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<scp>Market Segmentation: The Role of Opaque Travel Agencies</scp>

2008· article· en· W2020170007 on OpenAlex

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 Economics & Management Strategy · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBusinessMarket segmentationPrice discriminationCommitMarketingService (business)Competition (biology)Service providerProfit (economics)AdvertisingProduct (mathematics)OpacityProduct differentiationEconomicsMicroeconomicsComputer scienceCournot competition

Abstract

fetched live from OpenAlex

This paper investigates the role of discount travel agencies such as Priceline and Hotwire in the market segmentation of the hotel and airline industries. These agencies conceal important characteristics of the offered services, such as hotel locations or flight schedules. We explicitly model this opaque feature and show that it enables service providers to price discriminate between those customers who are sensitive to service characteristics and those who are not. Service providers can profit from such discrimination despite the fact that the opaque feature virtually erases product differentiation and thus intensifies competition. The reason is that the intensified competition for less sensitive customers enables service providers to commit to a higher price for more sensitive customers, which leads to higher profits overall. This explains why airlines or hotels are willing to lose the advantage of product differentiation and offer services through discount travel agencies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.587
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.210
Teacher spread0.180 · 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