Sustaining Coastal Cultural Tourism Through Community-Based Approaches: The Role of Heritage and Cultural Value
Bibliographic record
Abstract
Cultural heritage is essential for sustaining and enhancing community-based tourism (CBT), particularly in culturally rich destinations like Bang Saray Village, Chonburi Province, Thailand.Balancing cultural preservation with tourism development is challenging, as limited community involvement can reduce ownership and hinder sustainability.This study tested a structural model linking cultural heritage (CH), cultural learning (CL), cultural value (CV), CBT, and sustainable cultural tourism site development (SCT).Data were collected from 560 residents and stakeholders through purposive and convenience sampling using a structured questionnaire, reviewed by experts for validity.Respondents rated items on a five-point Likert scale, and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).Results indicated that CH significantly supports CBT by fostering CL, creating CV, and enhancing community engagement and pride.The model explained substantial variance in endogenous constructs (R² = 0.739-0.773),with all hypothesized paths significant (p < 0.05).The findings advance theoretical understanding of CH as a foundation and catalyst for sustainable tourism.Practically, they guide policymakers, local administrators, and community leaders to integrate cultural preservation, education, and participatory strategies into tourism planning, promoting authenticity, inclusivity, and long-term sustainability.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".