The Role of Tourist Experience in Shaping Memorable Tourism Experiences and Behavioral Intentions
Bibliographic record
Abstract
Tourism experiences have become central to understanding travel behavior, as they go beyond the mere consumption of a product or service and include a holistic encounter with a destination.This study explores the important role of tourists' experiences in shaping memorable tourism experiences and subsequent behavioral intentions.Based on a comprehensive review of the literature on tourist behavior and destination marketing, this research examines the complex relationship between the quality of tourism experiences and the formation of lasting memories, as well as their influence on tourists' future behavioral intentions.This research uses a quantitative data approach collected through surveys.Quantitative data analysis was used to measure the relationship between these experiences and tourists' intention to revisit and recommend the Sawarna Tourism Village.These findings highlight the multidimensional nature of the tourist experience, which includes sensory, emotional, cognitive, and social dimensions.Positive experiences and negative experiences were identified as significant contributors to the creation of lasting memories.In addition, this research reveals that memorable tourism experiences have a major influence on behavioral intentions, with satisfied tourists showing a higher likelihood of repeat visits, positive recommendations regarding tourist villages.Using data obtained from the results of a survey of 201 respondents visiting Sawarna Tourism Village.The research results stated that eleven hypotheses were supported, and three hypotheses were not supported.This research contributes to the theoretical framework of tourist behavior and provides practical insights for the development and marketing of tourist villages.Implications for tourism village marketers and tourism village managers and policy makers are discussed, emphasizing the importance of crafting experiences that align with the diverse needs and expectations of tourists.By understanding the mechanisms through which experiences shape memories and subsequent behavior, tourism village stakeholders can strategically improve the overall tourism offering, foster long-term relationships with visitors and have a positive impact on the image and competitiveness of tourist villages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".