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Record W3161540503 · doi:10.1080/19407963.2021.1928148

The role of financial and epidemic crises on tourism loyalty

2021· article· en· W3161540503 on OpenAlexaboutno aff
Mohammad Al‐Shboul, Sajid Anwar, Iman Akour

Bibliographic record

VenueJournal of Policy Research in Tourism Leisure and Events · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismLoyaltyChinaFinancial crisisSpurious relationshipDevelopment economicsBusinessEconomicsPolitical scienceMarketingMacroeconomics

Abstract

fetched live from OpenAlex

This paper examines the role of financial and epidemic crises on tourism loyalty. Using monthly data from January 1978 to February 2016, our analysis shows that tourists arriving to Singapore from 11 countries (Australia, Canada, China, France, Germany, Hong Kong, India, Japan, Taiwan, the UK, and the US) exhibit evidence of tourism loyalty, suggesting that international tourists considered Singapore as a long-run attractive tourist destination. During the five subsample periods, representing the four most recent global financial and epidemic crises (the AFC, SARS, the GFC and H1-N1), we find evidence of transitory effect, suggesting that international tourists considered Singapore as an attractive tourism destination in the short-run. We argue that shocks generated by the financial and epidemic crises strongly contribute to the existence of the permanent loyalty evidence, and that such evidence is likely to be spurious.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.073
GPT teacher head0.438
Teacher spread0.366 · 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 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

Citations4
Published2021
Admission routes1
Has abstractyes

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