Evidence map›Paper›PMID 39055802›Full record

ArticleHeliyon2024

Users' willingness to adopt metaverse drawing on flow theory: An empirical study using PLS-SEM and FsQCA.

Yikai Liang, Xiaojie Zhang, Haiqing Wang, Mengqing Liu

Abstract read
In one paragraph

Article in Heliyon, 2024. 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

4 authors.

Yikai LiangSchool of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China.
Xiaojie ZhangSchool of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China.
Haiqing WangBusiness School, Shandong Xiehe University, Jinan 250000, China.
Mengqing LiuSchool of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a rapidly developing information technology in recent years, the metaverse has significantly transformed how we live, learn, and work. In order to accelerate the use of metaverse technology and promote users' acceptance of the metaverse, this study constructs an integrated model based on flow theory and use and satisfaction theory, to further explore the factors affecting users' acceptance of the metaverse. A total of 265 valid questionnaires were obtained through a situational questionnaire survey. Considering the limitations of a single analysis technique, we use two methods to analyze the data. Among them, the symmetric PLS-SEM method is mainly used to analyze the effects of single variables, while the asymmetric fsQCA method is used to analyze the combined effects of variables. The PLS-SEM results manifest that flow experience, perceived risk, and personal innovation directly influence users' acceptance of the metaverse, while perceived cost has no effect. Simultaneously, interactivity, presence, and social presence indirectly affect users' acceptance of the metaverse, while informativeness and enjoyment have no indirect effect. Significantly, fsQCA unveiled five configurations resulting in a high user acceptance of the metaverse, as well as six configurations leading to a negative acceptance. The complementary findings from PLS-SEM and fsQCA offer valuable insights for both theoretical understanding and practical implementation.

Indexed as

Flow theoryMetaverse adoptionQualitative comparative analysisStructural equation modellingUses and gratifications theory

Identifiers

PMID39055802
PMCPMC11269873

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.