Investigating the appeal of a visitor guide: a triangulated approach
Bibliographic record
Abstract
Purpose Destination management organizations deliver travel-related information through visitor guides to build destination awareness and attract potential tourists. Therefore, this research aims to investigate how people read such a guide, understand their attitudes and to provide recommendations on enhancing its design. Design/methodology/approach This research used eye-tracking technology in tandem with surveys and in-depth interviews. Eye-tracking technology uncovered the elements of a visitor guide that attracted particular attention, whereas surveys and interviews provided deeper insights into people’s attitudes toward them. Findings People do not spend attention equally on each page of a visitor guide. Instead, they look at the reference points (i.e. photo credits, photos, headings and bolded words) and then read the adjacent areas if the information triggers their interest. The characteristics of the attractive components of a visitor guide were discussed and suggestions on designing a more appealing guide were provided. Research limitations/implications The triangulated approach not only generated objective and insightful results but also enhanced research validity. This exploratory sequential mixed method can usefully be applied to test other stimuli and assess attention. Practical implications To be deemed appealing, a visitor guide should avoid ads unrelated to the destination, include more photos, use the list format and bolded words, add stories or selected comments from social media and provide well-designed maps. Originality/value This research fills a gap in the literature by using a triangulated approach including eye-tracking, survey and interviews to examine a 68-page visitor guide. The concept of reference-point reading behavior is proposed. Practical implications are discussed to improve the design of a visitor guide.
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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 | 0.040 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".