Evidence map›Paper›PMID 38947345›Full record

ArticleFrontiers in public health2024

Continuance intention and digital health resources from the perspective of elaboration likelihood model and DART model: a structural equation modeling analysis.

Chengcheng Fei, Haixia Zhou, Wei Wu, Longyuan Jiang, Yuanqi Xu, Haiyan Yu

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. 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

6 authors.

Chengcheng FeiSchool of Medical Humanities and Management, Wenzhou Medical University, Wenzhou, China.
Haixia ZhouSchool of Medical Humanities and Management, Wenzhou Medical University, Wenzhou, China.
Wei WuSchool of Medical Humanities and Management, Wenzhou Medical University, Wenzhou, China.
Longyuan JiangSchool of Medical Humanities and Management, Wenzhou Medical University, Wenzhou, China.
Yuanqi XuSchool of Medical Humanities and Management, Wenzhou Medical University, Wenzhou, China.
Haiyan YuSchool of Medical Humanities and Management, Wenzhou Medical University, Wenzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Internet hospitals, online health communities, and other digital health APPs have brought many changes to people's lives. However, digital health resources are experiencing low continuance intention due to many factors, including information security, service quality, and personal characteristics of users. Methods: We used cross-sectional surveys and structural equation modeling analysis to explore factors influencing user willingness to continue using digital health resources. Results: Information quality ( Conclusion: The keys to solving the problem of low continuance intention are improving the quality and service level of digital health resources, and promoting users' value co-creation behavior. Meanwhile, enterprises should build a good reputation, create a positive communication atmosphere in the community, and enhance user participation and sense of belonging.

Indexed as

IntentionLatent Class AnalysisAdultCross-Sectional StudiesFemaleHealth ResourcesHumansMaleMiddle AgedSurveys and QuestionnairesTrustYoung Adultcontinuance intentionDART modeldigital health resourceselaboration likelihood modelstructural equation modeluser value co-creation behavior

Identifiers

PMID38947345
PMCPMC11211600

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.