Evidence map›Paper›PMID 42275371›Full record

ArticlePloS one2026

The effect of food sensory experience on tourist engagement behavior: A study based on mental imagery theory.

Sen Yang, Yi Liu, Liping Xu

Abstract read
In one paragraph

Article in PloS one, 2026. 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
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0citing papers in PubMed
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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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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

3 authors.

Sen YangSchool of Culture and Tourism, Hefei University, Hefei, People's Republic of China.ORCID https://orcid.org/0000-0001-9953-4499
Yi LiuShenzhen Tourism College, Jinan University, Shenzhen, People's Republic of China.
Liping XuSchool of Marxism, Anhui Xinhua University, Hefei, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research on the effect of food tourism on tourist engagement behavior is an important gap in tourism research. Based on mental imagery theory, this study constructed a theoretical model to explore how food sensory experience influences tourist engagement behavior, with food memory (stored multisensory experiences of food) and mental imagery (active cognitive reconstruction of sensory information) as parallel mediating variables. This is the first study to integrate mental imagery theory into food tourism research, focusing on the core role of multisensory experience. A cross-sectional online survey was conducted targeting tourists who participated in Zibo's food tourism (Shandong, China)-a typical case of post-epidemic tourism recovery driven by food-and 447 valid questionnaires were collected. Structural equation modeling (SEM) with bootstrapped indirect effects (sample size = 5000, 95% confidence interval) was used for data analysis. Results show that: (1) Food sensory experience (encompassing sight, smell, taste, and touch) exerts a significant positive effect on tourist engagement behavior, with the strongest impacts on feedback intention (β = 0.480, p < 0.001) and revisit intention (β = 0.411, p < 0.001); (2) Food memory and mental imagery play significant parallel mediating roles-sensory experience independently activates both constructs, which in turn drive engagement; (3) Mental imagery has a stronger mediating effect on recommendation intention than food memory, while food memory is more impactful on revisit intention. Theoretically, this study clarifies the sensory-cognitive-behavioral mechanism underlying tourist engagement, filling the gap of how multisensory experiences shape engagement behavior. Practically, it provides actionable strategies for tourism destinations to enhance tourist engagement. In addition, the implications and suggestions for future research are discussed and meaningful implications for relevant tourism destinations are provided.

Indexed as

FoodImaginationTourismAdultChinaCross-Sectional StudiesFemaleHumansMaleMemorySurveys and QuestionnairesYoung Adult

Identifiers

PMID42275371
PMCPMC13258128

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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.