Predicting Indonesia’s Urban Heritage Tourist Loyalty: The Impact of Memorable Tourism Experience, Cultural Destination Image, and Cultural Motivation
Bibliographic record
Abstract
The rich tapestry of Indonesian culture, with its multifaceted and diverse expressions, exerts a significant influence on the nation's tourist industry. Recognizing this, the integration of cultural heritage into tourism development has become a central pillar of national strategies aimed at boosting tourism revenue and strengthening the national economy. This study aims to elucidate the interconnected relationship between memorable tourism experiences, cultural destination image, and cultural motivation in influencing tourist loyalty within the context of Indonesia's urban heritage tourism. This research employs the structural equation modelling-partial least squares (SEM-PLS) approach, utilizing SmartPLS 4.0 software for data analysis. The main findings of this study confirmed that memorable tourism experiences could encourage tourist loyalty in Indonesia's urban heritage. Moreover, the study emphasises the essential contribution of cultural motivation in driving tourist loyalty. This study holds significant managerial implication, this research will give several pictures like how to attract the tourism through tourist experience features.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".