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Record W2808062143 · doi:10.1002/jtr.2217

Cultural antecedents of inbound tourism in five Asian and Middle East countries: A fuzzy set qualitative comparative analysis

2018· article· en· W2808062143 on OpenAlexaff
Mozhgan Azimi Hashemi, Emmy Hanser

Bibliographic record

VenueInternational Journal of Tourism Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Comparative Analysis Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQualitative comparative analysisMiddle EastTourismDestinationsHappinessChinaEast AsiaExternalityQualitative researchGeographyMarketingSociologyBusinessEconomicsPsychologySocial psychologySocial science

Abstract

fetched live from OpenAlex

Abstract This study illustrates the effects of national culture on tourists' destination choices to Asian and the Middle East. This study clarifies how national cultural configurations indicate international tourists' willingness‐to‐travel to destinations. Five countries in this region—India, Iran, China, Egypt, and Jordan—were selected as destinations of outbound tourism in world counties. Fuzzy qualitative comparative analysis approach was applied to consider how complex configurations of national culture affect international tourism in countries mentioned. The study examines 6 attributes/conditions to explain outcomes. Results demonstrated that configurations that more important in selection Asian and Middle East destinations are presences of happiness and absences of cultural distance, dynamic externality, and societal cynicism. This study supports the cultural values and the cultural distance between origins and destinations, one of the antecedents of tourists' behaviours.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.335
GPT teacher head0.580
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations20
Published2018
Admission routes1
Has abstractyes

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