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Record W2076951281 · doi:10.5367/000000009788254368

Room Rates as Signals of Quality, Sell-Out Risk and the Prospects of Getting a Better Deal: Analytical Model and Empirical Evidence

2009· article· en· W2076951281 on OpenAlexaff
Chih‐Chien Chen, Jane E. Ruseski, Zvi Schwartz

Bibliographic record

VenueTourism Economics · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsQuality (philosophy)Context (archaeology)ImperfectRevenuePerfect informationEconomicsPerceptionMarketingBusinessEmpirical evidenceRevenue managementRisk perceptionEmpirical researchMicroeconomicsEconometricsFinance

Abstract

fetched live from OpenAlex

Travellers make advanced booking decisions in an imperfect information environment, an environment in which price signalling is likely to occur. This study examines the unique informational role of room rates by suggesting an analytical model of room rates as a signal of quality and sell-out risk and by testing the theory empirically. The findings indicate that, even in advanced booking situations in which consumers might associate price deviations with the hotel's revenue management policies, prices can still signal quality to the consumers. Moreover, the study demonstrates that in a deal seeking/advanced booking context, there are two additional opposing impacts of the informational role of prices. Customers' propensity to book increases with higher rates because the perception of the sell-out risk is higher. However, at the same time, customers' propensity to book decreases because the higher room rate induces a higher expectation of the offer of a better deal.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0050.004
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.081
GPT teacher head0.328
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2009
Admission routes1
Has abstractyes

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