Using Consumer Behavior Research to Understand the Baby Boomer Tourist
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".