Hotel revenue management for the transient segment: taxonomy-based research
Bibliographic record
Abstract
Purpose This paper aims to (1) organize the open literature on hotel revenue management systems, (2) compare practitioner systems in terms of functionality and (3) integrate (1)-(2) into research stream recommendations for the open literature with an empirical focus. Design/methodology/approach The authors use Nickerson’s taxonomy development method from the field of information systems to build the taxonomy. Findings New forecasting areas include developing a metric for the degree of strategic fit of a hotel’s pricing strategy and using it in conjunction with quantifications of online reviews for predictions. New price optimization avenues include determining whether a lack of congruence between customer perceptions of fairness and trust and pricing history has a detrimental effect on overall hotel performance and determining which combinations of flexible products, decision-maker risk aversion, nonparametric forecasting and reference effect optimization features work best in which situations. Originality/value This is the first study to combine vendor activities outside the technical realms of forecasting and price optimization with an emphasis on the choice modeling technical framework. This study points to several promising studies using qualitative methods, action research and design science.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".