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Record W2517880896 · doi:10.5539/ibr.v9n10p127

Influences of Macau Visitor Expectations on Purchase and Behavioural Intention: Perspectives of Low-cost Carrier Passengers

2016· article· en· W2517880896 on OpenAlexvenueno aff
Suk Ha Grace Chan, Yang Marco Li, Yang Carol Song

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAVJavna Agencija za Raziskovalno Dejavnost RS
KeywordsVisitor patternLow-cost carrierTourismBusinessMarketingAdvertisingService qualityContext (archaeology)Perspective (graphical)ServicescapeService (business)Affect (linguistics)Quality (philosophy)PsychologyGeographyComputer science

Abstract

fetched live from OpenAlex

In today’s highly competitive environment, air transport is crucial to tourism development. A destination marketer must understand the reasons that affect visitors’ purchase behaviour to facilitate service quality. This study aimed to provide specific clues to understanding the low-cost airline (LCA) business in a broad context and to present a holistic perspective of the buying behaviour of low-cost carrier (LCC) passengers. Through its investigation of the motivations of LCC passengers, it found that the main determinants of passengers’ behavioural intentions rested on the values of these passengers and their satisfaction with the destination. This study adopted a qualitative approach in which 30 individuals who travelled to Macau within the last 6 months were interviewed. According to the results, the destination of Macau had a significantly positive effect on the visitors’ purchase intentions. Recommendations for destination marketers are identified in this study.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.097
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.096
GPT teacher head0.355
Teacher spread0.259 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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