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Record W4402678502 · doi:10.1080/03155986.2024.2405433

Trilateral game and vertical collaboration models in a two-stage green supply chain with substitutable green products

2024· article· en· W4402678502 on OpenAlexvenueno aff
Shivendra Kumar Gupta, Vinod Kumar Mishra

Bibliographic record

VenueINFOR Information Systems and Operational Research · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsnot available
Fundersnot available
KeywordsSupply chainStage (stratigraphy)BusinessMarketingGeology

Abstract

fetched live from OpenAlex

This paper explores the trilateral game and vertical collaboration model based on Stackelberg’s (manufacturer-leadership game) and Bertrand’s game-theoretical methodology for a two-stage green supply chain where the duopolistic manufacturers and the retailer are ecologically conscious. Both manufacturers produce and sell two substitutable green products through a common retailer. The selling prices and green levels (GLs) determine the demand for both green products. A game theoretical approach is implemented in the trilateral game model, and the result shows that the manufacturer with a bigger sales volume achieves superior performance in terms of earnings. In the vertical collaboration model, a manufacturer and retailer collaborate to optimize pricing issues, GLs, and profits. Two-player games among three participants are performed for this collaboration. The results of the vertical collaboration model show that the overall profit in vertical collaboration is greater than the sum of the individual profits corresponding to two participants in the trilateral game model. Whereas, the manufacturer outside the collaboration experiences a decline in profits. Further, a selection criterion of manufacturers is also developed to maximize the overall profit of the retailer. Finally, a numerical example and a sensitivity analysis are performed to demonstrate the model’s implementation and stability.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.010
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 head0.051
GPT teacher head0.307
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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations2
Published2024
Admission routes1
Has abstractyes

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