Evidence map›Paper›PMID 36212806›Full record

ArticlePsychology research and behavior management2022

Understanding Users' Continuance Usage Behavior Towards Digital Health Information System Driven by the Digital Revolution Under COVID-19 Context: An Extended UTAUT Model.

Bing Bai, Zhiqiong Guo

Abstract read
In one paragraph

Article in Psychology research and behavior management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 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

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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, 2 syntheses or guidelines pooled it.

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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.

Bing BaiBusiness School, Jiangsu Normal University, Xuzhou, 221116, People's Republic of China.
Zhiqiong GuoBusiness School, Jiangsu Normal University, Xuzhou, 221116, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Improving the health status of users through the use of digital health information systems has drawn the attention of practitioners and academics under the tide of the digital revolution, specifically in the time of global pandemic of COVID-19, and as a result, online medical consultation has developed rapidly. Purpose: Empirical studies, however, are lacking in terms of gaining insight into use digital health information system driven by the digital revolution under COVID-19 and identifying the factors for retaining users and encouraging their continuing use. To solve this problem, this study seeks to explore the factors that influence users' intention to use digital health information system. Methods: This study extended the Unified Theory of Acceptance and Use of Technology (UTAUT) model by introducing components of perceived risks into the model. Structural equation modeling was adopted to evaluate the research model based on an empirical survey of 241 users in China. Results: As indicated by the results, users' continuance usage behavior to digital health information system is shaped by intention to use and facilitating conditions, with effort expectancy, social influence, perceived ease of use and perceived enjoyment exerting indirect positive effects on continuance usage behavior via intention to use. In contrast, perceived risk and perceived cost have indirect negative impact on continuance usage behavior. Conclusion: The findings of this study can not only help the practitioners better understand the users' continuance usage behavior towards digital health information system driven by digital revolution in the time of COVID-19 pandemic and further tap into the potential market but also make up the short of traditional technology acceptance model explanatory by the extended UTAUT model.

Indexed as

continuance usage behaviordigital health information systemonline medicalUTAUT model

Identifiers

PMID36212806
PMCPMC9532259

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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.