Influence of Push and Pull Motivations on Satisfaction and Behavioral Intentions within a Culinary Tourism Event
Bibliographic record
Abstract
The study constructs a causal model of culinary tourist behavior from the theoretical framework of push and pull motivations. The study proposed that culinary event attendees' expenditures, word-of-mouth behavior, and repeat patronage intentions would be affected by their overall event satisfaction. Push and pull motivations subsequently were examined for effect on overall satisfaction. Using multiple regression analysis with data collected from an international culinary event the study examined the above relationship. Results of the analysis can be summarized as: 1) food, event novelty, and socialization were push motivations identified for attending a culinary event; 2) food product, support services, and essential services were pull motivations and had a significant predictive affect on overall satisfaction; and 3) overall satisfaction had a significant relationship with outcome variables: word-of-mouth behavior and repeat patronage intentions. It is believed that results of the present study will be useful to organizers of culinary events and/or destination managers.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".