [110]
Some reviewed studies aggregated data across professions, making comparisons possible but not carried out.
“Although individual or profession-specific differences may not have been possible to study in homogeneous samples, such as those comprised of only young physicians [99, 109], all-females [103], or all-males [96], there were studies in which comparisons were possible but not carried out, such as the study aggregating data from physicians, nurses, and information systems department staff [110], one”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [110]
Actual use has been too infrequently measured in the reviewed studies, precluding tests of several important relationships, such as the one between intention and actual use.
“First, certain variables such as actual use have been too infrequently measured, precluding tests of several important relationships, such as the one between intention and actual use [102, 110].”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [110]
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035 · see citing works
Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence
Zeithaml, V. A. (1988) · Journal of Marketing · doi:10.1177/002224298805200302 · Cited by 18,292 · see citing works
Information Systems Success: The Quest for the Dependent Variable
DeLone, W. H., & McLean, E. R. (1992) · Information Systems Research · doi:10.1287/isre.3.1.60 · Cited by 10,697 · see citing works
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150 · see citing works
Computer Self-Efficacy: Development of a Measure and Initial Test
Compeau, D. R., & Higgins, C. A. (1995) · MIS Quarterly · doi:10.2307/249688 · Cited by 6,553 · see citing works
Venkatesh, V., & Morris, M. G. (2000) · MIS Quarterly · doi:10.2307/3250981 · Cited by 4,382 · see citing works
Personal Computing: Toward a Conceptual Model of Utilization1
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991) · MIS Quarterly · doi:10.2307/249443 · Cited by 3,814 · see citing works
Agarwal, R., & Prasad, J. (1997) · Decision Sciences · doi:10.1111/j.1540-5915.1997.tb01322.x · Cited by 1,669 · see citing works
The Role of Espoused National Cultural Values in Technology Acceptance1
Srite, M., & Karahanna, E. (2006) · MIS Quarterly · doi:10.2307/25148745 · Cited by 1,169 · see citing works
STRUCTURAL EQUATION MODELS WITH UNOBSERVABLE VARIABLES AND MEASUREMENT ERROR
Fornll, C., & Larcker, D. F. (1981) · Cited by 1,165 · see citing works
The Determinants of Goal Commitment
Locke, E. A., Latham, G. P., & Erez, M. (1988) · Academy of Management Review · doi:10.5465/amr.1988.4306771 · Cited by 844 · see citing works
the Effect of Brand and Price Information on Subjective Product Evaluations
Dodds, W. B., & Monroe, K. B. (1985) · ACR North American Advances · Cited by 596 · see citing works
Gender Differences in Managerial Careers: Yesterday, Today, and Tomorrow
Kirchmeyer, C. (2002) · Journal of Business Ethics · doi:10.1023/a:1014721900246 · Cited by 213 · see citing works
Radio Frequency Identification Applications in Hospital Environments
Wicks, A. M., Visich, J. K., & Li, S. (2006) · Hospital Topics · doi:10.3200/htps.84.3.3-9 · Cited by 166 · see citing works
Exploring m-commerce in terms of viability, growth and challenges
Smith, A. D. (2006) · International Journal of Mobile Communications · doi:10.1504/ijmc.2006.010362 · Cited by 52 · see citing works
DeShon, R. P., & Landis, R. S. (1997) · Organizational Behavior and Human Decision Processes · doi:10.1006/obhd.1997.2699 · Cited by 46 · see citing works
Evaluating the technological characteristics and trust affecting mobile device usage
Mahatanankoon, P., Wen, H. J., & Lim, B. B. L. (2006) · International Journal of Mobile Communications · doi:10.1504/ijmc.2006.010361 · Cited by 42 · see citing works
The influence of user interface usability on rural consumers' trust of e-health services
Fruhling, A. L., & Lee, S. M. (2006) · International Journal of Electronic Healthcare · doi:10.1504/ijeh.2006.010424 · Cited by 31 · see citing works
Lubitz, D. V., & Wickramasinghe, N. S. (2006) · International Journal of Electronic Healthcare · doi:10.1504/ijeh.2006.010429 · Cited by 30 · see citing works
Development of standardised e-business solutions via e-chain analysis in the digital utility
Wei, J. C., Platt, R., White, B., & Jasquith, A. P. (2006) · International Journal of Services and Standards · doi:10.1504/ijss.2006.008728 · Cited by 18 · see citing works
The influence of age and education on perceptions of occupational attractiveness
Fields, M. W., & Shallenberger, W. R. (1987) · Journal of Occupational Psychology · doi:10.1111/j.2044-8325.1987.tb00266.x · Cited by 9 · see citing works
Design and culture innovations combine at Baldrige winner.
Eagle, A. (2006) · PubMed · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The Technology Acceptance Model: Its past and its future in health care
Holden, R. J., & Karsh, B.-T. (2010) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2009.07.002 · Cited by 2,669 · see citing works
A Systematic Review of Patient Acceptance of Consumer Health Information Technology
Or, C. K. L., & Karsh, B.-T. (2009) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2888 · Cited by 586 · see citing works
Factors affecting the adoption of healthcare information technology.
Phichitchaisopa, N., & Naenna, T. (2013) · EXCLI journal · Cited by 165 · see citing works
Kummer, T., Recker, J., & Bick, M. (2016) · Information & Management · doi:10.1016/j.im.2016.04.002 · Cited by 105 · see citing works
Modeling Citizen Satisfaction with Mandatory Adoption of an E-Government Technology
Chan, F., Thong, J. Y., Venkatesh, V., Brown, S. A., Hu, P. J.-H., & Tam, K. (2010) · Journal of the AIS · doi:10.17705/1jais.00239 · Cited by 429 · see citing works
van der Togt, R., Bakker, P. J. M., & Jaspers, M. (2010) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2010.12.004 · Cited by 66 · see citing works
Bozan, K., Parker, K. R., & Davey, B. (2016) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2016.391 · Cited by 64 · see citing works
Huang, J.-C. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2012.12.011 · Cited by 60 · see citing works
Ifinedo, P. (2015) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.12.012 · Cited by 59 · see citing works
Examining consumer use of mobile health applications by the extended UTAUT model
Semiz, B. B., & Semiz, T. (2021) · Business And Management Studies An International Journal · doi:10.15295/bmij.v9i1.1773 · Cited by 44 · see citing works
A Systematic Review of Patient Tracking Systems for Use in the Pediatric Emergency Department
Dobson, I., Doan, Q. H., & Hung, G. R. (2012) · Journal of Emergency Medicine · doi:10.1016/j.jemermed.2012.02.017 · Cited by 33 · see citing works
Radio frequency identification (RFID) of dentures in long-term care facilities
Madrid, C., Korsvold, T., Rochat, A., & Abarca, M. (2012) · Journal of Prosthetic Dentistry · doi:10.1016/s0022-3913(12)60057-2 · Cited by 32 · see citing works
Stahl, J. E., Holt, J. K., & Gagliano, N. J. (2009) · Journal of Medical Systems · doi:10.1007/s10916-009-9365-7 · Cited by 25 · see citing works
Beyond technology acceptance to effective technology use: a parsimonious and actionable model
Holahan, P. J., Lesselroth, B., Adams, K. A., Wang, K., & Church, V. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocu043 · Cited by 22 · see citing works
A study of RFID application impacts on medical safety
Chang, S.-I., Ou, C. S., Ku, C. Y., & Yang, M. (2008) · International Journal of Electronic Healthcare · doi:10.1504/ijeh.2008.018918 · Cited by 19 · see citing works
Application of RFID Technology for In-House Drug Management System
Parida, M., Yang, H.-C., Jheng, S.-W., & Kuo, C. (2012) · International Conference on Network-Based Information Systems · doi:10.1109/nbis.2012.90 · Cited by 18 · see citing works
Al-Rahmi, A. M., Ramin, A. K., Alamri, M. M., Al-Rahmi, W. M., Yahaya, N., Abualrejal, H. M., & Al-Maatouk, Q. (2019) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.f1099.0986s319 · Cited by 18 · see citing works
Appiah, B., Burdine, J. N., Aftab, A., Asamoah‐Akuoko, L., Anum, D. A., Kretchy, I. A., Samman, E., Appiah, P. B., & Bates, I. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9752 · Cited by 15 · see citing works
Gößwein, E., & Liebherr, M. (2025) · SAGE Open · doi:10.1177/21582440241311126 · Cited by 15 · see citing works
Understanding the Acceptance of RFID in the Healthcare Industry: Extending the TAM Model
Chong, A. Y., & Chan, F. T. S. (2012) · Decision engineering · doi:10.1007/978-1-4471-4033-7_6 · Cited by 13 · see citing works
RFID Privacy Issues in Healthcare: Exploring the Roles of Technologies and Regulations
Parks, R. F., Chu, C., & Xu, H. (2010) · Journal of Information Privacy and Security · doi:10.1080/15536548.2010.10855891 · Cited by 12 · see citing works
Understanding Pharmacists’ Intention to Use Medical Apps
Ng, S.-N., Matanjun, D., D'Souza, U. J. A., & Alfred, R. (2015) · Cited by 10 · see citing works
Use of Radio Frequency Identification to Establish Emergency Medical Service Offload Times
Steer, S., Bhalla, M. C., Zalewski, J., Frey, J. A., Nguyen, V., & Mencl, F. R. (2015) · Prehospital Emergency Care · doi:10.3109/10903127.2015.1076093 · Cited by 8 · see citing works
Radio frequency identification: beyond the myths. A case for health care
Bureau, S. P., Gadh, R., & Prabhu, B. S. (2008) · HAL · Cited by 7 · see citing works
(2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.b1061.0982s919 · Cited by 6 · see citing works
E-health information as a support of health information technology systems – patients attitudes
Soboń, M. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.230 · Cited by 4 · see citing works
Vichitkraivin, P., & Naenna, T. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/649/1/012023 · Cited by 4 · see citing works
Bick, M., Kummer, T., & Ryschka, S. (2014) · Information Systems Management · doi:10.1080/10580530.2015.983021 · Cited by 3 · see citing works
Alkhowaiter, W. A., Dwivedi, Y. K., & Williams, M. D. (2013) · UKAIS · Cited by 3 · see citing works
Clark, D. A., & Pasupathy, K. S. (2014) · IGI Global eBooks · doi:10.4018/978-1-4666-5202-6.ch170 · Cited by 3 · see citing works
Factors affecting the adoption of healthcare information technology
Naenna, T., & Phichitchaisopa, N. (2013) · EXCLI journal · doi:10.17877/de290r-5602 · Cited by 169 · see citing works
Factors Influencing Surgeon Adoption of Technology in the Medical Device Industry
Reynolds, S. (2022) · Digital Archive @ GSU · doi:10.57709/17702268 · Cited by 1 · see citing works
RFID technology in outpatient logistics:an analysis of its potential and acceptance
Onurlu, B. B. (2013) · TU/e Research Portal · Cited by 2 · see citing works
Trends of Factors and Theories in Health Information Systems Acceptance
Sezgın, E., & Özkan, S. (2016) · IGI Global eBooks · doi:10.4018/978-1-4666-9978-6.ch085 · Cited by 4 · see citing works
Zheng, C., Zhang, Y., Luo, T., Li, F., Qin, Z., Xiong, S., Zhang, J., Lei, E., Lu, L., Zhang, L., et al. (2026) · BMC Medical Education · doi:10.1186/s12909-025-08499-4 · Cited by 2 · see citing works
Theera-Ampornpunt, N. (2009) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 1 · see citing works
Siang, S. Y. (2012) · Cited by 1 · see citing works
Zhang, X. (2015) · Research Online (University of Wollongong) · Cited by 1 · see citing works
The Adoption of Mobile Health Systems in Hospitals: An Empirical Cross-City Study in China
Duan, B. (2019) · Open Research Exeter (University of Exeter) · Cited by 1 · see citing works
Winston, T. G. (2015) · NSUWorks (Nova Southeastern University)
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- Authors
- Chen, C. C., Wu, J., & Crandall, R. E.
- Affiliations
- Appalachian State University (US)
- Tamkang University (TW)
- Venue
- International Journal of Electronic Healthcare
- Publisher
- Inderscience Publishers
- Published
- 2007
- Volume
- 3
- Issue
- 2
- Pages
- 193
- ISSN
- 1741-8453 (ISSN-L)
- 1741-8461
- OpenAlex
- W1976252223
- Cited by
- 43
- References
- 23
- Citations by year
- 2026: 1 · 2025: 1 · 2022: 2 · 2021: 1 · 2019: 4 · 2018: 1 · 2017: 1 · 2016: 3 · 2015: 6 · 2014: 2 · 2013: 5 · 2012: 5
- FWCI
- 8.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Electronic Healthcare (publishedVersion)
- PubMed (publishedVersion)
- Tamkang University Institutional Repository (TKUIR) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Media Technology › RFID technology advancements
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- radio frequency identification, RFID adoption, emergency department, technology adoption barriers, UTAUT, vital signs monitoring, medical equipment availability, medical errors, hospital resource utilization
- MeSH terms
- Attitude of Health Personnel, Emergency Service, Hospital, Hospital Communication Systems, Humans, Information Systems, Quality of Health Care, Social Support, Safety Management, Intention
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