Online retail platform cooperative promotion strategies with manufacturer competition

Zongsheng Huang; Xuechun Yan

doi:10.3934/jimo.2024067

Article Contents

2024, Volume 20, Issue 12: 3697-3724. Doi: 10.3934/jimo.2024067

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Online retail platform cooperative promotion strategies with manufacturer competition

Zongsheng Huang^, and
Xuechun Yan

School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China

^*Corresponding author: Zongsheng Huang
^*Corresponding author: Zongsheng Huang

Received: October 2023

Revised: March 2024

Early access: May 13, 2024

Published online: May 13, 2024

The first author is supported by National Social Science Fund of China with Grants Nos. 22BGL018.

Abstract / Introduction Full Text(HTML) Figure(2) / Table(5) Related Papers Cited by

Abstract

This study delved into the dynamics of cooperative sales promotion between a retail platform and its manufacturer suppliers. It was observed that retail platforms were increasingly inclined to collaborate with manufacturers on sales promotion initiatives, such as nationwide advertising campaigns, to enhance brand development. Adopting a differential game model, this paper investigated the retail platform's strategic decision regarding participation in cooperative promotions in scenarios ranging from monopoly to duopoly among manufacturers. The findings revealed that the platform's willingness to engage in cooperative promotions was significantly influenced by the commission fee, with the platform opting for such promotions only when the commission fee surpassed a critical threshold. Additionally, the study shed light on how the intensity of upstream competition shapes the platform's stance on cooperative promotions. Notably, the platform adopted a more lenient policy in a low-competition environment and a more stringent one in the face of heightened competition. This study contributed to the existing body of literature by addressing a gap in the understanding of cooperative promotion and advertising dynamics within the context of modern retail platforms and their supply chains, complementing the predominantly studied traditional reselling supply chains.

Keywords:

Mathematics Subject Classification: Primary: 91A23; Secondary: 91-10.

Citation:

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Figure 1. Trajectories of product sales under MN and MC

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Figure 2. Trajectories of product sales under DN and DC with low $ (\beta = 0.5) $ and high competition $ (\beta = 1.5) $

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Table 1. Table of Notations

Notation	Definition
$ A_m(t) $	Manufacturer's advertising effort at time $ t $
$ P_n(t) $	Manufacturer's promotion effort at time $ t $
$ E_b(t) $	Retail platform's advertising effort at time $ t $
$ S $	Cost-sharing rate of retail platform for the manufacturer
$ \gamma $	The commission rate the retail platform charges the manufacturer
$ \lambda_m, \lambda_n $	Impact of manufacturer's national advertising, promotion on the goodwill of manufacturer
$ \lambda_b $	Impact of manufacturer's promotion on the goodwill of retail platform
$ G_m(t), G_b(t) $	Accumulated goodwill of manufacturer, retail platform at time $ t $
$ \delta $	Decay rate of the goodwill
$ Q_n(t) $	Demand for the product at time $ t $
$ \alpha $	Effect of promotion effort on current sale rate
$ \phi_m, \phi_b $	Effect of manufacturer's goodwill, retail platform's goodwill on demand rate
$ r $	Discount rate of manufacturer and retail platform
$ C_m, C_n $	Advertising cost and the promotion cost function of manufacturer
$ C_b $	Advertising cost function of retail platform
$ \mu_m, \mu_n $	Advertising cost and promotion cost coefficient of manufacturer
$ \mu_b $	Advertising cost coefficient of retail platform
$ \pi_m $	Profit margin of manufacturer
$ \beta $	promotion competition between the two duopoly manufacturers
$ J_m, J_b $	Profit functions of manufacturer and retail platform
$ V_m, V_b $	Value functions of manufacturer and retail platform

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Table 2. Sensitivity analysis results in MN scenario

Parameter	$ \gamma $	$ \lambda_m $	$ \lambda_n $	$ \lambda_b $	$ \alpha $	$ \phi_m $	$ \phi_b $
$ A_n^{M N} $	$ \downarrow $	$ \uparrow $	$ = $	$ = $	$ = $	$ \uparrow $	$ = $
$ E_b^{M N} $	$ \uparrow $	$ = $	$ = $	$ = $	$ = $	$ = $	$ \uparrow $
$ P_n^{M N} $	$ \downarrow $	$ = $	$ \downarrow $	$ \uparrow $	$ \uparrow $	$ \downarrow $	$ \uparrow $
Note: $ \uparrow $: increasing; $ \downarrow $: decreasing; =: unchanged.

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Table 3. Sensitivity analysis results in MC scenario

Parameter	$ \gamma $	$ \lambda_m $	$ \lambda_n $	$ \lambda_b $	$ \alpha $	$ \phi_m $	$ \phi_b $
$ A_n^{M C} $	$ \downarrow $	$ \uparrow $	$ = $	$ = $	$ = $	$ \uparrow $	$ = $
$ E_b^{M C} $	$ \uparrow $	$ = $	$ = $	$ = $	$ = $	$ = $	$ \uparrow $
$ P_n^{M C} $	$ \uparrow $	$ = $	$ \downarrow $	$ \uparrow $	$ \uparrow $	$ \downarrow $	$ \uparrow $
$ S^{M C} $	$ \uparrow $	$ = $	$ = $	$ = $	$ = $	$ = $	$ = $
Note: $ \uparrow $: increasing; $ \downarrow $: decreasing; =: unchanged.

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Table 4. Sensitivity analysis results in DN scenario

Parameter	$ \gamma $	$ \beta $	$ \lambda_m $	$ \lambda_n $	$ \lambda_b $	$ \alpha $	$ \phi_m $	$ \phi_b $
$ A_n^{D N} $	$ \downarrow $	$ = $	$ \uparrow $	$ = $	$ = $	$ = $	$ \uparrow $	$ = $
$ E_b^{D N} $	$ \uparrow $	$ = $	$ = $	$ = $	$ = $	$ = $	$ \uparrow $
$ P_{n i}^{D N} $	$ \downarrow $	$ \uparrow $	$ = $	$ \downarrow $	$ \uparrow $	$ \uparrow $	$ = $	$ \downarrow $
Note: $ \uparrow $: increasing; $ \downarrow $: decreasing; =: unchanged.

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Table 5. Sensitivity analysis results in DC scenario

Parameter	$ \gamma $	$ \beta $	$ \lambda_m $	$ \lambda_n $	$ \lambda_b $	$ \alpha $	$ \phi_m $	$ \phi_b $
$ A_n^{D C} $	$ \downarrow $	$ = $	$ \uparrow $	$ = $	$ = $	$ = $	$ \uparrow $	$ = $
$ E_b^{D C} $	$ \uparrow $	$ = $	$ = $	$ = $	$ = $	$ = $	$ = $	$ \uparrow $
$ P_{n i}^{D C} $	$ \uparrow $	$ \uparrow $	$ = $	$ \downarrow $	$ \uparrow $	$ \uparrow $	$ \downarrow $	$ \uparrow $
$ S^D(\beta<\bar{\beta}) $	$ \uparrow $	$ \downarrow $	$ = $	$ \uparrow $	$ \downarrow $	$ \downarrow $	$ \uparrow $	$ \downarrow $
$ S^D\left(\bar{\beta}<\beta<\beta^*\right) $	$ \uparrow $	$ \downarrow $	$ = $	$ \uparrow $	$ \uparrow $	$ \downarrow $	$ \uparrow $	$ \uparrow $
$ S^D\left(\beta>\beta^*\right) $	$ \uparrow $	$ \downarrow $	$ = $	$ \downarrow $	$ \uparrow $	$ \uparrow $	$ \downarrow $	$ \uparrow $
where $ \bar{\beta}=\frac{\lambda_n \phi_m-\alpha(\delta+r)}{2(\delta+r)}, \beta^=\frac{\lambda_b \phi_b}{\delta+r} $. Note: $ \uparrow $ : increasing; $ \downarrow $ : decreasing; $ = $ : unchanged.*

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