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Record W3125872432 · doi:10.1080/14927713.2021.1872405

The antecedents of behavioural intention for island tourism across traveller generations: a case of Bali

2021· article· en· W3125872432 on OpenAlexvenueno aff
Andriani Kusumawati, Humam Santosa Utomo, Suharyono Suharyono, Sunarti Sunarti

Bibliographic record

VenueLeisure/Loisir · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsTourismWord of mouthAdvertisingHarmBeautyQuality (philosophy)MarketingDestination imageBusinessConstruct (python library)Natural (archaeology)PsychologyDestinationsGeographySocial psychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Island tourism presents the natural beauty and the authenticity of local culture that provides a unique experience for foreign tourists. If not being managed properly, tourist abundance at the island destination can threaten the destination quality which can actually harm tourists and reduce trust. The main objective of this study is to construct an understanding regarding the effect of destination quality on trust and behavioural intention. The sample in this research was 450 international tourists visiting Bali, Indonesia. Bali is a natural-based and cultural-based tourism destination which offers natural beauty, the authenticity of local culture, and unique cultural attractions. WarpPLS was used to analyze the effect of destination quality, trust, word-of-mouth intention, and revisit intention. The findings show that destination quality has a significant effect on trust. Trust is proven to have a significant effect on word-of-mouth intention and revisit intention. In addition, the results of this study also show that destination quality has a direct effect on word-of-mouth intention and revisit intention. More in-depth results show that in generation Y, destination quality has no significant effect on word-of-mouth intention and revisit intention.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.051
GPT teacher head0.345
Teacher spread0.294 · 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 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

Citations5
Published2021
Admission routes1
Has abstractyes

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