Predicting Behavioral Intention: The Mechanism from Pretrip to Posttrip
Bibliographic record
Abstract
Despite research on predicting tourist behavioral intention, the existing research lacks a holistic understanding of the interrelationships among the determinants (i.e., a continuous mechanism from pretrip to posttrip). This article develops an integrated model to test the effects of motivation (pretrip), tourist activity participation and perceived value (on-site), and satisfaction (posttrip) on behavioral intention to help explain this mechanism. This article first establishes a five-factor structure of motivation and then examines the causal relationships among research constructs using structural equation modeling (SEM). Results show that motivation directly and significantly affects all other constructs and has strong total effects on satisfaction and behavioral intention. Tourist activity participation predicts satisfaction but not the behavioral intention. The relationships among perceived value, satisfaction, and behavioral intention are consistent with the literature. Regarding the total effects on behavioral intention, satisfaction is the strongest predictor, followed by perceived value and motivation. Also, this study is among only a few attempts to explore the Canadian domestic tourism market and provides marketing insights into destination marketing organizations (DMOs).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".