Cited by 235Technology Adoption and User BehaviourPatient Safety and Medication ErrorsElectronic Health Records Systems

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Testing the technology acceptance model for evaluating healthcare professionals’ intention to use an adverse event reporting system

Wu, J.-H., Shen, W.-S., Lin, L.-M., Greenes, R. A., Bates, D. W.·2007·International Journal for Quality in Health Care

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Abstract

BACKGROUND: Many healthcare organizations have implemented adverse event reporting systems in the hope of learning from experience to prevent adverse events and medical errors. However, a number of these applications have failed or not been implemented as predicted. OBJECTIVE: This study presents an extended technology acceptance model that integrates variables connoting trust and management support into the model to investigate what determines acceptance of adverse event reporting systems by healthcare professionals. METHOD: The proposed model was empirically tested using data collected from a survey in the hospital environment. A confirmatory factor analysis was performed to examine the reliability and validity of the measurement model, and a structural equation modeling technique was used to evaluate the causal model. RESULTS: The results indicated that perceived usefulness, perceived ease of use, subjective norm, and trust had a significant effect on a professional's intention to use an adverse event reporting system. Among them, subjective norm had the most contribution (total effect). Perceived ease of use and subjective norm also had a direct effect on perceived usefulness and trust, respectively. Management support had a direct effect on perceived usefulness, perceived ease of use, and subjective norm. CONCLUSION: The proposed model provides a means to understand what factors determine the behavioral intention of healthcare professionals to use an adverse event reporting system and how this may affect future use. In addition, understanding the factors contributing to behavioral intent may potentially be used in advance of system development to predict reporting systems acceptance.

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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
Multivariate Data Analysis.
Herne, H., Cooley, W. W., & Lohnes, P. R. (1973) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344428 · Cited by 35,801
On the Evaluation of Structural Equation Models
Bagozzi, R. P., & Yi, Y. (1988) · Journal of the Academy of Marketing Science · doi:10.1007/bf02723327 · Cited by 26,163
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066
To Err Is Human
Institute of Medicine (2000) · National Academies Press eBooks · doi:10.17226/9728 · Cited by 14,390
Trust and TAM in Online Shopping: An Integrated Model1
Gefen, D., Karahanna, E., & Straub, D. W. (2003) · MIS Quarterly · doi:10.2307/30036519 · Cited by 8,635
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · cited 1×
“Reporting will occur only if practitioners feel professionally comfortable and legally safe in reporting and view it as a culturally accepted activity within the healthcare community [13–16].”
Background
Book Review
Wall, A. (2000) · British Journal of Healthcare Management · doi:10.12968/bjhc.2000.6.9.19311 · Cited by 5,912
Examining the technology acceptance model using physician acceptance of telemedicine technology
Hu, P. J., Chau, P. Y. K., Sheng, O. R. L., & Tam, K. Y. (1999) · Journal of Management Information Systems · doi:10.1080/07421222.1999.11518247 · Cited by 1,951
Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model1
Igbaria, M., Zinatelli, N., Cragg, P. B., & Cavaye, A. (1997) · MIS Quarterly · doi:10.2307/249498 · Cited by 1,711

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Authors: Wu, J.-H., Shen, W.-S., Lin, L.-M., Greenes, R. A., & Bates, D. W.
Affiliations: National Sun Yat-sen University (TW)
Meiho University (TW)
Arizona State University (US)
Brigham and Women's Hospital (US)
Corresponding author: Greenes, R. A.
ORCID: Lin, L.-M.
Greenes, R. A.
Bates, D. W.
Venue: International Journal for Quality in Health Care
Publisher: Oxford University Press (OUP)
Published: 2007-12-11
Volume: 20
Issue: 2
Pages: 123-129
ISSN: 1353-4505 (ISSN-L)
1464-3677
DOI: 10.1093/intqhc/mzm074
OpenAlex: W2128504617
Cited by: 235
References: 23
Citations by year: 2026: 5 · 2025: 1 · 2024: 16 · 2023: 9 · 2022: 21 · 2021: 17 · 2020: 12 · 2019: 11 · 2018: 19 · 2017: 9 · 2016: 18 · 2015: 18 · 2014: 16 · 2013: 18 · 2012: 13
FWCI: 15.11
Type: journal article
Language: en
Open access: closed
Locations: International Journal for Quality in Health Care (publishedVersion)
PubMed (publishedVersion)
Topics: Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Patient Safety and Medication Errors
Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords: technology acceptance model, adverse event reporting system, healthcare workers, behavioral intention, trust, management support, perceived usefulness, perceived ease of use, subjective norms, medical errors, hospital safety
MeSH terms: Attitude of Health Personnel, Confidentiality, Female, Humans, Male, Models, Psychological, Taiwan, Organizational Culture, Adverse Drug Reaction Reporting Systems, Intention, Trust

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Testing the technology acceptance model for evaluating healthcare professionals’ intention to use an adverse event reporting system

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