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Record W1969956503 · doi:10.1177/1096348003256604

The Cross-Border Consumer: Investigation of Motivators and Inhibitors in Dining Experiences

2004· article· en· W1969956503 on OpenAlexaboutno aff
Kenneth R. Lord, Sanjay Putrevu, H. G. Parsa

Bibliographic record

VenueJournal of Hospitality & Tourism Research · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsnot available
Fundersnot available
KeywordsSeekersVariety (cybernetics)MarketingAdvertisingConsumption (sociology)Value (mathematics)PerceptionEthnocentrismPsychologySample (material)Cross-culturalConsumer behaviourConsumer ethnocentrismSocial psychologyBusinessSociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Consumers who cross national borders potentially participate in multiple consumption experiences. This study focuses on one of the most common—the dining experience. It examines the variables that motivate or inhibit crossing national borders to dine and profiles motivational segments of cross-border diners. A sample of 466 cross-border diners living near the U.S.-Canada border provided information about an array of potential influencing variables. Factor analysis revealed the existence of eight dimensions: ethnocentrism, value drivers, variety seeking, awareness, affective/social considerations, ease of border crossing, perceived differences between restaurants on the two sides of the border, and distance fromthe border. Ethnocentrism and affective/social considerations exerted the strongest influence on consumer cross-border-dining perceptions and behaviors. Three distinct motivational segments emerged—variety seekers, comfort seekers, and value seekers. Discussion profiles each segment, identifies marketing implications, and proposes relevant strategies.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.046
GPT teacher head0.394
Teacher spread0.347 · 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 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

Citations30
Published2004
Admission routes1
Has abstractyes

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