A systematic review of augmented reality tourism research: What is now and what is next?
Bibliographic record
Abstract
The application of augmented reality in tourism is flourishing and promising, bringing an emerging body of studies. While virtual reality might be a virtual threat to the travel and tourism as being a potential substitute, augmented reality allows users to interact with the real environment that could potentially enhance visitors’ experience. Distinguishing from reviews that combine studies of augmented reality and virtual reality, this study systematically investigates the current state of augmented reality research exclusively in the tourism literature. The results identify five established and emerging research clusters, with one predominant cluster that focuses on user acceptance of augmented reality, commonly applying the technology acceptance model. A meta-analysis of a subset of four empirical studies reveals that perceived ease of use has an overall influence of 52.79% on perceived usefulness. Lastly, a concept map visually presents the constructs that have been explored across the clusters. Based on our review, future research directions are proposed to advance knowledge in the emerging area of gamification, to explore the potential negative consequences of augmented reality, and to apply more innovative methods and study designs.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".