Evidence map›Paper›PMID 37174774›Full record

ArticleHealthcare (Basel, Switzerland)2023

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

Mian Yan, Meijuan Zhang, Alex Pak Ki Kwok, Haoyan Zeng, Yanfeng Li

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mian YanSchool of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China.ORCID 0000-0002-1305-4257
Meijuan ZhangSchool of Management, Jinan University, Guangzhou 510000, China.
Alex Pak Ki KwokData Science and Policy Studies Programme, Faculty of Social Science, The Chinese University of Hong Kong, Hong Kong 999077, China.
Haoyan ZengSchool of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China.
Yanfeng LiSchool of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China.

Funding

Funding by Science and Technology Projects in Guangzhou 202201010430Guangdong Basic and Applied Basic Research Foundation 2020A1515110889National Natural Science Foundation of China 71904062National Natural Science Foundation of China 72001091
6 · The paper itself

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.

Indexed as

online medical consultationpublic healthtechnology acceptancetrust

Identifiers

PMID37174774
PMCPMC10177990

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.