Hotel yield management using different reservation modes
Bibliographic record
Abstract
Purpose This paper explores the implementation of yield management using different reservation modes at a global hotel network (referred to as the “Group”). Design/methodology/approach The Group operates close to half‐a‐million rooms in about 4,000 hotels world‐wide. Following an overview of yield management in hotel industry, the two reservation modes used in the Group are presented. The performance of Group's online and off‐line reservation modes globally over a two‐year period is then discussed in terms of three yield management performance measures, namely: average price (AP), occupancy rate, and average revenue per available room. Findings The findings indicate that the online mode outperforms the off‐line mode with respect to performance measures of AP and average revenue. Further to a global‐based comparison, a localized evaluation of these two modes is also presented for two sub‐groups of hotels clustered in a given region. Statistical analysis of findings is provided, pointing to a substantial revenue increase for the hotel sub‐group that switched from the off‐line to the online reservation mode, compared with the hotel sub‐group that continued to operate off‐line. The paper concludes with a brief discussion on the strengths, weaknesses, opportunities and threats associated with the online reservation mode. Research limitations/implications Future research could look into the impact of specific macro and micro economic conditions on the three yield management performance measures defined. Originality/value The research reported is of value to hotel executives who want to pursue online reservations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".