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Record W4382984902 · doi:10.1111/joie.12330

Industry Structure, Segmentation, and Quality Competition in the U.S. Hotel Industry*

2023· article· en· W4382984902 on OpenAlex
R. Andrew Butters, Thomas N. Hubbard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Industrial Economics · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsCompetition (biology)BusinessDestinationsMarket segmentationMarket sizeQuality (philosophy)MarketingHotel industryProduct differentiationIndustrial organizationProduct (mathematics)Market competitionEconomies of scaleMarket structureScale (ratio)Hospitality industryTourismCommerceEconomicsMicroeconomicsMarket economyGeography

Abstract

fetched live from OpenAlex

We examine how quality competition affects the relationship between market size and industry structure at the product level using evidence from the U.S. hotel industry. Starting in the early 1980s, quality competition for business travelers became more based on variable and less on fixed costs, and became less scale intensive. Since then, market size increases have been met by more, but smaller, hotels in business travel destinations but continued to be met by larger hotels in personal travel destinations. Our results illustrate how the way consumers benefit from increases in market size depends on how firms compete.

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.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.038
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.083
GPT teacher head0.290
Teacher spread0.207 · 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