Distribution Channels in the Travel Industry: Using Mystery Shoppers to Understand the Influence of Travel Agency Recommendations
Bibliographic record
Abstract
The objective of this study was to understand the factors that influence travel agency recommendations in the United Kingdom. This was achieved using a mixture of focus groups, interviews, and “mystery shoppers.” Exploration into the process of choosing a holiday showed that the brochure plays an important role for many consumers. However, for the travel agent the brochure is low priority, and even when the brochure is used the travel agent often has a considerable amount of influence on consumer decision making. Whether the agency is vertically integrated has considerable influence on the recommendation process. Based on analysis of the interviews and focus groups, a model was developed and tested using mystery shoppers. Results from investigating 156 travel agents across the United Kingdom indicate that the majority of travel agents owned by large tour operators will attempt to push the holidays of their parent company rather than give impartial advice to consumers. Theoretical and practical implications are discussed.
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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 | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".