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Record W2189584527 · doi:10.1287/mnsc.2017.2788

Output Decisions and Price Matching: Theory and Experiment

2017· article· en· W2189584527 on OpenAlexaff
Mongoljin Batsaikhan, Norovsambuu Tumennasan

Bibliographic record

VenueManagement Science · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMatching (statistics)EconomicsEconometricsMathematical economicsComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

We study the effects of price matching in a setting in which each firm selects both its price and output, simultaneously. We show that the availability of a price-matching option leads to the Cournot outcome in this setting. Our experimental study confirms this result in the laboratory. Our finding is a stark contrast to the one obtained in the standard price competition that the most likely market price in the presence of a price-matching option is the monopolistic price. In addition, we show that price matching benefits consumers in markets with a large number of firms. If a market has a few firms, then the effects of price matching on consumers depend on the market demand and cost functions. Thus, our study suggests that the effect of price matching depends on the strategic variables of the firms. Data and the online appendix are available at https://doi.org/10.1287/mnsc.2017.2788 . This paper was accepted by Eric Anderson, marketing.

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)0.005
metaresearch head score (Gemma)0.031
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.034
GPT teacher head0.290
Teacher spread0.256 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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