MétaCan
Menu
Back to cohort
Record W1997914972 · doi:10.1080/19368623.2011.570648

Relationship Between Destination Image and Behavioral Intentions of Tourists to Consume Cultural Attractions

2011· article· en· W1997914972 on OpenAlexaff
Haywantee Ramkissoon, Muzaffer Uysal, Keith G. Brown

Bibliographic record

VenueJournal of Hospitality Marketing & Management · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsCape Breton University
Fundersnot available
KeywordsDestination imageTourismExtant taxonStructural equation modelingSalientPsychologyAdvertisingCultural tourismConsumer behaviourMarketingConceptual modelCultural heritageConceptual frameworkDestinationsSocial psychologySociologyGeographyTourism geographyBusinessComputer scienceSocial science

Abstract

fetched live from OpenAlex

The cultural tourism market segment has experienced increasing interest in recent years. This article analyzes the structural relationship between destination image and cultural behavioral intentions of tourists using the island of Mauritius as a case study. Drawing from an extant literature review, a conceptual model was developed which was tested using data collected from tourists visiting selected cultural and natural heritage sites of the island. Structural equation modeling (SEM) was used for analyzing the results. Findings indicated that destination image is a salient factor influencing the cultural behavioral intentions of tourists. The research also attempted to investigate which dimensions of image had the highest influence on behavioral intentions. Results indicated that the cultural attributes of the island exerted the highest influence on tourists' behavioral intentions. The theoretical and managerial implications of the study are discussed. The study concludes that destination image remains an integral concept requiring further investigation to understand the cultural tourist's behavior.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.125
GPT teacher head0.404
Teacher spread0.279 · 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

Citations108
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Hospitality Marketing & ManagementSame topicDiverse Aspects of Tourism ResearchFrench-language works237,207