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Record W4409592113 · doi:10.1111/ijcs.70069

The Effects of Restaurant Cancellation Policies and No‐Show Awareness Campaigns on Reservation Behaviors

2025· article· en· W4409592113 on OpenAlexaff
Esther L. Kim, Jason Tang, Minji Kim

Bibliographic record

VenueInternational Journal of Consumer Studies · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsMount Royal University
FundersMontclair State University
KeywordsReservationBusinessAdvertisingMarketingPsychologyComputer scienceComputer network

Abstract

fetched live from OpenAlex

ABSTRACT This study addresses the persisting issue of restaurant reservation no‐shows by applying the availability heuristic principle to analyze booking behaviors. Three experiments identified cancellation policy features and factors influencing booking and no‐show behaviors. Findings confirmed that a cancellation barrier (i.e., strict policy and complex cancellation method) decreases no‐show intentions and negatively affects booking intentions. This research suggests that a lenient cancellation policy, an easy cancellation method, and an awareness campaign can encourage positive guest booking behavior and reduce no‐shows. Applying the availability heuristic principle to restaurant management, the current research enhances the understanding of psychological factors affecting guest decisions and identifies effective strategies to reduce no‐shows while supporting favorable booking behavior.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.336
Teacher spread0.309 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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