Evidence map›Paper›PMID 37221562›Full record

ArticleBMC health services research2023

Factors influencing loyalty to online health consultation platform: a cross-strait cultural perspective.

Li-Yun Huang, Wen-Ming Shiau, Pei Chin Chou

Abstract read
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Li-Yun HuangDivision of Dermatology, E-Da Cancer Hospital, Kaohsiung, Taiwan.
Wen-Ming ShiauInstitute of Information Management, National Chung Cheng University, Chia-Yi, Taiwan.
Pei Chin ChouDepartment of Respiratory Therapy, Fu Jen Catholic University, No.510, Zhongzheng Rd., Xinzhuang Dist, New Taipei City, 242062, Taiwan. harning999@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe geographical, cultural, and linguistic proximity between Taiwan and Mainland China has facilitated rapid growth of cross-strait interactions. Both countries have developed online health consultation platforms on the Internet for the public to access healthcare related information. This study examines factors that influence loyalty to a specific online health consultation platform (OHCP) from a cross-strait perspective.

methodsBased on the Expectation Confirmation Theory and the combined Trust, Perceived Health Risks and Culture, we examine factors that influence loyalty to OHCPs among cross-strait users by investigating the roles of trust, perceived health risks, and culture. Data was collected through a questionnaire survey.

resultsThe research models used provide a high-power explanation of loyalty to OHCPs. Results generally align with those of previous studies, with the exception of the relationships between Perceived Health Risks and Perceived Usefulness, Perceived Usefulness and Loyalty, Confirmation and Satisfaction, and Trust and Loyalty. In other words, culture may have moderated these relationships.

conclusionsFindings can help promote OHCPs among cross-strait users to make things easier for patients, and further reduce the load on the emergency department, especially in view of the still ongoing issues related to global outbreak of Coronavirus disease by facilitating early detection of potential cases.

Indexed as

Coronavirus InfectionsChinaDisease OutbreaksEmergency Service, HospitalHumansReferral and ConsultationCultureExpectation confirmation theoryLoyaltyOnline Health Consultation platformPerceived Health RisksTrust

Identifiers

PMID37221562
PMCPMC10204682

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LicenceCC BY
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Registered trials

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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.