Sensation Seekers as a Target Market for Volunteer Tourism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The purpose of this study was to determine if sensation seeking and consumer innovativeness are useful characteristics in identifying a productive target market for volunteer tourism offerings. Sensation seeking and consumer innovativeness are trait characteristics describing needs for new experiences, risk taking, simulation, and consumer willingness to integrating these needs into their consumption of products and services. Extreme sports enthusiasts, thought to be high sensation seekers, were surveyed. Chain-referral methods were used to recruit the sample. Findings indicate that respondents were high in sensation seeking and consumer innovativeness. Many also expressed a desire for future volunteer work. Findings indicate that respondents would be a potential target market for volunteer tourism experiences and suggest that certain individual traits can be useful in identifying other individuals that would be a productive target market for volunteer tourism offerings. A better understanding of the benefits this group desires can have implications for approaching this group. The area of volunteer tourism is relatively new and underresearched. Investigating whether or not high sensation seekers represent a potential market for volunteer tourism has not been previously researched. KEYWORDS: consumer innovativenesssensation seekingvacation volunteeringvolunteer tourismvoluntourism This project was partially supported by a grant from the Auburn University Montgomery Research Grant-in-Aid Program. Notes a Totals may not equal due to nonresponse to specific questions. b HHI, annual household income. a Gender is a dummy variable in which female = 0, male = 1. a Gender is a dummy variable in which female = 0, male = 1.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it