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Record W2065000156 · doi:10.1177/109634800002400210

Using Consumer Behavior Research to Understand the Baby Boomer Tourist

2000· article· en· W2065000156 on OpenAlexaboutno aff
Megan Cleaver, B. Christine Green, Thomas E. Müller

Bibliographic record

VenueJournal of Hospitality & Tourism Research · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismBaby boomersHospitalityMarketingBaby boomAdvertisingPromotion (chess)PleasurePerceptionConsumer behaviourEmpirical researchPsychologyMarket segmentationBusinessSociologyEconomicsGeographyPolitical scienceDemographic economicsDemography

Abstract

fetched live from OpenAlex

This article focuses on consumer behavior research to better understand Australian baby boomer tourists, although the principles and methods behind this empirical study are equally applicable to the baby boomer tourism markets in the United States, Canada, and New Zealand, all of which experienced a major postwar baby boom. Lifestyle research, using both secondary and primary data, was the principal behavioral research approach enhanced with survey findings on the psychological motivation for vacation travel among baby boomers, as well as their vacation risk perceptions and travel patterns. Three prime-target baby boomer lifestyle segments were identified on the basis of their propensity for pleasure travel, and the three groups were profiled by their travel motivations, risk perceptions, and patterns. Distinct intergroup differences were found, which indicate a need to tailor the development and promotion of new tourism and hospitality products to each segment of boomers. The findings have relevance for marketing to North American baby boomers as well.

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)0.044
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0040.004
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.339
GPT teacher head0.530
Teacher spread0.191 · 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; both teacher heads agree on what is shown here.

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

Citations43
Published2000
Admission routes1
Has abstractyes

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