Predicting Behavioral Intention: The Mechanism from Pretrip to Posttrip
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it