A Demand-driven Success Factor Analysis for Agritourism in Switzerland
Bibliographic record
Abstract
This article aims to define categories of tourists’ motivations for visiting an agritourism farm. It also analyses the difference in motivation between and actual who have already been on an agritourism farm. Survey data from a total of 780 respondents (647 and 133 actual of agritourism facilities) were collected using a standardized questionnaire. The data was factor analysed (using PCA), and seven factors were identified: (1) comfort and consumption, (2) rural life, (3) accessibility to nature, (3) fun and relaxation on the farm, (4) regional products, (5) simple accommodation, and (6) culinary offers. The factors with most importance relate to the accessibility to nature as well as the possibility to buy regional food. The means of the factors were calculated for both respondent groups and compared using the Mann-Whitney U test. This test shows that the two groups potential customers and effective on the farm differ significantly only in the two characteristics of consumer behavior and comfort of accommodation. All other factors were either significantly less important or showed no differences for of agritourism farms. The results have implications for the communication process of agritourism farms or destination marketing organizations (DMOs), namely that the focus on comfort and consumption should be increased as a customer’s decision comes closer to definitive booking. Keywords: agritourism, success factors, factor analysis, consumer choice, accommodation facilities
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".