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Cited by 14Technology Adoption and User BehaviourHealth, Technology, Consumer BehaviorTelemedicine and Telehealth Implementation

The Roles of Trust and Its Antecedent Variables in Healthcare Consumers’ Acceptance of Online Medical Consultation during the COVID-19 Pandemic in China

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Yan, Mian, Zhang, Meijuan, Kwok, Alex Pak Ki, Zeng, Haoyan, Li, Yanfeng·2023·Healthcare
DOI 10.3390/healthcare11091232PMC PMC10177990
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Abstract

Online medical consultation (OMC) is generating considerable interest among researchers and practitioners due to the mandatory quarantine measures implemented during the COVID-19 pandemic in China. However, the acceptance rate of OMC has declined over time. This paper aims to empirically investigate OMC acceptance using a proposed research model by integrating the technology acceptance model (TAM) with trust and its antecedent variables. A quantitative self-administered cross-sectional survey was conducted to collect data from 260 healthcare consumers. A partial least squares structural equation modeling method was used to examine the data. Results revealed that healthcare consumers’ behavioral intention was influenced by attitudes, while perceived usefulness and trust significantly influenced behavioral intention through attitude as a mediator. In addition, perceived risk, perceived privacy protection, network externalities, cognitive reputation, and interactivity directly influenced trust. Overall, the research model explained 50% of the variance in attitude and 71% of the variance in behavioral intention. The study’s findings should provide useful insights into making effective design, development, and implementation decisions for OMC services.

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  • Establishing trust in AI and robotics is essential for societal acceptance.

    “Establishing trust in AI and robotics is essential for societal acceptance.”

    From Ethical implications of AI and robotics in healthcare: A review (2023) · cites this paper as [45]

    Section 2.3 says trust is a core component of healthcare consumers’ acceptance of online medical consultation, and Section 6 reports that trust influenced their intention to use it. The paper does not establish that trust in AI and robotics is essential for societal acceptance; those technologies and that broader population are outside its findings. The indexed passages have no page numbers. (LLM, conf=0.87)

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Cited by

Sorted by citations

Authors
Yan, M., Zhang, M., Kwok, A. P. K., Zeng, H., & Li, Y.
Affiliations
  • Jinan University (CN)
  • Chinese University of Hong Kong (HK)
  • School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China
Corresponding author
Kwok, Alex Pak Ki
ORCID
Venue
Healthcare
Publisher
MDPI AG
Published
2023-04-26
Volume
11
Issue
9
Pages
1232
ISSN
2227-9032 (ISSN-L)
DOI
10.3390/healthcare11091232
PMC
PMC10177990
OpenAlex
W4367182863
Cited by
14
References
6
Citations by year
2026: 6 · 2025: 3 · 2024: 3 · 2023: 2
FWCI
2.78
Type
journal article
Language
en
Open access
Licence
Locations
  • Healthcare (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Science and Technology Projects in Guangzhou (202201010430, 71904062, 72001091, 2020A1515110889)
  • National Natural Science Foundation of China (202201010430, 71904062, 72001091, 2020A1515110889)
  • Guangdong Basic and Applied Basic Research Foundation (202201010430, 71904062, 72001091, 2020A1515110889)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
Keywords
online medical consultation, trust, technology acceptance model, COVID-19 pandemic, health care consumers, behavioral intention, perceived usefulness, risk perception, privacy preservation, network effects, interactivity, attitudes toward technology, China

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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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