Evidence map›Paper›PMID 37813664›Full record

ArticleKorean journal of women health nursing2023

What factors influence continuous usage intention of head-mounted display-based virtual reality content?: a cross-sectional survey.

JeongSil Choi, Heakyung Moon, Mijeong Park

Abstract read
In one paragraph

Article in Korean journal of women health nursing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

JeongSil ChoiCollege of Nursing, Gachon University, Incheon, Korea.
Heakyung MoonDepartment of Nursing, Hoseo University, Asan, Korea.
Mijeong ParkDepartment of Nursing, Hoseo University, Asan, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of this study was to explore the continuous usage intention of head-mounted display-based virtual reality (HMD-based VR) content among college students. The study also sought to understand how this intention is influenced by factors related to personal cognition, social aspects, VR content, and HMD-related elements.

methodsThis descriptive correlational study used a self-report questionnaire to survey 217 students from two universities in Korea who had prior experience with HMD-based VR content.

resultsThe mean score for continuous usage intention of HMD-based VR content was 2.59±0.57 points (range, 1-5 points). Regarding the average frequency of HMD-based VR content usage, 64.5% of participants reported using it 1 to 2 times, while 91.7% indicated a total HMD-based VR usage period of less than 6 months. Factors such as personal cognition, VR content, social aspects, and HMD-related elements had explanatory power of 35.1%, 10.7%, 4.4%, and 2.5%, respectively, for the continuous usage intention of HMD-based VR content. Additionally, engagement (β=.45, p<.001), influential others (β=.37, p<.001), environmental support (β=-.18, p=.030), and cyber sickness (β=-.21, p=.001) were identified as having a significant influence.

conclusionWhen developing HMD-based VR content, strategies to improve users' personal cognition should be included. Additionally, it is necessary to develop strategies that enhance enjoyment and interest in the content, while also facilitating ongoing social support. Furthermore, coping strategies should be devised that take into account cyber sickness, a potential side effect of these devices.

Indexed as

IntentionVirtual RealityCognitionCross-Sectional StudiesHumansPleasureHead-mounted displayHealth servicesIntentionVirtual reality

Identifiers

PMID37813664
PMCPMC10565525

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LicenceCC BY
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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.