Coastal Cities Seen from Loyalty and Their Tourist Motivations: A Study in Lima, Peru
Bibliographic record
Abstract
The objective of this empirical study is to determine: (a) the underlying variables of the travel motivations related to a coastal city; and (b) the motivational dimensions that predict return, recommendation, and saying positive things about a coastal city as loyalty variables. This project utilized an in situ investigation carried out in Lima, a coastal city located on the Pacific Ocean near Peru with important natural and cultural attractions. The researchers used 381 questionnaires that were analyzed through factor analysis, in addition to the stepwise multiple regression method. Reesults identified six underlying variables or motivational factors: “culture and nature”, “authentic coastal experience”, “novelty and social interaction”, “learning”, “sun and beach”, and “nightlife”. Regarding loyalty, the “novelty and social interaction” dimension is the most important predictor of return and the “authentic coastal experience” dimension is the most important predictor of recommending and saying positive things about a coastal city. To motivate a return, events could be created on the beach to motivate novelty, as well as increase recommendations and the amount of positive things said about the destination; educational and sports activities and workshops could also be created with the community and the coastal environment. Results can be used by firms for preparing information for new customers in order to increase trip intention and improve guides for destination marketing organizations (DMOs).
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How this classification was reachedexpand
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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".