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Record W2178901577 · doi:10.1108/ijtc-08-2014-0016

Tourism destination image development: a lesson from Macau

2015· article· en· W2178901577 on OpenAlexaff
Weng Hang Kong, Hilary du Cros, Chin‐Ee Ong

Bibliographic record

VenueInternational Journal of Tourism Cities · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsTourismVisitor patternStakeholderOriginalityDestination imageMarketingDestinationsGovernment (linguistics)BusinessPerceptionAdvertisingGeographyPublic relationsPolitical scienceSociologyQualitative researchPsychology

Abstract

fetched live from OpenAlex

Purpose – Drawing upon an analysis of resident and visitor survey data and Macau Government Tourist Office (MGTO) press releases in 2012, the purpose of this paper is to understand the tourism destination image for this tourist historic city produced by these three key stakeholder groups in Macau. Design/methodology/approach – This is achieved using a new stakeholder analysis tool, developed from previous studies, which compares the perspective of the MGTO, the city’s destination marketing organization, with that of its residents and visitors. This study examines the perceptions that residents and visitors have about the general images projected and generated in Macau. Findings – This research highlights the multiplicity of images and producers of images in Macau. Originality/value – The lesson from this case study is that public sector agencies need to acknowledge more clearly the tourism planning role of the host community in particular. The possibility of detecting disconnections and misalignments of shared destination imagery by residents and visitors has implications for the public sector in Macau and other destinations in relation to managing and developing a destination and contributes to a greater understanding of stakeholders and sustainable tourism development overall.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.362
Teacher spread0.301 · 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 designNot applicable
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

Citations61
Published2015
Admission routes1
Has abstractyes

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