Evidence map›Paper›PMID 40479417›Full record

ArticleTourism and hospitality research : the Surrey quarterly review2022

Understanding Tourists' Attitude Toward Online Travel Health Information During and Post-COVID-19: A Health Belief Model Application.

Arej Alhemimah

Abstract read
In one paragraph

Article in Tourism and hospitality research : the Surrey quarterly review, 2022. 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

1 author.

Arej AlhemimahKing Abdul-Aziz University, Jeddah, Saudi Arabia.ORCID https://orcid.org/0000-0002-4522-8645

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to people's anxieties about COVID-19, it may take years before international tourism returns to pre-pandemic levels. Thus, it is crucial to understand how tourists' health beliefs influence their travel decision-making processes during and after the SARS-COV2 pandemic, and to develop new strategies to support and meet tourists' current needs and concerns. The current study employs a Health Belief Model (HBM) perspective to examine the influence on tourists' health risk prevention - and subsequently on their travel intention - of reading travel health information online, while considering tourists' perceptions of threat susceptibility and severity, and usefulness of travel health information. As risk perception is influenced by individual differences such as gender and previous experience, the study model includes the demographic factors of age, gender, and health status. The model was tested using a survey questionnaire completed by 261 respondents in Saudi Arabia who were considering travelling abroad for tourism. Results were analyzed using PLS-SEM. The study found that perceived susceptibility and perceived usefulness each significantly and positively influenced the perception of importance of reading health information, and the perception of importance of reading travel health information online significantly and positively influenced travel intention. The discussion includes additional findings as well as implications for industry practice and policy regarding online pandemic-related information, in order to improve protection efficacy and enhance information content and style to adequately serve the needs of tourists from a health belief perspective.

Indexed as

e-marketingHealth beliefonline informationperceived risktravel intention

Identifiers

PMID40479417
PMCPMC9760503

What OpenQuestion holds

Textmetadata
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.