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Record W1996101433 · doi:10.1002/jtr.541

Endorsement advertising in aboriginal tourism: an experiment in Taiwan

2005· article· en· W1996101433 on OpenAlexafffund
Janet Chang, Geoffrey Wall, Chen‐Tsang Tsai

Bibliographic record

VenueInternational Journal of Tourism Research · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Council
KeywordsAdvertisingTourismMarketingPsychologyBusinessPolitical science

Abstract

fetched live from OpenAlex

Abstract Aboriginal cultures have become important tourism attractions worldwide. Cultural villages provide ready access to selected aspects of these cultures in a staged format. They package and promote culture to tourists. Advertising is one component of the promotional mix. Using brochures as the advertising media, the objective of this research is to ascertain the types of advertising endorsers and advertising appeals that are most likely to be successful in attracting visitors to such cultural villages. Adopting an experimental approach, a two‐factor experimental design is manipulated. The causal effect is obtained by using MANOVA and Scheffe tests. The findings are twofold: the advertising effectiveness of using an aboriginal employee is better than using other types of endorsers; an emotional advertising appeal elicits a better response than a rational advertising appeal. Copyright © 2005 John Wiley & Sons, Ltd.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.058
GPT teacher head0.408
Teacher spread0.349 · 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 designRandomized trial
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

Citations38
Published2005
Admission routes2
Has abstractyes

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