Please visit again: investigating the role of awe experiences, leisure satisfaction, and perceived safety in theme park retention strategies
Bibliographic record
Abstract
Customer retention is critical for gaining a competitive edge in the highly dynamic theme park industry. This study highlights the role of awe experiences, leisure satisfaction, and psychological factors in shaping moments that foster customer loyalty. Guided by the stimulus-organism-response (SOR) framework, a conceptual model was tested using data from 460 tourists across three leading Indian theme parks through PLS-SEM analysis. Cognitive enjoyment, hedonic benefits, and concentration significantly influenced awe, which subsequently enhanced leisure satisfaction and customer retention. Perceived safety emerged as a critical moderating factor. The study also examined demographic variations, revealing notable differences across age (Gen Z vs. older generations) and gender (male/female). These findings offer actionable insights for marketers and operators to design more engaging, inclusive, and safe experiences that promote loyalty. The research contributes to experiential marketing literature by underscoring the psychological mechanisms and emotional drivers central to visitor retention in the theme park sector.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".