The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Mobile computing acceptance factors in the healthcare industry: A structural equation model
Abstract
Objective This paper presents a revised technology acceptance model to examine what determines mobile healthcare systems (MHS) acceptance by healthcare professionals.Method Conformation factor analysis was performed to test the reliability and validity of the measurement model. The structural equation modeling technique was used to evaluate the causal model.Results The results indicated that compatibility, perceived usefulness and perceived ease of use significantly affected healthcare professional behavioral intent. MHS self-efficacy had strong indirect impact on healthcare professional behavioral intent through the mediators of perceived usefulness and perceived ease of use. Yet, the hypotheses for technical support and training effects on the perceived usefulness and perceived ease of use were not supported.Conclusion This paper provides initial insights into factors that are likely to be significant antecedents of planning and implementing mobile healthcare to enhance professionals' MHS acceptance. The proposed model variables explained 70% of the variance in behavioral intention to use MHS; further study is needed to explore extra significant antecedents of new IT/IS acceptance for mobile healthcare. Such as privacy and security issue, system and information quality, limitations of mobile devices; the above may be other interesting factors for implementing mobile healthcare and could be conducted by qualitative research.
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“There was variation in the health IT that served as the focus of the study, including telemedicine technology [32], picture archiving and communications systems [108, 109], and computerized provider order entry (CPOE) [101] (see Table 2 for full list).”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [109]
In the citing paper
“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 [109]
In the citing paper
“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 [109]
In the citing paper
“Perhaps so, but in some studies, the effect of PEOU is as strong as that of PU [103], if not much stronger [109].”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [109]
In the citing paper
“For example, the common addition of variables related to compatibility between the health IT and clinical work processes in the reviewed studies [48, 49, 98, 103, 105, 106, 109] is in line with growing interest in the concept of “fit” as a critical need for successful IT design [131, 132], especially in health care [21, 28, 57, 62, 72, 100, 133-135].”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [109]
In the citing paper
“Indeed, the two studies with relatively strong PEOU effects both had young, mostly non-physician samples in Taiwanese medical centers who had hands-on experience with the IT [103, 109].”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [109]
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Metadata
- Authors
- Wu, J.-H., Wang, S.-C., & Lin, L.-M.
- Affiliations
- National Sun Yat-sen University (TW)
- Meiho University (TW)
- Corresponding author
- Wu, Jen-Her
- ORCID
- Wang, Shu-Ching
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2007-01
- Volume
- 76
- Issue
- 1
- Pages
- 66-77
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W2041318031
- Cited by
- 603
- References
- 65
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 17 · 2023: 25 · 2022: 29 · 2021: 35 · 2020: 34 · 2019: 37 · 2018: 40 · 2017: 36 · 2016: 38 · 2015: 49 · 2014: 59 · 2013: 41 · 2012: 33
- FWCI
- 35.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Education
- National Science Council (NSC 93-2416-H-110-015)
- National Sun Yat-sen University
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- technology acceptance model, healthcare workers, behavioral intention, perceived usefulness, perceived ease of use, compatibility, self-efficacy, technical support, training
- MeSH terms
- Attitude of Health Personnel, Attitude to Computers, Diffusion of Innovation, Taiwan, Models, Organizational, Telemedicine, Point-of-Care Systems, Health Care Sector, Computers, Handheld
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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