# New Practice of E-Commerce Platform: Evidence from Two Trade-In Programs

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## Abstract

**:**

## 1. Introduction

- We consider VPN programs and compare the implementation effects of VPN and ON programs. We also analyze the impacts of the two different trade-in programs on the pricing decisions of brand owners and e-commerce platforms for two successive-generation products and discuss their preferences for both trade-in programs, then advise brand owners and e-commerce platforms on their choices.
- Based on the two-stage sales of two successive-generation products, we develop Stackelberg game models between a brand owner and a B2C e-commerce platform with both trade-in programs and derive pricing strategies for two-generation products. Product rollover is often common in industry practice, and 3C products are more representative, namely, computer, communications, and consumer electronic products.

## 2. Literature Review

## 3. Problem Description

## 4. The Model

#### 4.1. Product Demand

#### 4.1.1. Demand with ON Program

#### 4.1.2. Demand with VPN Program

#### 4.2. ON Model

#### 4.3. VPN Model

## 5. Results and Analysis

#### 5.1. Model Results

**Lemma**

**1.**

**Lemma**

**2.**

**Lemma**

**3.**

#### 5.2. Analysis of Results

**Proposition**

**1.**

- (1)
- Both ${p}_{1a}^{T*}$ and ${p}_{2a}^{T*}$ are positively correlated with the net revenue brought by recycling of per unit of old products (s value).
- (2)
- Both ${p}_{1a}^{T*}$ and ${p}_{2a}^{T*}$ are negatively correlated with the remaining rate of customer perceived value of the previous-generation products at second-stage sales (α value).
- (3)
- If s > α, ${p}_{1a}^{T*}$ is positively correlated with discount factor (τ value) and improvement rate of consumer-perceived value of new-generation products (r value). If s < α, they are negatively correlated. If s = α, ${p}_{1a}^{T*}=\frac{1+{c}_{1}}{2}$, which is only linearly correlated with the unit production cost of current-generation products (${c}_{1}$ value).
- (4)
- ${p}_{2a}^{T*}$ and τ are not correlated (this means that the discount factor will not affect the agreed price of new-generation products).

**Proposition**

**2.**

- (1)
- ${p}_{1a}^{M*}$ is positively correlated with the net revenue brought by recycling per unit of old products (s value), negatively correlated with discount factor (τ value), and does not correlate with α, β, or r values.
- (2)
- ${p}_{2a}^{M*}$ is negatively correlated with α value, positively correlated with β value, and has no correlation with s, τ values.

**Conclusion**

**1.**

**Proposition**

**3.**

- (1)
- ${p}_{2r}^{T*}$ and ${p}_{t}^{*}$ are positively correlated with s value, while ${p}_{1r}^{T*}$ is not correlated with s value.
- (2)
- ${p}_{2r}^{T*}$ is negatively correlated with α value, ${p}_{t}^{*}$ is positively correlated with α value, while ${p}_{1r}^{T*}$ is not correlated with α value.
- (3)
- If ${c}_{2}<\frac{(1+r)(1+{c}_{1})}{2}$, ${p}_{1r}^{T*}$, ${p}_{2r}^{T*}$, and ${p}_{t}^{*}$ are all positively correlated with τ value. If ${c}_{2}>\frac{(1+r)(1+{c}_{1})}{2}$, ${p}_{1r}^{T*}$, ${p}_{2r}^{T*}$, and ${p}_{t}^{*}$ are all negatively correlated with τ value. If ${c}_{2}=\frac{(1+r)(1+{c}_{1})}{2}$, ${p}_{1r}^{T*}$, ${p}_{2r}^{T*}$, and ${p}_{t}^{*}$ are all not correlated with τ value.

**Proposition**

**4.**

**Conclusion**

**2.**

## 6. Numerical Simulation

#### 6.1. Impacts of Main Parameters on {${p}_{1r}^{M*}$, ${p}_{2r}^{M*}$, ${p}_{m}^{*}$}

#### 6.2. Impacts of Improvement Rate r on Pricing with Two Trade-In Programs

**Conclusion**

**3.**

#### 6.3. Preferences of Two Trade-In Programs

**Conclusion**

**4.**

#### 6.4. The Dominant Scenario of Two Trade-In Programs

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

ON | Old-for-new |

VPN | Value-preserved-for-new |

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**Figure 5.**Impacts of main parameters on the optimal pricing strategy set {${p}_{1r}^{M*}$, ${p}_{2r}^{M*}$, ${p}_{m}^{*}$ }. (

**a**) The impact of s on {${p}_{1r}^{M*}$, ${p}_{2r}^{M*}$, ${p}_{m}^{*}$ }; (

**b**) The impact of α on {${p}_{1r}^{M*}$, ${p}_{2r}^{M*}$, ${p}_{m}^{*}$ }; (

**c**) The impact of τ on {${p}_{1r}^{M*}$, ${p}_{2r}^{M*}$, ${p}_{m}^{*}$ }; (

**d**) The impact of β on {${p}_{1r}^{M*}$, ${p}_{2r}^{M*}$, ${p}_{m}^{*}$ }.

**Figure 6.**Impacts of improvement rate r on the pricing with two trade-in programs. (

**a**) The impact of r on the pricing under ON model; (

**b**) The impact of r on the pricing under VPN model.

**Figure 7.**Impacts of remaining rate α and improvement rate r on optimal profits with two trade-in programs. (

**a**) Impacts on the brand owner’s optimal profit; (

**b**) Impacts on the e-commerce platform’s optimal profit.

Notation | Description |
---|---|

The superscript | |

$i$ | $i=\{T,M\}$, denotes the ON and VPN model, respectively. |

The brand owner’s decision variables | |

${p}_{1a}^{i}({p}_{2a}^{i})$ | The brand owner’s agreement price at first-stage sales (second-stage sales). |

The e-commerce platform’s decision variables | |

${p}_{1r}^{i}({p}_{2r}^{i})$ | The e-commerce platform’s retail price at first-stage sales (second-stage sales). |

${p}_{m}^{}$ | The e-commerce platform’s VPN price at first-stage sales. |

${p}_{t}^{}$ | The e-commerce platform’s rebate price at second-stage sales. |

Parameters | |

${c}_{1}({c}_{2})$ | The brand owner’s unit production cost of current-generation products (new-generation products). |

s | The e-commerce platform’s average net revenue per unit of old products recovered through ON and VPN programs. |

v | $\mathrm{Customer}\mathrm{perceived}\mathrm{value}\mathrm{of}\mathrm{current}\text{-}\mathrm{generation}\mathrm{products}\mathrm{at}\mathrm{first}\text{-}\mathrm{stage}\mathrm{sales},\mathrm{subject}\mathrm{to}\mathrm{a}\mathrm{uniform}\mathrm{distribution},\mathrm{let}v~U(0,1)$. |

r | $\mathrm{Improvement}\mathrm{rate}\mathrm{of}\mathrm{consumer}\text{-}\mathrm{perceived}\mathrm{value}\mathrm{of}\mathrm{new}\text{-}\mathrm{generation}\mathrm{products}\mathrm{compared}\mathrm{to}\mathrm{previous}\text{-}\mathrm{generation}\mathrm{products};\mathrm{that}\mathrm{is},\mathrm{the}\mathrm{customer}\text{-}\mathrm{perceived}\mathrm{value}\mathrm{of}\mathrm{new}\text{-}\mathrm{generation}\mathrm{products}\mathrm{is}v(1+r)$$,\mathrm{where}r0$. |

$\alpha $ | $\mathrm{The}\mathrm{remaining}\mathrm{rate}\mathrm{of}\mathrm{customer}\text{-}\mathrm{perceived}\mathrm{value}\mathrm{of}\mathrm{the}\mathrm{previous}\text{-}\mathrm{generation}\mathrm{products}\mathrm{at}\mathrm{sec}\mathrm{ond}\text{-}\mathrm{stage}\mathrm{sales};\mathrm{that}\mathrm{is},\mathrm{the}\mathrm{initial}\mathrm{consumer}\u2019\mathrm{s}\mathrm{perceived}\mathrm{value}\mathrm{of}\mathrm{the}\mathrm{previous}\text{-}\mathrm{generation}\mathrm{products}\mathrm{at}\mathrm{sec}\mathrm{ond}\text{-}\mathrm{stage}\mathrm{sales}\mathrm{is}$$\alpha v$$,\mathrm{where}\alpha \in (0,1)$$.$$\mathrm{The}\mathrm{higher}\alpha $ value, the more durable the previous-generation products. |

${p}_{o}^{}$ | Recycling the price of old products at the second-hand market during the second-stage sales. Note that, in the ON model, the rebate price set by the e-commerce platform is significantly higher than the recycling price, otherwise the conditions for implementing the ON program are not available. Let p_{o} be given exogenously. |

$\beta $ | $\mathrm{The}\mathrm{value}\text{-}\mathrm{preservation}\mathrm{rate}\mathrm{of}\mathrm{the}\mathrm{brand}\u2019\mathrm{s}\mathrm{previous}\text{-}\mathrm{generation}\mathrm{products},\mathrm{hence},\beta $$\mathrm{can}\mathrm{be}\mathrm{expressed}\mathrm{as}\beta ={p}_{o}^{}/{p}_{1r}^{i}$$,\beta \in (0,1)$. |

$\tau $ | $\mathrm{Discount}\mathrm{factor}\mathrm{of}\mathrm{sec}\mathrm{ond}\text{-}\mathrm{stage}\mathrm{sales},\tau \in (0,1)$. |

Functions | |

${D}_{1}^{T}$ | The demand for current-generation products at first-stage sales. |

${D}_{2o}^{T}$ | The demand of initial consumers to trade-in for new-generation products with an ON program at second-stage sales. |

${D}_{2n}^{i}$ | The demand of new consumers to purchase new-generation products at second-stage sales. |

${D}_{1j}^{M}({D}_{1nj}^{M})$ | Demand for current-generation products with (without) a VPN program at first-stage sales. |

${D}_{2oj}^{M}$ | The demand of the initial consumers who had participated in a VPN program to trade-in for new-generation products at second-stage sales. |

${D}_{2onj}^{M}$ | The demand of the initial consumers who did not participate in a VPN program to purchasing new-generation products again at second-stage sales. |

${\pi}_{B}^{i}$ | The brand owner’s profit. |

${\pi}_{E}^{i}$ | The e-commerce platform’s profit. |

${L}_{E}^{i}$ | $\mathrm{The}\mathrm{Lagrangian}\mathrm{function}\mathrm{of}{\pi}_{E}^{i}$. |

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## Share and Cite

**MDPI and ACS Style**

Hu, Q.; Lou, T.; Li, J.; Zuo, W.; Chen, X.; Ma, L.
New Practice of E-Commerce Platform: Evidence from Two Trade-In Programs. *J. Theor. Appl. Electron. Commer. Res.* **2022**, *17*, 875-892.
https://doi.org/10.3390/jtaer17030045

**AMA Style**

Hu Q, Lou T, Li J, Zuo W, Chen X, Ma L.
New Practice of E-Commerce Platform: Evidence from Two Trade-In Programs. *Journal of Theoretical and Applied Electronic Commerce Research*. 2022; 17(3):875-892.
https://doi.org/10.3390/jtaer17030045

**Chicago/Turabian Style**

Hu, Qiang, Tingyuan Lou, Jicai Li, Wenjin Zuo, Xihui Chen, and Lindong Ma.
2022. "New Practice of E-Commerce Platform: Evidence from Two Trade-In Programs" *Journal of Theoretical and Applied Electronic Commerce Research* 17, no. 3: 875-892.
https://doi.org/10.3390/jtaer17030045