A game theoretic approach for pricing and advertising of an integrated product family in a duopoly
Soroush Safarzadeh ()
Additional contact information
Soroush Safarzadeh: Quchan University of Technology
Journal of Combinatorial Optimization, 2023, vol. 45, issue 5, No 3, 26 pages
Abstract:
Abstract Product family concept predicates to a products’ group or services that derive from a common base and they have usually the same production process and physical features, in different industries. In this paper, we assume two manufacturers that produce some homogeneous product families that they want to decide upon their wholesale price and national advertising expenditure, as a new problem in the multi-agent environment. According to this problem, we propose the non-cooperative, cooperative and two-stage game models to maximize manufacturers' profits and then, based on the optimization approaches i.e., Nash and Stackelberg methods, we obtain the appropriate equilibrium strategies in a special example. Also, we propose a new profit-sharing approach for the Stackelberg model of the defined problem and discuss the solutions as some propositions and marginal points, based on the numerical studies. The results show that the centralized model leads to better profit than the non-centralized and Stackelberg models.
Keywords: Pricing; Advertising; Supply chain coordination; Product family; Game theory (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10878-023-01041-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:jcomop:v:45:y:2023:i:5:d:10.1007_s10878-023-01041-6
Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878
DOI: 10.1007/s10878-023-01041-6
Access Statistics for this article
Journal of Combinatorial Optimization is currently edited by Thai, My T.
More articles in Journal of Combinatorial Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().