From crowding perception to destination loyalty. Assessing the moderating role of travel satisfaction in European cities [postprint]
Bibliographic record
Abstract
The post COVID-19 pandemic surge in tourism has created significant overcrowding challenges in major cities. This study examines the relationships between crowding perception, destination attractiveness, destination satisfaction, and destination loyalty, with a focus on the moderating role of travel satisfaction. Data were collected from 1,859 tourists in London, Paris, and Rome using Computer-Assisted Personal Interviews (CAPI). A structural equation modelling approach (PLS-SEM) was employed to analyze these relationships, incorporating a multigroup analysis to compare tourists based on their transport modes and mobility limitations. The relationships were analyzed through PLS-SEM, using a multigroup analysis to compare tourists based on their transport modes and mobility limitations. The results show that travel satisfaction moderates the effects of crowding on destination attractiveness and satisfaction, although the extent of this varies across cities and transport modes. In Rome, crowding increases satisfaction, while in London and Paris it has a negative effect. Differences are also found between tourists using micromobility, public transport, and walking. The findings highlight the need for tailored strategies to improve urban tourism experiences and address overcrowding, including consideration of tourists’ mobility preferences. This study advances the theory by emphasizing the moderating role of travel satisfaction.
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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 | 0.003 | 0.005 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".