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Record W2136726930 · doi:10.1142/s0219198901000282

STACKELBERG LEADERSHIP IN A MARKETING CHANNEL

2001· article· en· W2136726930 on OpenAlex
Steffen Jørgensen, Simon-Pierre Sigué, Georges Zaccour

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Game Theory Review · 2001
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsGroup for Research in Decision AnalysisHEC Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStackelberg competitionInefficiencyChannel (broadcasting)Margin (machine learning)MicroeconomicsMarketing channelEconomicsBusinessAdvertisingComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

This paper provides an answer to the question who should, if any, lead a marketing channel? We consider a channel consisting of one manufacturer and one retailer where each player controls his advertising rate and margin. Supposing that advertising has a carry over effect on demand, we adopt a dynamic model. Nash and Stackelberg equilibria are characterised and outcomes compared with an efficient coordinated solution. Our findings suggest that manufacturer's leadership reduces inefficiency in a channel and is more beneficial to the consumer.

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)0.003
metaresearch head score (Gemma)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.111
GPT teacher head0.289
Teacher spread0.178 · 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