AUGUST 2001JOURNAL OF TRAVEL RESEARCH Prediction of Senior Travelers ’ Motorcoach Use from Demographic, Psychological, and Psychographic
Bibliographic record
Abstract
Senior travelers (those 55 or older) are a substantial and accessible market for motorcoach operators because they represent a major portion of the motorcoach market. This study examined Pennsylvania senior travelers ’ motorcoach use and their important considerations in selecting a motorcoach tour. It was found that senior travelers ’ taking motorcoach tours could be predicted from their demo-graphic, psychological, and psychographic characteristics. The study provides practical implications that could be help-ful for tourist destinations and motorcoach companies to tar-get senior travelers and to build a sound marketing planning strategy. Motorcoach tour groups were one of the fastest and con-sistently growing segment of travel and tourism over the past decade (Chacko and Nebel 1993; Robinson 1994; Illum and Schaefer 1995). There are about 4,000 private motorcoach companies operating in the United States. A report prepared by the National Tour Association (NTA) indicated that a total of 624,626 tours were operated by tour companies in the United States and Canada in 1996, representing a 6.6% increase over 1995. They generated 74.1 million tour passen-ger days—a 5.3 % increase over 1995—and had a direct eco-nomic impact of $9.6 billion in tour expenditures in the United States (NTA and International Association of Con-vention & Visitors Bureaus 1996). Research shows that senior citizens, those 55 or older, represent 32 % of the motorcoach market (Robinson 1994) and 79 % of those 55 or older are likely to charter a motorcoach. As a point of perspective, the baby boomers (ages 35 to 54) represented the largest segment of past motorcoach travelers, with 36%. As this group ages, it can be argued they will continue to take motorcoach tours (United Motorcoach Association 1999). Although the senior market has been studied extensively (e.g., Teaff and Turpin 1996; Lieux, Weaver, and McCleary
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.011 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".