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Record W2621181916 · doi:10.1111/poms.12936

Resale Price Maintenance with Strategic Customers

2018· article· en· W2621181916 on OpenAlexafffund
Андрей Бажанов, Yuri Levin, Mikhail Nediak

Bibliographic record

VenueProduction and Operations Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupply chainBusinessProfit (economics)Industrial organizationTariffResale price maintenanceMicroeconomicsProduct (mathematics)WelfareMarketingCommerceEconomics

Abstract

fetched live from OpenAlex

We consider a decentralized supply chain (DSC) under resale price maintenance (RPM) selling a limited‐lifetime product to forward‐looking customers with heterogeneous valuations. When customers do not know the inventory level, double marginalization under RPM leads to a higher profit and aggregate welfare than without RPM under a two‐part tariff contract (TT). Both RPM and TT profits are higher and aggregate welfare is lower than in a centralized supply chain (CSC). When customers know the inventory, RPM coincides with CSC. Thus, overestimation of customer awareness may lead to overcentralization of supply chains with profit loss comparable with the loss from strategic customers. The case of RPM with unknown inventory is extended to an arbitrary number of retailers with inventory‐independent and inventory‐dependent demand. In both cases, the manufacturer, by setting a higher wholesale price, mitigates the inventory‐increasing effect of competition and reaches the same profit as with a single retailer. The high viability and efficiency of RPM in using double marginalization as a strategic‐behavior‐mitigating tool may serve as another explanation of why manufacturers may prefer DSC with RPM to a vertically integrated firm.

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 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.222
Teacher spread0.201 · 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 teacher head, 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

Citations6
Published2018
Admission routes2
Has abstractyes

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