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Record W4407113000 · doi:10.6007/ijarbss/v15-i2/24574

From Value to Action: Exploring How Value Perception and Satisfaction Shape Behavioral Intentions in Folk Museum Tourism

2025· article· en· W4407113000 on OpenAlexaff
Li Qin, Hanina Halimatusaadiah Hamsan, Jeffrey Lawrence D’Silva, Xingjie Wang

Bibliographic record

VenueInternational Journal of Academic Research in Business and Social Sciences · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsCanadian Rural Health Research Society
Fundersnot available
KeywordsTourismAction (physics)PerceptionValue (mathematics)PsychologySocial psychologyGeographyMathematicsArchaeologyStatistics

Abstract

fetched live from OpenAlex

From the demand perspective, tourists’ value perceptions of folk museum tourism and their behavioral intentions are pivotal for the sustainable and healthy development of the burgeoning folk culture tourism sector. These factors not only shape the growth of the industry but also play a crucial role in the preservation and dissemination of Chinese folk culture. This paper reports an empirical survey of 201 tourists, using a revised scale adapted from previous research. A structural equation model was developed to analyze the relationships among tourists’ value perception, satisfaction, and behavioral intention, focusing on visitors to folk museums and scenic spots. The results reveal: 1) value perception has a significant and direct positive effect on satisfaction; 2) satisfaction directly and positively influences behavioral intention; 3) value perception exerts a significant positive influence on behavioral intention, both directly and indirectly; and 4) satisfaction acts as a mediator between value perception and behavioral intention. These findings provide actionable insights for promoting folk museum tourism and advancing cultural heritage preservation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.264
GPT teacher head0.439
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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