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Record W2533491927 · doi:10.1177/1356766716672278

An interregional extension of destination brand equity

2016· article· en· W2533491927 on OpenAlexaff
Seongseop Kim, Markus Schuckert, Holly Hyungjeong Im, Statia Elliot

Bibliographic record

VenueJournal Of Vacation Marketing · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDestinationsBrand equityBusinessChinaTourismAdvertisingEquity (law)MarketingBrand loyaltyLoyaltyMarket shareBrand awarenessGeographyPolitical science

Abstract

fetched live from OpenAlex

Since the 1990s, the Asia-Pacific region’s world market share of international travelers has increased, as America’s and Europe’s shares have fallen. China (People’s Republic of China) has become the world’s biggest tourism source market with an overseas spend of US$292 billion in 2015, fueling opportunities for the region and beyond. Now, Asia Pacific outbound travel is extending past short-haul interregional travel to long-haul destinations, specifically Europe. To realize this potential, European destinations need a better understanding of the Chinese traveler; their perceptions of destinations, awareness, and loyalty. This study measures the brand equity of Switzerland and Austria as perceived by Hong Kong Chinese tourists. Structural equation modeling results indicate that destination brand image and associations significantly impact brand loyalty, whereas destination awareness does not, contrary to past interregional research findings. Understanding the influence of brand components on overall brand equity supports the efficacy of the brand equity model for interregional destinations.

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)0.002
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 score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.072
GPT teacher head0.431
Teacher spread0.360 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

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