Educational tourism and smart information and communication technologies (ICT) in geoparks: a behavioural framework for enhancing visitor experience
Bibliographic record
Abstract
In the age of digital transformation, intelligent information and communication technologies (ICT) has become essential to tourism; yet, its function in geopark-based educational tourism has not been well examined. This research examines the Ciletuh–Palabuhanratu UNESCO global geopark to analyze how tourists use smart ICT for education on geoheritage and sustainability. A contextual and adaptable conceptual model is provided, enhancing the unified theory of acceptance and use of technology version 2 (UTAUT2) framework by integrating two new constructs, perceived learning value (PLV) and geo-heritage engagement (GHE), as well as two moderating variables, educational level and digital experience. A thorough literature assessment and bibliometric analysis revealed research gaps concerning the incorporation of educational components in technology uptake within geoparks. The results demonstrate that dimensions including performance anticipation, hedonic motivation, and habit are primary determinants of behavioral intention, whereas PLV and GHE substantially improve user engagement and learning outcomes. Furthermore, educational background and digital experience attenuate the impact of fundamental structures on technology adoption. The suggested approach enhances theoretical comprehension by situating UTAUT2 within educational tourism and offers practical guidance for developing inclusive, engaging, and sustainable ICT applications. These findings provide essential direction for geopark administrators and policymakers in advancing digital learning initiatives that foster cultural appreciation, environmental consciousness, and community engagement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".