Evidence map›Paper›PMID 37221504›Full record

ArticleBMC health services research2023

What drives the adoption of online health communities? An empirical study from patient-centric perspective.

Qianyao Zhang, Runtong Zhang, Xinyi Lu, Xijing Zhang

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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 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

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3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Qianyao ZhangDepartment of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China.
Runtong ZhangDepartment of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China. rtzhang@bjtu.edu.cn.
Xinyi LuSchool of Management and E-business, Zhejiang Gongshang University, Hangzhou, Zhejiang, China.
Xijing ZhangDepartment of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China.

Funding

the Fundamental Research Funds for the Central Universities 2022YJS039the National Natural Science Foundation of China 62173025the National Social Science Foundation of China 18ZDA086
6 · The paper itself

Abstract

backgroundOnline health communities (OHCs) provide platforms for patients to seek advice from physicians and receive professional suggestions online. It can improve the efficiency of patients' diagnosis of simple diseases and alleviate hospital congestion. However, few empirical studies have comprehensively explored the factors influencing patients' intention to use OHCs through objective data. This study aims to fill this gap by identifying key factors that influence patients' acceptance of OHCs and proposing effective ways to promote the applications of OHCs in China.

methodsBased on the Unified Theory of Acceptance and Usage of Technology (UTAUT), extended with additional constructs identified with patients' information demands in OHCs, this study developed a research model and proposed nine hypotheses. An online survey involving 783 valid responses was conducted in China to collect data to validate the proposed model. Confirmatory factor analysis and partial least squares (PLS) path model were conducted for instrument validation and hypothesis testing.

resultsPrice value, eHealth literacy, and performance expectancy are the most prominent constructs in the study context. Interestingly, relation quality was also found to have a significant positive relationship with behavioral intention.

conclusionsBased on these findings, OHC operators need to create a user-friendly platform, improve information quality, set reasonable prices, and establish consummate security systems. Physicians and related organizations can raise awareness and assist patients in developing the skills to appropriately comprehend and utilize information in OHCs. This study contributes to both technology adoption theory and practice.

Indexed as

HospitalsPatient-Centered CareChinaEmpirical ResearchFactor Analysis, StatisticalHumansBehavioral intentioneHealth literacyInformation demandsOnline health communities (OHCs)Price valueRelation quality

Identifiers

PMID37221504
PMCPMC10204683

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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.