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Record W3087951375 · doi:10.3390/jrfm13100229

The Impact of Conventions on Hotel Demand: Evidence from Indianapolis Using Daily Hotel Occupancy Data

2020· article· en· W3087951375 on OpenAlexvenueno aff
Colin Steitz, Joshua C. Hall

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsOccupancyRevenueCrowdingBusinessConventionNames of the days of the weekAdvertisingMarketingFinanceEngineeringPolitical sciencePsychologyLaw

Abstract

fetched live from OpenAlex

This paper uses daily hotel occupancy data for the Indianapolis metro area from STR to estimate the effect of multi-day conventions on hotel demand. In addition to multi-day conventions, we hand collect data on other major events such as the Indy 500 and major sporting events. Hotel demand is an important part of the economic activity generated by multi-day events because hotel rooms are largely occupied by out-of-town guests and represent new local economic activity. We look at the effect of conventions and other large events in Indianapolis on average daily room rates, revenue per room, demand, occupancy, and total revenue. We find large and statistically significant effects for multi-day conventions on hotel demand with very little evidence of crowding out. A single day of a multi-day convention brings in approximately $928,000 in additional hotel revenue. Our findings contribute to the literature on the economic impact of large events such as conventions and sporting events that attract out-of-town visitors.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.167
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.099
GPT teacher head0.367
Teacher spread0.268 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of risk and financial management→Same topicSport and Mega-Event Impacts→French-language works237,207→