Understanding the motivations of millennials in participating in wine tourism: a case study of the Kamloops wine trail
Bibliographic record
Abstract
Tourism has become one of the world’s largest and fastest growing contributors to the global economy, with Millennials being the industry’s largest market segment. As for most people, experiencing local culture and traditions is one of the main motivators to travel, thus resulting in an increased demand in gastronomic tourism. Introducing Millennials to wine tourism and understanding their motivation for participation is a crucial part to ensure the industry’s future growth. This research paper reviews literature vis-à-vis the tourism industry in Canada, one of the emerging destinations for culinary tourism. Special attention is drawn to wine tourism, wine tourist profiles and travel motivations, as well as the development and growth of wine tourism in British Columbia, which is one of the country’s major players in the wine industry. A case study on the Kamloops Wine Trail, British Columbia’s latest addition to wine tourism development, focuses on the Millennial Generation of students at Thompson Rivers University in engaging in local wine tourism practices. The results identify product related experiences as the main motivation for Millennials to participate in wine tourism, as well as the components of socializing and enjoying the natural setting as important attributes of a wine experience. By profiling the Millennial wine tourists who are dominantly female, the research provides recommendations for the participating wineries on the Kamloops Wine Trail to enhance their marketing strategy. Results indicate that Millennials rely on personal testimonials and recommendations when choosing to visit a winey yet are reluctant to share own experiences on social media.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".