Predicting Tourists' Intention to Consume Genetically Modified Food
Bibliographic record
Abstract
The role which food plays in the tourism industry cannot be ignored. However, the literature has also led to believe that food risks are perceived to be higher abroad than at home and this could act as an impediment for the tourism industry. Though research on food as a tourism product has been growing in the literature, little has been said about genetically modified (GM) food intake by tourists. This study develops a model to predict tourists' intention to consume GM food based on the postulates of the theory of planned behavior. Attitude to GM food, perceived behavioral control, and subjective norms are proposed as the determinants of behavioral intention to consume such foods. The model also considers perceived risks with GM foods to be an important determinant of attitude. Furthermore, factors likely to be antecedents of the travelers' perceived risks with GM foods are discussed and incorporated in the proposed model. Some propositions on which future research could be based on are also made. The study concludes that the model is particularly useful for those destinations introducing GM foods in their markets and where the tourism industry plays an important role.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".