Surrogate Buyers in Corporate Buying of Luxury Hotel Rooms
Bibliographic record
Abstract
Hotel industry is a significant stakeholder in the Indian tourism sector. According to Knight Frank research, 2008 Indian hotel industry is currently adding about 42,022 five and four star category rooms in the major cities. Hotel demand has grown much faster than supply, but the need to market the hotels, optimally remains. The persons who handle the travel arrangements for corporate houses are not buying the hotel services for their own personal use. This is the reason why, they can be termed as surrogate buyers. An identification of the how these surrogate buyers contribute to sales of luxury hotels, is what the researchers are trying to establish through this research. A study of a stratified sample of Sales Managers of all the hotels which fall into the luxury category of hotels in the city of Kochi, Kerala is undertaken during the first quarter of 2012, using the tools like a questionnaire and personal interview of Sales managers of these hotels. Thus the observations were arrived at. Hotel managers have to recognize this fact and should try to pamper these surrogate buyers by creation of business relationships. Also we would like to argue that that out of all the room business received from corporate, almost all (up to 95%) are routed through these surrogate buyers and they are definitely a business source. Managing them can surely bring additional business for any luxury hotel.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".