Bibliographic record
Abstract
Hotel guests seek value, and hotel managers seek to provide that value. The matter is not that simple, however, because the hotel attributes that create value depend on the reason a guest is traveling (e.g., for business or for pleasure). Moreover, the value-creating attributes that guests consider in the decision to book a hotel are not necessarily the same attributes that create value during the hotel stay. In particular, guests seem to consider only an outstanding performance as value laden. Only half of the 469 frequent travelers surveyed by a Cornell University study, for example, could recall an instance of outstanding value in their most recent hotel stay. Travelers were able to identify well over 1,000 hotel attributes that help to drive their purchase decision. Fortunately, those attributes can be aggregated. The top attributes driving the guests' purchase decision were: location, brand name and reputation, physical property (exterior, public space), guest-room design, and value for money. Some of the same attributes also created value during the stay. The top five were: guest-room design, physical property (exterior, public space), interpersonal service, functional service, and F&B-related services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".