Luxurious or economical? An identification of tourists’ preferred hotel attributes using best–worst scaling (BWS)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article explores consumer tendencies to opt for luxury or economy hotels by identifying their most and least important selection attributes. The researchers investigate how sociodemographic and behavioral characteristics influence traveler assessments of hotel attributes. In explaining consumer hotel selection preferences, the researchers used an unconditional method—best–worst scaling (BWS). Based on an analysis of responses from 397 luxury hotel customers and 351 economy hotel customers in the United States, it was found that the two groups perceive hotel attributes differently. Differentials were also identified on the basis of gender, income, and frequency of purchase. While acknowledging that the task is complex, there is an urgent need to identify the factors influencing hotel selection, because hoteliers need to attract new markets and also balance this by retaining existing patrons. The findings extend existing literature by applying BWS to the identification of hotel selection attributes.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it