Visitor responses to environmental interpretation in protected areas in Vietnam: a motivation-based segmentation analysis
Bibliographic record
Abstract
Environmental interpretation is regarded as an effective soft management strategy for educating visitors and managing their impacts on protected areas. Only limited research has been conducted on visitors’ views on environmental interpretation in protected areas in the rapidly developing destinations of South-East Asia, with particular gaps in understanding different visitor groups. This article seeks to fill this gap in the context of Vietnam by examining visitor responses to services for environmental interpretation in one of the country’s largest national parks. The research employed importance-performance analysis and subsequent motivation-based visitor segmentation based on 237 sets of pre- and post-visit questionnaires distributed by the authors as self-complete questionnaires at the entry and exit gateway to the national park. The findings highlight that site interpreters were considered the most important service providers, while displays at the museum and videos were identified as important but low performing. A number of differences between motivation-based visitor groups as well as some culturally anchored response patterns emerged which highlighted the need for park management to consider different visitor groups; not only in terms of their motivations to visit but also their cultural backgrounds when designing, investing maintenance funding, and evaluating interpretive services.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".