Evidence map›Paper›PMID 36897630›Full record

ArticleJournal of medical Internet research2023

Determinants Influencing the Adoption of Internet Health Care Technology Among Chinese Health Care Professionals: Extension of the Value-Based Adoption Model With Burnout Theory.

Dongsheng Bian, Yuyin Xiao, Keyu Song, Minye Dong, Li Li, Ross Millar, Chenshu Shi, Guohong Li

Full text read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. 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

8 authors.

Dongsheng Bian *School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0003-2312-114X
Yuyin Xiao *School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0002-4621-9628
Keyu SongSchool of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0002-1213-0510
Minye DongSchool of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0002-6465-7015
Li LiChina Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, China.ORCID 0000-0001-7012-4699
Ross MillarHealth Services Management Centre, University of Birmingham, Birmingham, United Kingdom.ORCID 0000-0001-5790-7947
Chenshu ShiCenter for Health Technology Assessment, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, China.ORCID 0000-0002-4995-4169
Guohong LiSchool of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0003-0445-5363

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe global COVID-19 pandemic has been widely regarded as a catalyst for adopting internet health care technology (IHT) in China. IHT consists of new health care technologies that are shaping health services and medical consultations. Health care professionals play a substantial role in the adoption of any IHT, but the consequences of doing so can often be challenging, particularly when employee burnout is prevalent. Few studies have explored whether employee burnout influences the adoption intention of IHT in health care professionals.

objectiveThis study aims to explain the determinants influencing the adoption of IHT from the perspective of health care professionals. To do so, the study extends the value-based adoption model (VAM) with consideration for employee burnout as a determining factor.

methodsA cross-sectional web-based survey using a sample of 12,031 health care professionals selected through multistage cluster sampling from 3 provinces in mainland China was conducted. The hypotheses of our research model were developed based on the VAM and employee burnout theory. Structural equation modeling was then used to test the research hypotheses.

resultsThe results indicate that perceived usefulness, perceived enjoyment, and perceived complexity positively correlate with perceived value (β=.131, P=.01; β=.638, P<.001; β=.198, P<.001, respectively). Perceived value had a positive direct effect on adoption intention (β=.725, P<.001), perceived risk negatively correlated with perceived value (β=-.083, P<.001), and perceived value negatively correlated with employee burnout (β=-.308, P<.001). In addition, employee burnout was negatively related to adoption intention (β=-.170, P<.001) and mediated the relationship between perceived value and adoption intention (β=.052, P<.001).

conclusionsPerceived value, perceived enjoyment, and employee burnout were the most important determinants of IHT adoption intention by health care professionals. In addition, while employee burnout was negatively related to adoption intention, perceived value inhibited employee burnout. Therefore, this study finds that it is necessary to develop strategies to improve the perceived value and reduce employee burnout, which will benefit the promotion of the adoption intention of IHT in health care professionals. This study supports the use of the VAM and employee burnout in explaining health care professionals' adoption intention regarding IHT.

Indexed as

Burnout, ProfessionalCOVID-19Biomedical TechnologyCross-Sectional StudiesHealth PersonnelHumansIntentionPandemicsSurveys and QuestionnairesburnoutChinacross-sectional studyemployee burnouthealth carehealth care workersinternetinternet health care technologyinternet hospitalstechnologyvalue-based adoption model

Identifiers

PMID36897630
PMCPMC10039406

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read11
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 OpenQuestion graph.