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Record W3033194040 · doi:10.1111/itor.12835

Multichannel retailing and price competition

2020· article· en· W3033194040 on OpenAlexafffund
Salma Karray, Simon Pierre Sigué

Bibliographic record

VenueInternational Transactions in Operational Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsAthabasca UniversityOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBusinessProfitability indexProfit (economics)DilemmaCompetition (biology)Online and offlineIndustrial organizationPosition (finance)Context (archaeology)MicroeconomicsProduct (mathematics)First-mover advantageMarketingCommerceEconomicsComputer scienceFinance

Abstract

fetched live from OpenAlex

Abstract We investigate the profitability of multichannel retailing for competing offline retailers. Each firm can sell a product both offline and online in a position of a first mover or follower in the online market. We find that, depending on the online market size and price competition levels across channels and retailers, the adoption of multichannel retailing may or may not enhance an offline retailer's profits in the first‐mover position. If one retailer profitably expands online, the second can also improve its profit by introducing another online channel to the detriment of the pioneer. However, when offline retailers are given the possibility of selecting an equilibrium channel mix, multichannel retailing could be adopted strategically by the two retailers to maintain their market shares, resulting in a prisoner's dilemma situation. In such a context, it drives down retail prices, increases sales, and reduces the profits of the two competing retailers.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.118
GPT teacher head0.359
Teacher spread0.241 · 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 designNot applicable
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

Citations16
Published2020
Admission routes2
Has abstractyes

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