Evidence map›Paper›PMID 41193621›Full record

ArticleScientific reports2025

Integrating TAM-ISSM-SOR framework to explore how virtual tourism experience influences tourists' travel intention.

Li Chen, Xianling Wu, Yidong Liu, Zhiyuan Hu, Xing Chen, Zheyue Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Li ChenSchool of Design, Jianghan University, Wuhan, China.
Xianling WuSchool of Design, Jianghan University, Wuhan, China.
Yidong LiuSchool of Design, Jianghan University, Wuhan, China. vintonliu@hanyang.ac.kr.
Zhiyuan HuSchool of Design, Jianghan University, Wuhan, China.
Xing ChenSchool of Design, Jianghan University, Wuhan, China.
Zheyue ZhangSchool of Design, Jianghan University, Wuhan, China.

Funding

The Youth Program of the China National Social Science Foundation 24CSH036
6 · The paper itself

Abstract

The rapid development of immersive technologies has transformed the tourism experience, enabling tourists to explore destinations virtually before physical visits. This study investigates how virtual tourism experiences influence tourists' intentions to visit real-world destinations, using the online museum of Porcelain Temple as a case study. Integrating the Stimulus-Organism-Response (SOR) framework with the Information System Success Model (ISSM) and the Technology Acceptance Model (TAM), the research examines the effects of system quality (SYQ), information quality (IQ), and service quality (SEQ) on perceived usefulness (PU), perceived ease of use (PEU), and perceived enjoyment (PE), which in turn affect tourists' travel intentions (TI). Data collected from 602 online museum users were analyzed using structural equation modeling. Results indicate that IQ and SEQ significantly influence PU, PEU, and PE, while SYQ significantly impacts PEU and PE but not PU. PU, PEU, and PE all positively affect TI, highlighting the importance of both cognitive and affective responses in translating virtual experiences into real-world travel behavior. These findings provide theoretical insights into virtual-to-physical tourism conversion and practical guidance for optimizing online museum platforms to stimulate on-site visitation.

Indexed as

TourismVirtual RealityAdolescentAdultFemaleHumansInformation TechnologyMaleMiddle AgedMuseumsSurveys and QuestionnairesYoung AdultISSMOnline museumSORTAMVirtual tourism

Identifiers

PMID41193621
PMCPMC12589446

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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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.