Evidence map›Paper›PMID 42192151›Full record

ArticleScientific reports2026

Determinants of medical crowdfunding information sharing attitudes through integrated source credibility and technology acceptance models.

Yingying Cai, Syafila Kamarudin

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Yingying CaiSchool of Media and Communication, Yili Normal University, Yining, Xinjiang, China.
Syafila KamarudinInstitute for Social Science Studies, Universiti Putra Malaysia, Seri Kembangan, Selangor, Malaysia. syafila@upm.edu.my.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical crowdfunding has emerged as an important alternative for financing healthcare expenses, enabling individuals to raise funds through donation-based campaigns. While social media facilitates campaign outreach, concerns regarding privacy, misinformation, and trust hinder information-sharing behaviors, limiting campaign effectiveness. This study contributes to this understanding by integrating Source Credibility Theory and the Technology Acceptance Model to examine how expertise, trustworthiness, perceived usefulness, and perceived ease of use influence attitudes toward sharing medical crowdfunding information. A cross-sectional survey was conducted among 345 young social media users in China, with data analyzed using PLS-SEM. The findings reveal that expertise does not directly impact sharing attitudes but influences them through perceived usefulness, whereas trustworthiness exerts both significant direct and indirect effects on sharing attitudes. Additionally, perceived usefulness and perceived ease of use positively predict sharing attitudes, highlighting the importance of practical value and platform usability in driving user engagement. The findings advance understanding of credibility-technology interactions in medical crowdfunding information sharing, though the youth-focused sample limits generalizability. This integrated approach offers promising directions for optimizing campaign engagement strategies through balanced credibility signaling and enhanced platform usability.

Indexed as

AttitudeCrowdsourcingInformation DisseminationSocial MediaAdolescentChinaCross-Sectional StudiesFemaleHumansMaleSurveys and QuestionnairesTrustYoung AdultExpertiseInformation sharingMedical crowdfundingPerceived ease of usePerceived usefulnessTrustworthiness

Identifiers

PMID42192151
PMCPMC13469478

What OpenQuestion holds

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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.