Dual-Channel Supply Chain Coordination Considering Green and Service Inputs

Abstract

The rise of the green economy and the dual-channel model has led to consumer preferences for a model that is both green and service-based. At the same time, customer service expectations have led to greater uncertainties in corporate decision-making. However, many research gaps remain in terms of how green and service-based models work together in a dual-channel supply chain to influence operational decisions and achieve efficiency improvements. Therefore, while considering customer expectations, this study adopts Stackelberg game theory to construct a dual-channel supply chain analysis that considers green and service inputs and analyzes the optimal decisions of manufacturers and retailers. The results show that when the costs of green inputs are low, this increases the greenness and prices of green products while also stimulating the retailer to improve service levels and common product prices. When the retailer’s service costs are low, this promotes higher service levels and product prices but inhibits product greenness and green product prices in the online channel. In addition, centralized decision-making is associated with higher product greenness, which is beneficial from an environmental perspective. Numerical analysis further reveals that profit-sharing contracts can be effective in achieving supply chain coordination. These findings have reference significance for the coexistence and interaction of green and service-based factors in dual-channel supply chains, as well as provide a reference value for the impact of customer service expectations on supply chain-related decision-making.

1. Introduction

As environmental pollution, resource depletion, and the so-called greenhouse effect have become increasingly serious concerns, governments and societies have come to recognize the need to invest in low-carbon and green energy to alleviate the crisis, along with consumers who are becoming more aware of green consumption [1]. Green inputs have been shown to improve corporate competitiveness; for example, Pepsi saved USD 44 million by reusing cold-beverage bottles and USD 196 million by using reusable plastic shipping containers. Therefore, many companies have established green supply chains to improve their resource efficiency and green performance while simultaneously reducing their environmental impacts [2]. Additionally, due to consumers’ growing preference for green consumption, manufacturers now produce green and generic products to meet consumers’ diverse needs. This complex market environment provides new challenges for business decision-making.

Amid the rise of dual-channel and new-retail models, manufacturing companies often choose to sell green products online and common products offline [3], with green inputs becoming a major factor in the operation of the dual-channel model. The other major influencing factor is service. To increase market share, some retailers (e.g., 7-Eleven) provide convenience services to customers offline, and this asymmetry in the provision of services, wherein only the retailers provide value-added services, can increase the retailer’s competitive advantage compared to the manufacturer’s direct channel. For customers, the dual-channel model allows for better differentiation due to the higher degree of shopping choices and enhanced service experiences. As a result, consumers now prefer green and service-based approaches.

Both the green and service-based models are critical to increasing profits. Studies have been conducted on single-channel green supply chains, specifically on supply chain green innovations [4], supply chain coordination [5], and performance management [6]. With the rise of the new-retail model, the single-channel model can no longer satisfy consumers’ shopping needs. Thus, scholars have investigated delivery channels under the scenarios of demand change [7], customer preferences [8], and government subsidies [9], especially in the context of dual-channel supply chain decision problems. The above studies all explore the green management of enterprises or the analysis of supply chains from a green perspective.

Although enterprises sell green products through online and offline channels, they often fail to consider customers’ service preferences. When a company provides a service, customers have expectations of it, and when the level of service does not meet customer expectations, customers will feel dissatisfied, thus causing losses within the system. Thus, companies need to make rational decisions to reduce the costs associated with services while providing the appropriate level of service whenever possible. In this regard, some studies have considered the simultaneous impacts of green and service-based models on supply chains. Scholars have also investigated the production, design, and practice of green services through case studies [10], in which service quality has become a decisive point of concern for firms when services are combined with green services and where the combination of green and service-based innovations facilitates good supply chain performance [11,12]. The above studies also examine the contribution to performance when green and service-based approaches are combined but do not consider the competition between the two channels and the possible impact of customer expectations when providing services from a supply chain perspective. The current study suggests that customer service preferences have become as important as green preferences in influencing supply chain-related decisions.

Due to the low-cost advantage of the online channel, manufacturers can invest in extra green costs to produce green products and then sell them through online channel. As for retailers, they can take advantage of the opportunity for service provision to provide attractive services to promote the market demand for common products. When manufacturers sell green products online and retailers provide services offline, there is an interaction between green and service-based inputs due to dual-channel conflict. Therefore, in the present study, we aim to explore the following questions: (1) How do customer green preferences and service preferences affect decision-making and profits? (2) How is the supply chain coordinated? (3) How do customer service expectations affect equilibrium decisions in a dual-channel supply chain? and (4) What is the optimal green input for manufacturers and the optimal service level for retailers in a dual-channel supply chain?

In this study, a dual-channel model is developed in which a manufacturer sells green products and a retailer sells common products and provides value-added services. Mechanisms of price decision, green-input decision, and service-input decision in both centralized and decentralized decision-making are proposed, considering the service and green preferences under the logic of customer service expectations. This work has two potential contributions to the literature. First, this paper establishes a dual-channel supply chain that considers both green and service-based preferences. Most of the literature consists of studies that consider the model of companies providing green products and services from the perspective of case studies. These works typically do not consider the competition between the two channels and the interaction between green and service-based models when they coexist. Second, focusing on the influence of customer service expectations on dual-channel decision-making, this work establishes a centralized decision-making, decentralized decision-making, and coordination model in which green and service-based approaches serve as the decision variables. There is a lack of research on the influence of customer expectations on corporate decisions and the interactions between corporate decisions in the supply chain environment. It is important to address this gap because an in-depth understanding of the abovementioned laws is essential for companies to improve their profitability and customers’ experiences. In this regard, the present study introduces customer expectations into the supply chain and analyzes firms’ decision-making patterns when customer expectations change, taking into account the service quality cost factor.

The remainder of this paper is organized into sections. The relevant literature is analyzed in Section 2, and the model design and symbol descriptions are presented in Section 3. We calculate the model and derive the equilibrium results in Section 4 and perform numerical analysis in Section 5. Finally, we analyze the conclusions and outlook in Section 6.

2. Literature Review

2.1. Green Supply Chain

Current studies have pointed out that low carbon, renewable energy, and green input have become urgent issues [13,14]. Among them, green input, green innovation, wholesale prices, and product prices have important impacts on green supply chain decisions [15,16]. Zheng and Xu [17] studied the impact of government green subsidies and carbon tax policies on the decision-making dynamics of green investments made by manufacturers and recyclers in the context of waste power battery recycling. Wang and Song [18] investigated the green input strategies of manufacturers when producing both green and normal products amid uncertain demands. Similarly, Rahmani and Yavari [7] studied the greenness of manufacturers’ product and pricing decisions in the context of demand perturbations. Li et al. [19] found that government subsidies can incentivize green input from supply chain members and improve social welfare when considering consumer utility. Ma et al. [20] studied product pricing and green input decisions in a competitive situation between two manufacturers and designed a coordination contract. Liu et al. [21] explored the impact of manufacturer competition and retailer competition on the profitability of supply chain firms. While the abovementioned studies examined green supply chain decision-making, they did not consider dual-channel conflicts.

2.2. Dual-Channel Supply Chain

Given that manufacturers have certain cost advantages after opening online channels, and retailers may also benefit from wholesale discounts, dual channels have become the main supply chain mode [22,23]. The dual-channel decision mainly includes pricing, green input, and service input strategies [16,24,25]. In studies investigating the factors influencing decision-making, scholars have focused on risk aversion, free-rider behavior, and information sharing [8,26,27]. Saha et al. [28] considered delivery time sensitivity and proposed revenue and distribution cost-sharing contracts to achieve supply chain coordination. Li et al. [29] studied return policies under direct channel, indirect channel, dual-channel, and no refunds for both channels. Matsui [30] examined the bargaining strategies of retailers and showed that accepting a manufacturer’s wholesale price was necessary to gain more revenue. Yang et al. [31] argued that manufacturers should decide whether to open a review channel based on review information. The abovementioned studies have explored multifaceted decision-making behavior in supply chains. Importantly, we aim to explore the conflict between manufacturers’ green input and retailers’ service input.

2.3. Service Input and Customer Expectations

When a firm provides a service to its customers, such a service can act as a direct incentive for product sales, which, in turn, can affect service demand. When demand is uncertain, retailers’ product prices and service levels become inversely proportional to their risk sensitivity [32,33]. Tsay and Agrawal [34] first introduced retail services into the supply chain and developed a price competition and service competition model. Dumrongsiri et al. [35] showed that retailers’ improving service quality may increase manufacturers’ profits, while Liu et al. [36] demonstrated that service cooperation between manufacturers and retailers can effectively increase supply chain profits. In addition, considering the degree to which customers perceive the service (i.e., customer satisfaction with the level of service and expectations can have a significant impact on consumer behavior), a service can not only directly influence product demand but can also indirectly influence product demand by affecting consumer behavior. Parasuraman et al. [37] and Chen [38] pointed out the existence of a customer tolerance interval, arguing that customers’ expectations of service can be divided into two levels: consensual expectations and ideal expectations. Lee et al. [39] concluded that online comments can have an impact on customer expectations and alter the sales of services. Hsieh and Yuan [40] studied the impact of customer expectations on the service experience and found that effective management can help businesses to achieve maximum value. The abovementioned studies focused on the intrinsic generation mechanism of service expectations and the influence of service expectations on customers’ shopping behavior, with relevant findings revealing the decision-making behavior of individual service providers. However, we aim to explore the influence of customer service expectations on each participating firm’s decisions in the supply chain environment, as well as the interaction pattern between firms’ decisions.

2.4. Literature Comments

In summary, the abovementioned studies indicate that scholars have conducted valuable research in the fields of green supply chains, dual-channel supply chains, service input, and customer expectations. Some scholars have incorporated green or service inputs as influencing factors into the research scope of supply chain management and analyzed their value in improving supply chain performance through case studies and empirical research methods. However, of these, only a few scholars have considered the conflict between the green input of manufacturers and the service input of retailers in dual-channel supply chains. To date, research on how customer service expectations affect the decision-making interactions of each participating enterprise in the supply chain environment remains lacking. In addition, few scholars have analyzed the impact of the coexistence of green and service inputs on supply chain decision-making through mathematical models, as well as how customer service expectations affect the equilibrium decision-making of dual-channel supply chains. A comparison between the existing literature and our work is shown in

Table 1. Location of our work.

	Green	Dual Channel	Service Input	Customer Expectations
Assumpção et al. (2022) [4]	✓
Rahmani and Yavari (2019) [7]	✓	✓
Haiyun et al. (2021) [15]	✓	✓
Wang and Song (2020) [18]	✓	✓
Peng et al. (2022) [14]	✓	✓
Chen et al. (2021) [10]	✓		✓
Hoang et al. (2021) [11]	✓		✓
Lin and Chen (2017) [12]	✓		✓
Tsay et al. (2000) [34]		✓	✓
Dumrongsiri et al. (2008) [35]		✓	✓
Liu et al. (2020) [36]		✓	✓
Lee et al. (2020) [39]				✓
Hsieh and Yuan (2021) [40]				✓
Our work	✓	✓	✓	✓

.

3. Model Design and Symbol Description

3.1. Model Design

The dual-channel model in this paper includes a manufacturer and a retailer. Here, the manufacturer produces a green product with greenness $g$ at the cost $c_1$ and a normal product at the cost $c_2$. The manufacturer then sells the green product through an online channel at the price $p_1$ and the common wholesale product to retailers at the price $w$, while the retailers sell them at the price $p_2$ [41]. The retailer provides the service at the service level $s$ (Figure 1). In particular, $M$ denotes the manufacturer, and $R$ denotes the retailer. It is assumed that consumers in the market have a green preference, which, in turn, affects consumers’ acceptance of the green product being sold. The symbols are given in

Table 2. Symbols.

Symbols	Description
$q$	Total potential demand
$\lambda$	Manufacturer market share as a percentage of potential demand
$\alpha$	Sensitivity coefficient of own price to demand
$\beta$	Cross-price coefficient between online and offline channels
$t$	Sensitivity coefficient of services to demand
$\mu$	Consumers’ green preference coefficient
$\phi$	Green input cost coefficient for the manufacturer
$w$	Manufacturer’s wholesale prices
$p_1$	Manufacturer’s green product prices
$p_2$	Retailer’s common product prices
$c_1$	Production cost per unit of green product
$c_2$	Production cost per unit of common product
$g$	Greenness of the manufacturer’s green product
$s_c$	Minimum level of service that customers expect from the retailer
$s_r$	$\mathrm{Amount} \mathrm{of} \mathrm{service} \mathrm{decision} \mathrm{bias} \mathrm{the} \mathrm{retailer} \mathrm{faces} \mathrm{with} s_c$
$\eta$	Service input cost coefficient
$\tau$	Service quality cost coefficient

.

Considering that market demand is inversely proportional to product price and positively proportional to product greenness and service level, the market demand of supply chain members is expressed as follows:

$$d_1=\lambda q-\alpha p_1+\beta p_2-ts+\mu g,$$

(1)

$$d_2=(1-\lambda )q-\alpha p_2+\beta p_1+ts+(1-\mu )g,$$

(2)

where $q (q>0)$ is the total potential demand, $\lambda (0<\lambda <1)$ is the manufacturer’s market share as a percentage of potential market demand, $\alpha (\alpha >0)$ is the coefficient of sensitivity of the manufacturer’s own price to demand, $\beta (0<\beta <\alpha )$ is the cross-price sensitivity coefficient between the online channel and the retail channel, $t (t>0)$ is the sensitivity coefficient of the service to demand, and $\mu (0<\mu <1)$ is the consumer green preference coefficient.

3.2. Basic Assumptions

Hypothesis 1. 

The manufacturer needs to invest additional costs in producing a green product. It is assumed that the additional cost for the manufacturer to produce such a product is calculated using the following equation: $c\left(g\right)=\phi g^2/2$ [7,18].

Hypothesis 2. 

Let c(s) be the retailer’s cost associated with the service level. Referring to a past study [32], we assume that $c\left(s\right)=\eta s^2/2$, where $\eta$ is the service input cost coefficient.

Hypothesis 3. 

Let $s_c$ and $s_r$ be the customer’s minimum expected service level and the amount of the retailer’s service deviation in the face of $s_c$, respectively. Additional service quality costs are incurred when $s_r<0$. The loss cost function $c_r$ is used to portray the service quality costs, where $c_r=\tau {\left[min\left(s-s_c,0\right)\right]}^2/2=\tau {\left[\mathit{min}\left(s_r,0\right)\right]}^2/2$.

Hypothesis 4. 

For the sake of convenience and without affecting the results, assume that the production cost of ordinary product $c_2=0$, then $c_1=c>0$.

Based on the above assumptions, we have established profit functions for each member of the supply chain, where the manufacturer’s profit function is expressed as follows:

$${\pi }_1=\left(p_1-c\right)\left(\lambda q-p_1+\beta p_2-ts+\mu g\right)+w\left[\left(1-\lambda \right)q-p_2+\beta p_1+ts+\left(1-\mu \right)g\right]-\phi g^2/2.$$

(3)

The profit function of retailers is given by

$${\pi }_2=\left(p_2-w\right)\left[\left(1-\lambda \right)q-p_2+\beta p_1+ts+\left(1-\mu \right)g\right]-\eta s^2/2-\tau {\left[\min \left(s_r,0\right)\right]}^2/2,$$

(4)

where $q (q>0)$ is the total potential demand, $\lambda (0<\lambda <1)$ is the manufacturer’s market share as a percentage of potential market demand, $\alpha (\alpha >0)$ is the coefficient of sensitivity of the manufacturer’s own price to demand, $\beta (0<\beta <\alpha )$ is the cross-price sensitivity coefficient between the online and retail channels, $t (t>0)$ is the sensitivity coefficient of the service to demand, and $\mu (0<\mu <1)$ is the consumer’s green preference coefficient.

4. Model Calculation and Result Analysis

4.1. Centralized Decision-Making

There is no leader in centralized decision-making. In particular, $M$ makes the decisions on $p_1$ and $g$, and $R$ makes the decisions on $p_2$ and $s$. By adding (2) and (3), we have the following:

$$\begin{array}{cc}\hfill {\pi }_c={\pi }_1+{\pi }_2=& \left(p_1-c\right)\left(\lambda q-p_1+\beta p_2-ts+\mu g\right)+p_2\left[\left(1-\lambda \right)q-p_2+\beta p_1+ts+\left(1-\mu \right)g\right]\hfill \\ \hfill & -\phi g^2/2-\eta s^2/2-\tau {\left[\min \left(s_r,0\right)\right]}^2/2\hfill \end{array},$$

(5)

The retailer’s service decisions meet the following conditions:

$$s_r=\left\{\begin{array}{l}\left[t{p}_2-t\left(p_1-c\right)\right]/\eta ,if s_c\le \left[t{p}_2-t\left(p_1-c\right)\right]\\ \left[t{p}_2-t\left(p_1-c\right)-\eta s_c\right]/\left(\eta +\tau \right),if s_c>\left[t{p}_2-t\left(p_1-c\right)\right]\end{array}\right.,$$

(6)

When $s_c\le \left[t{p}_2-t\left(p_1-c\right)\right]/\eta$, the optimal decision in centralized decision-making is defined as $p_1^{c1}$,$p_2^{c1}$,$s^{c1}$, and $g^{c1}$. When $s_c>\left[t{p}_2-t\left(p_1-c\right)\right]/\eta$, additional service quality costs are incurred due to the impact of customer expectations. The optimal decision in centralized decision-making is defined as $p_1^{c2}$, $p_2^{c2}$, $s^{c2}$, and $g^{c2}$.

4.1.1. Without Service Quality Cost

When $s_c\le \left[t{p}_2-t\left(p_1-c\right)\right]/\eta$, $s_r\ge 0$ when no additional service quality cost is incurred.

Proposition 1. 

When $4\left[\eta \phi \left(\alpha +\beta \right)-\eta \mu \left(1-\mu \right)-\phi t^2\right]\left(\alpha -\beta \right)>2\eta \alpha -t^2$ and $4\left[\mu \left(1-\mu \right)+\phi \left(\alpha +\beta \right)\right]\left(\alpha -\beta \right)>2\alpha$, the total profit ${\pi }^c$ is a joint concave function of the product price$s p_1$ and  $p_2$, the service level $s$, and the product greenness $g$. In centralized decision-making, the optimal decision for  $M$ and  $R$ is expressed as follows:

$$p_1^{c1}={\displaystyle \frac{\begin{array}{l}q\left(\eta {\mu }^2+\phi t^2\right)-c{t}^2-\eta \left(\mu q-\alpha c+2\beta \phi q-\beta c\mu \right)+\eta \lambda q\left(1-\mu \right)\\ -2c\eta \phi \left({\alpha }^2-{\beta }^2\right)-\left[2c\eta \mu +2\eta \lambda \phi q-3c\left(\phi t^2+\eta {\mu }^2\right)\right]\left(\alpha -\beta \right)\end{array}}{4\left[\phi t^2-\eta \phi \left(\alpha +\beta \right)-\eta \mu \left(1-\mu \right)\right]\left(\alpha -\beta \right)+2\eta \alpha -t^2}},$$

(7)

$$p_2^{c1}={\displaystyle \frac{q\left(\eta {\mu }^2+\phi t^2\right)+\eta \left(\alpha c\mu -2\alpha \phi q-\lambda \mu q\right)+\left(2\eta \lambda \phi q-c\eta {\mu }^2-c\phi t^2\right)\left(\alpha -\beta \right)}{4\left[\phi t^2-\eta \phi \left(\alpha +\beta \right)-\eta \mu \left(1-\mu \right)\right]\left(\alpha -\beta \right)+2\eta \alpha -t^2}},$$

(8)

$$g^{c1}={\displaystyle \frac{\left[2c\eta \mu \left(\alpha +\beta \right)+2\eta q\left(\lambda +\mu \right)-4\eta \lambda \mu q-c{t}^2\right]\left(\alpha -\beta \right)+q{t}^2-2\alpha \eta q}{4\left[\phi t^2-\eta \phi \left(\alpha +\beta \right)-\eta \mu \left(1-\mu \right)\right]\left(\alpha -\beta \right)+2\eta \alpha -t^2}},$$

(9)

$$s^{c1}={\displaystyle \frac{t\left[\alpha c-\lambda q+\mu q+\left(4\lambda \phi q-c\mu -2\phi q-2c\phi \left(\alpha +\beta \right)\right)\left(\alpha -\beta \right)\right]}{4\left[\phi t^2-\eta \phi \left(\alpha +\beta \right)-\eta \mu \left(1-\mu \right)\right]\left(\alpha -\beta \right)+2\eta \alpha -t^2}}.$$

(10)

4.1.2. With Service Quality Cost

When $s_c>\left[t{p}_2-t\left(p_1-c\right)\right]/\eta$, $s_r<0$ and additional service quality costs will be incurred at this point.

Proposition 2. 

When $4\left[\left(\phi \left(\alpha +\beta \right)+\mu \left(1-\mu \right)\right)\left(\tau +\eta \right)-\phi t^2\right]\left(\alpha -\beta \right)>2\alpha \left(\tau +\eta \right)-t^2$ and $4\left[\mu \left(1-\mu \right)+\phi \left(\alpha +\beta \right)\right]\left(\alpha -\beta \right)>2\alpha$, the total profit ${\pi }^c$ is a joint concave function of the product prices $p_1$ and $p_2$, the service level $s$, and the product greenness $g$. In centralized decision-making, the optimal decision for $M$ and $R$ is given by

$$p_1^{c2}={\displaystyle \frac{\begin{array}{l}\left(\eta +\tau \right)\left(\mu q-\alpha c+2\beta \phi q-\beta c\mu \right)-c{t}^2-q\left[\left(\eta +\tau \right){\mu }^2-\phi t^2\right]-t\tau s_c\left(1-\mu \right)+\lambda q\left(\eta +\tau \right)\left(1-\mu \right)\\ -2c\phi \left(\eta +\tau \right)\left({\alpha }^2-{\beta }^2\right)-\left[2\left(c\mu +\lambda \phi q\right)\left(\eta +\tau \right)-3c\left(\phi t^2+\eta {\mu }^2\right)-3\alpha c{\mu }^2-2\phi t\tau s_c\right]\left(\alpha -\beta \right)\end{array}}{4\left[\left(\phi t^2-\left(\mu \left(1-\mu \right)+\phi \left(\alpha +\beta \right)\right)\left(\tau +\eta \right)\right)\left(\alpha -\beta \right)\right]+2\alpha \left(\eta +\tau \right)-t^2}},$$

(11)

$$p_2^{c2}={\displaystyle \frac{\begin{array}{l}q\left[\left(\eta +\tau \right){\mu }^2+\phi q{t}^2\right]+\left(\alpha c\mu -2\alpha \phi q-\lambda \mu q\right)\left(\eta +\tau \right)+\mu s_{c}t\tau \\ +\left[2\lambda \phi q\left(\eta +\tau \right)-c{\mu }^2\left(\eta +\tau \right)-c\phi t^2\right]\left(\alpha -\beta \right)-2\phi s_{c}t\tau \left(\alpha -\beta \right)\end{array}}{4\left[\left(\phi t^2-\left(\mu \left(1-\mu \right)+\phi \left(\alpha +\beta \right)\right)\left(\tau +\eta \right)\right)\left(\alpha -\beta \right)\right]+2\alpha \left(\eta +\tau \right)-t^2}},$$

(12)

$$g^{c2}={\displaystyle \frac{\begin{array}{l}\left[\left(2c\mu \left(\alpha +\beta \right)+2q\left(\lambda +\mu \right)-4\lambda \mu q\right)\left(\eta +\tau \right)-c{t}^2\right]\left(\alpha -\beta \right)\\ +q{t}^2-2\alpha q\left(\eta +\tau \right)+\left(4\mu s_{c}t\tau -2s_{c}t\tau \right)\left(\alpha -\beta \right)\end{array}}{4\left[\left(\phi t^2-\left(\mu \left(1-\mu \right)+\phi \left(\alpha +\beta \right)\right)\left(\tau +\eta \right)\right)\left(\alpha -\beta \right)\right]+2\alpha \left(\eta +\tau \right)-t^2}},$$

(13)

$$s_r={\displaystyle \frac{\begin{array}{l}t\left[\alpha c-\lambda q+\mu q+\left(4\lambda \phi q-c\mu -2\phi q-2c\phi \left(\alpha +\beta \right)\right)\left(\alpha -\beta \right)\right]\\ -s_c\left[4\left(\eta {\mu }^2+\phi t^2-\eta \phi \left(\alpha +\beta \right)-\eta \mu \right)\left(\alpha -\beta \right)+2\eta \alpha -t^2\right]\end{array}}{4\left[\left(\phi t^2-\left(\mu \left(1-\mu \right)+\phi \left(\alpha +\beta \right)\right)\left(\tau +\eta \right)\right)\left(\alpha -\beta \right)\right]+2\alpha \left(\eta +\tau \right)-t^2}}.$$

(14)

4.2. Decentralized Decision-Making

Here, $M$ decides $p_1$ and $g$ as a leader, and $R$ decides $p_2$ and $s$ as a follower. $R$’s service decisions meet the following conditions:

$$s_r=\left\{\begin{array}{l}\left(t{p}_2-w\right)/\eta ,if s_c\le \left(t{p}_2-w\right)/\eta \\ \left(t{p}_2-w-\eta s_c\right)/\left(\eta +\tau \right),if s_c>\left(t{p}_2-w\right)/\eta \end{array}\right..$$

(15)

When $s_c\le \left(t{p}_2-w\right)/\eta$, the optimal decision in decentralized decision-making is defined as $p_1^{d1}$, $p_2^{d1}$, $s^{d1}$, and $g^{d1}$. When $s_c>\left(t{p}_2-w\right)/\eta$, additional service quality costs are incurred due to the impact of customer expectations, as a result of which the optimal decision in decentralized decision-making is defined as $p_1^{d2}$, $p_2^{d2}$, $s^{d2}$, and $g^{d2}$.

4.2.1. Without Service Quality Cost

When $s_c\le \left(t{p}_2-w\right)/\eta$, $s_r\ge 0$ when no additional service quality cost is incurred.

Proposition 3. 

When $2\alpha \eta >t^2$, ${\pi }_2$ is jointly concave in  $p_2$ and  $s$, their distinct optimal solutions are, respectively, presented as follows:

$$p_2^{d1}={\displaystyle \frac{w\left(\alpha \eta -t^2\right)+\eta g\left(1-\mu \right)+\eta q\left(1-\lambda \right)+\beta \eta p_1}{2\alpha \eta -t^2}},$$

(16)

$$s^{d1}={\displaystyle \frac{t\left[\left(1-\mu \right)g+\left(1-\lambda \right)q+\beta p_1-\alpha w\right]}{2\alpha \eta -t^2}}.$$

(17)

Substituting (16) and (17) into (3), we get

$$p_1^{d1}={\displaystyle \frac{c\left[\left(2\alpha \phi A_1+B^2\right){\eta }^2+\left(2{\alpha }^2+A_1\right)\eta \phi t^2+X_1+Y_1\right]-w\left(\alpha {\eta }^{2}C+t^2{Z}_1\right)+\phi q\left(\eta D+E\right)+\phi q{t}^4}{\left(B^2-4A_1\phi \alpha \right){\eta }^2+2\eta t^2\left[\phi \left(2{\alpha }^2+A\right)-2\phi \alpha \beta -B\right]+t^4\left(2\phi \beta -2\phi \alpha +1\right)}},$$

(18)

$$g^{d1}={\displaystyle \frac{\begin{array}{l}c\left[\left(\alpha -\beta \right)t^4+AB{\eta }^2-t^2\eta \left(A+\left(\alpha -\beta \right)B\right)\right]-q{t}^4-IBq{\eta }^2\\ -w\left[\left(\alpha B-\beta B+2\alpha F\right)\eta -4F{\alpha }^2{\eta }^2-t^4\left(\alpha -\beta \right)\right]-\left(G-B\right)t^{2}q\eta \end{array}}{\left(B^2-4A_1\phi \alpha \right){\eta }^2+2\eta t^2\left[\phi \left(2{\alpha }^2+A_1\right)-2\phi \alpha \beta -B\right]+t^4\left(2\phi \beta -2\phi \alpha +1\right)}},$$

(19)

where $A=2{\alpha }^2-{\beta }^2$, $B=\beta +2\alpha \mu -\beta \mu$, $C=\alpha \left[\left(1-\mu \right)B+4\alpha \beta \phi \right]$, $D=2\alpha \left(2\alpha \lambda +\beta -\beta \lambda \right)$, $E=-2\alpha t^2\left(1+\lambda \right)-\beta t^2\left(1-\lambda \right)$, $F=\alpha \mu -\beta \mu -\alpha$, $G=\beta -\beta \lambda +2\alpha \lambda$, $X_1=t^2\left(t^2-4\alpha \eta \mu +2\beta \eta \mu -2\beta \eta \right)$, $Y_1=\phi t^2\left(\beta t^2-\alpha t^2-2\alpha \beta \eta \right)$, and $Z_1=\left(\alpha \mu -\alpha +2{\alpha }^2\phi -4\alpha \beta \phi \right)-\left(\alpha -\beta \right)\phi t^2$.

Substituting (18) and (19) into (16) and (17), the optimal decision of the retailer is obtained.

4.2.2. With Service Quality Cost

When $s_c>\left(t{p}_2-w\right)/\eta$, $s_r<0$ and additional service quality costs will be incurred at this point.

Proposition 4. 

When $2\alpha \left(\eta +\tau \right)>t^2$, ${\pi }_2$ is jointly concave in  $p_2$ and $s$, their distinct optimal solutions are, respectively, presented as follows:

$$p_2^{d2}={\displaystyle \frac{w\left[\alpha \left(\eta +\tau \right)-t^2\right]+\left[g\left(1-\mu \right)+q\left(1-\lambda \right)+\beta p_1\right]\left(\eta +\tau \right)+\tau t{s}_c}{2\left(\eta +\tau \right)-t^2}},$$

(20)

$$s_r^{d2}={\displaystyle \frac{t\left[g\left(1-\mu \right)+q\left(1-\lambda \right)+\beta p_1-\alpha w\right]-s_c\left(2\alpha \eta -t^2\right)}{2\alpha \eta +2\alpha \tau -t^2}},$$

(21)

Substituting (20) and (21) into (3), we obtain the following:

$$p_1^{d2}={\displaystyle \frac{\begin{array}{l}c\left[\left(2\alpha \phi A+B^2\right){\left(\eta +\tau \right)}^2+\left(2{\alpha }^2+A_1\right)\left(\eta +\tau \right)\phi t^2+X_2-\phi \alpha t^4\right]-w\left[\alpha {\left(\eta +\tau \right)}^{2}C+t^2{Z}_2\right]\\ +\phi q{t}^4+\phi q\left[\left(\eta +\tau \right)D+E\right]\left(\eta +\tau \right)+\phi t\tau s_c\left(t^2-2\alpha \right)\left(2\alpha -\beta \right)\left(\tau +\eta \right)\end{array}}{\left(B^2-4A\phi \alpha \right){\left(\eta +\tau \right)}^2+2t^2\left(\eta +\tau \right)\left[\phi \left(2{\alpha }^2+A_1\right)-\mu B\right]+t^4\left({\mu }^2-2\phi \alpha \right)}},$$

(22)

$$g^{d2}={\displaystyle \frac{\begin{array}{l}c\left[\left(\alpha -\beta \right)t^4+AB{\left(\eta +\tau \right)}^2-t^2\left(\eta +\tau \right)\left(A+\left(\alpha -\beta \right)B\right)\right]-\left[t^2-\left(\tau +\eta \right)B\right]\left(2\alpha -\beta \right)t\tau s_c-q{t}^4\\ -w\left[t^2\left(\alpha B-\beta B+2\alpha F\right)\left(\eta +\tau \right)-4F{\alpha }^2{\left(\eta +\tau \right)}^2-t^4\left(\alpha -\beta \right)\right]-GBq{\left(\eta +\tau \right)}^2-\left(G-B\right)\left(\eta +\tau \right)t^{2}q\end{array}}{\left(B^2-4A_1\phi \alpha \right){\left(\eta +\tau \right)}^2+2t^2\left(\eta +\tau \right)\left[\phi \left(2{\alpha }^2+A\right)-\mu B\right]+t^4\left({\mu }^2-2\phi \alpha \right)}},$$

(23)

where $X_2=t^2\left[{\mu }^2{t}^2-2\mu B\left(\eta +\tau \right)\right]$, and $Z_2=\mu t^4\left(1-\mu \right)+2\beta \phi t^4-t^2\left[\left(\alpha \mu +B\right)\left(1-\mu \right)+6\alpha \beta \phi \right]$.

Substituting (22) and (23) into (20) and (21), respectively, the optimal decision of the retailer is thus obtained.

Corollary 1. 

Whether  $s_r>0$ or  $s_r<0$, compared with decentralized decision-making, centralized decision-making in the supply chain has a higher total profit; that is, ${\pi }^c>{\pi }^d$. This is because the supply chain optimizes the overall profit level when decisions are made centrally. In comparison, when decisions are decentralized, the independent decisions made by each member to maximize their own interests can cause losses to other channels.

Corollary 2. 

Whether  $s_r>0$ or $s_r<0$, for both  $M$ and  $R$, the profit of centralized decision-making in the entire supply chain is not higher than that of decentralized decision-making. From Corollary 1, it follows that for the entire supply chain, there is a higher total profit at the time of centralized decision-making; however, for each supply chain member, centralized decision-making does not necessarily translate to higher profit, i.e., ${\pi }_1^d>{\pi }_1^c$ or  ${\pi }_2^d>{\pi }_2^c$.

4.3. Supply Chain Collaboration Model

Considering the double marginal effect, the total profit of the supply chain in decentralized decision-making is less than that in centralized decision-making (${\pi }^d<{\pi }^c$). To maximize profits for M and R, we coordinate the supply chain through profit-sharing contracts, where $M$ and $R$ share the residual profits based on their respective bargaining power. Assume that the bargaining power of $M$ and $R$ are ${\gamma }_1$ and ${\gamma }_2$, respectively ($0<{\gamma }_1+{\gamma }_2\le 1$), and supply chain members with a stronger bargaining power can gain higher profits. Supply chain coordination can be realized by contract design so that supply chain members have higher profits when making decentralized decisions.

Assuming that the proportion of the residual profit obtained by $M$ is ${\gamma }_1$, then $M$’s profit increases by ${\gamma }_1\left({\pi }^c-{\pi }^d\right)$. Similarly, assuming that the proportion of the residual profit obtained by $R$ is ${\gamma }_2$, then $R$’s profit increases by ${\gamma }_2\left({\pi }^c-{\pi }^d\right)$. By sharing the surplus profit $\left({\pi }^c-{\pi }^d\right)$, manufacturers and retailers will be willing to cooperate with each other. Thus, the collaboration model can be expressed as follows:

$$\begin{array}{c}\hfill \max {\pi }^{co}=\left(p_1-c\right)\left(\lambda q-\alpha p_1+\beta p_2-ts+\mu g\right)-\eta s^2/2-\tau {\left[\min \left(s_r,0\right)\right]}^2/2\hfill \\ \hfill +p_2\left[\left(1-\lambda \right)q-\alpha p_2+\beta p_1+ts+\left(1-\mu \right)g\right]-\phi g^2/2\hfill \end{array}.$$

(24)

For the manufacturer and the retailer, we have the following:

$$\begin{array}{cc}\hfill {\pi }_1^{co}& =\left(p_1-c\right)\left(\lambda q-\alpha p_1+\beta p_2-ts+\mu g\right)-\phi g^2/2\hfill \\ \hfill & +w\left[\left(1-\lambda \right)q-\alpha p_2+\beta p_1+ts+\left(1-\mu \right)g\right]\hfill \\ \hfill & \ge {\pi }_1^d+{\gamma }_1\left({\pi }^c-{\pi }^d\right)\hfill \end{array},$$

(25)

$$\begin{array}{c}\hfill {\pi }_2^{co}=p_2\left[\left(1-\lambda \right)q-\alpha p_2+\beta p_1+ts+\left(1-\mu \right)g\right]-\eta s^2/2-\tau {\left[\min \left(s_r,0\right)\right]}^2/2\hfill \\ \ge {\pi }_2^d+{\gamma }_2\left({\pi }^c-{\pi }^d\right)\hfill \end{array}$$

(26)

Due to the complexity of the calculation process, the relevant conclusions are solved and analyzed via numerical simulation.

5. Numerical Analysis

In this section, we perform a numerical analysis to prove the theoretical results. First, we assign two sets of values to verify the equilibrium model. Then, we perform sensitivity analysis on the green input and service input cost coefficients to discuss their effects on optimal decisions and profits.

5.1. Equilibrium Analysis

We first perform an equilibrium analysis. The numerical settings of this paper are shown in

Table 3. Numerical data of $K1 \mathrm{a}\mathrm{n}\mathrm{d} K2$.

	$\mathit{q}$	$\mathit{\lambda }$	$\mathit{\alpha }$	$\mathit{\beta }$	$\mathit{w}$	$\mathit{c}$	$\mathit{t}$	$\mathit{\mu }$	$\mathit{\eta }$	$\mathit{\tau }$	$\mathit{\phi }$
K1	80	0.55	2.5	1.5	10	5	4	0.55	5	10	4
K2	100	0.45	3.5	2.55	15	7.5	2.5	0.35	5.5	15	7.55

, in which two data sets ($K1, K2$) are used. In the table, the $K1$ data group is set to $s_c=15$ when $s_r<0, \mathrm{w}\mathrm{h}\mathrm{i}\mathrm{l}\mathrm{e} \mathrm{t}\mathrm{h}\mathrm{e} K2$ data group is set to $s_c=8$ in centralized decision-making and $s_c=15$ in decentralized decision-making.

Table 4. Equilibrium results of centralized decision making without the service quality cost.

	${\mathit{p}}_1^{\mathit{c}1}$	${\mathit{p}}_2^{\mathit{c}1}$	${\mathit{g}}^{\mathit{c}1}$	${\mathit{s}}^{\mathit{c}1}$	${\mathit{\pi }}_1^{\mathit{c}1}$	${\mathit{\pi }}_2^{\mathit{c}1}$	${\mathit{\pi }}_{\mathit{s}\mathit{c}}^{\mathit{c}1}$
$K1 (s_c\le 5.75$)	21.37	23.57	4.90	5.75	387.07	383.61	770.68
$K2 (s_c\le 2.61$)	29.98	28.22	3.47	2.61	727.39	529.49	1256.88

,

Table 5. Equilibrium results of centralized decision making with the cost of service quality.

	${\mathit{p}}_1^{\mathit{c}2}$	${\mathit{p}}_2^{\mathit{c}2}$	${\mathit{g}}^{\mathit{c}2}$	${\mathit{s}}^{\mathit{c}2}$	${\mathit{\pi }}_1^{\mathit{c}2}$	${\mathit{\pi }}_2^{\mathit{c}2}$	${\mathit{\pi }}_{\mathit{s}\mathit{c}}^{\mathit{c}2}$
$K1 (s_c=15$)	20.40	24.54	4.88	11.61	361.00	161.27	522.27
$K2 (s_c=8$)	29.13	29.19	3.51	6.76	686.61	520.27	1206.88

,

Table 6. Equilibrium results of decentralized decision making without the cost of quality of service.

	${\mathit{p}}_1^{\mathit{d}1}$	${\mathit{p}}_2^{\mathit{d}1}$	${\mathit{g}}^{\mathit{d}1}$	${\mathit{s}}^{\mathit{d}1}$	${\mathit{\pi }}_1^{\mathit{d}1}$	${\mathit{\pi }}_2^{\mathit{d}1}$	${\mathit{\pi }}_{\mathit{s}\mathit{c}}^{\mathit{d}1}$
$K1 (s_c\le 12.84$)	11.39	26.05	1.74	12.84	421.84	231.73	653.57
$K2 (s_c\le 4.71$)	22.39	25.36	1.77	4.71	832.07	314.74	1146.81

,

Table 7. Equilibrium results of decentralized decision making with the cost of service quality.

	${\mathit{p}}_1^{\mathit{d}2}$	${\mathit{p}}_2^{\mathit{d}2}$	${\mathit{g}}^{\mathit{d}2}$	${\mathit{s}}^{\mathit{d}2}$	${\mathit{\pi }}_1^{\mathit{d}2}$	${\mathit{\pi }}_2^{\mathit{d}2}$	${\mathit{\pi }}_{\mathit{s}\mathit{c}}^{\mathit{d}2}$
$K1 (s_c=15$)	14.24	23.20	2.13	12.33	428.94	19.88	448.82
$K2 (s_c=12.5$)	20.73	26.85	1.67	10.59	809.99	155.47	965.46

and

Table 8. Equilibrium results of the collaborative model for the data set K1 for different combinations of ${\gamma }_1$ and ${\gamma }_2$($s_r>0$).

${\mathit{\gamma }}_{\mathit{i}}$	${\mathit{p}}_1^{\mathit{c}\mathit{o}}$	${\mathit{p}}_2^{\mathit{c}\mathit{o}}$	${\mathit{g}}^{\mathit{c}\mathit{o}}$	${\mathit{s}}^{\mathit{c}\mathit{o}}$	${\mathit{\pi }}_1^{\mathit{c}\mathit{o}}$	${\mathit{\pi }}_2^{\mathit{c}\mathit{o}}$	${\mathit{\pi }}_{\mathit{s}\mathit{c}}^{\mathit{c}\mathit{o}}$
${\gamma }_1=0.85$ ${\gamma }_2=0.10$	23.29	20.39	4.60	0.23	515.49	238.75	754.23
${\gamma }_1=0.55$ ${\gamma }_2=0.40$	22.66	20.99	4.59	1.53	484.00	276.60	760.60
${\gamma }_1=0.25$ ${\gamma }_2=0.60$	22.04	21.74	4.52	2.97	451.83	313.99	765.83

reveal several results regarding the data sets $K1$ and $K2$. (1) $R$ has a higher level of service in decentralized decision-making (i.e., $s^d>s^c$). Considering consumers’ service preference, $R$ will try to improve the service level to attract them because of the competition of online green products in the scenario of decentralized decision-making. (2) The product greenness of $M$ is higher in centralized decision-making than that in decentralized decision-making (i.e., $g^c>g^d$). (3) Customer expectations are dynamic; thus, when customer expectations are too high, $R$’s profitability decreases significantly due to certain factors, such as customer churn, which brings about additional service quality costs. In comparison, when customer expectations increase, the impact on $M$ is two-way: on the one hand, some demand flows to the online channel when $R$ has difficulty meeting customer service preferences, thus allowing $M$ to increase the price of green products to increase profits. On the other hand, the decline in $R$’s demand extends upstream of the supply chain, thus affecting $M$’s profits. (4) Compared with decentralized decision-making, centralized decision-making in the supply chain has a higher total profit, which is consistent with Corollary 1. (5) Finally, M has the highest profit when the decision is decentralized, while $R$ has the highest profit when the decision is centralized. This is consistent with Corollary 2.

From Table 8 and

Table 9. Equilibrium results of the collaborative model for the data set K1 for different combinations of ${\gamma }_1$ and ${\gamma }_2$ ($s_c=15$).

${\mathit{\gamma }}_{\mathit{i}}$	${\mathit{p}}_1^{\mathit{c}\mathit{o}}$	${\mathit{p}}_2^{\mathit{c}\mathit{o}}$	${\mathit{g}}^{\mathit{c}\mathit{o}}$	${\mathit{s}}^{\mathit{c}\mathit{o}}$	${\mathit{\pi }}_1^{\mathit{c}\mathit{o}}$	${\mathit{\pi }}_2^{\mathit{c}\mathit{o}}$	${\mathit{\pi }}_{\mathit{s}\mathit{c}}^{\mathit{c}\mathit{o}}$
${\gamma }_1=0.85$ ${\gamma }_2=0.10$	15.31	20.67	3.34	11.33	453.94	25.92	479.85
${\gamma }_1=0.55$ ${\gamma }_2=0.40$	15.98	21.50	3.58	11.34	443.74	45.80	489.54
${\gamma }_1=0.25$ ${\gamma }_2=0.60$	16.66	22.25	3.84	11.38	432.57	66.97	499.55

, we can ascertain several findings. (1) When $s_r>0$, as ${\gamma }_2$ increases and ${\gamma }_1$ decreases, $M$’s product price and green input decrease, while $R$’s product price and service level increase. On the contrary, when $s_r>0$, as ${\gamma }_2$ increases and ${\gamma }_1$ decreases, the optimal product price, green input, and service input of $M$ and $R$ increase. (2) As M’s bargaining power decreases and $R$’s bargaining power increases, $M$’s profit decreases and $R$’s profit increases. Furthermore, the tables show that the profit-sharing contract is valid because the profits of $M$ and $R$ have increased.

5.2. Sensitivity Analysis

To further analyze the effects of green input cost coefficient and service input cost coefficient on supply chain members’ decisions and profits, we assume that $q=80$, $\lambda =0.51$, $\alpha =2.5$, $\beta =1.5$, $w=10$, $t=3.5$, $c=5$, $\mu =0.55$, and $\tau =12.5$.

5.2.1. Impact of Green Input Cost Coefficient ($\phi$)

This section focuses on the effects of $\phi$ on optimal decisions and profits and assumes the service cost coefficient to be $\eta =5$. Figure 2 indicates the effect of $\phi$ on the optimal decisions and profit, where Figure 2a,b,e show the optimal decision and profit when $s_r\ge 0$, while Figure 2c,d,f show the optimal decision and profit when $s_r<0$ ($s_c=15$). (1) In both the centralized and decentralized models, product greenness, service level, and product price decrease with increasing $\phi$, and the sensitivity of the service level to $\phi$ is low (Figure 2a–d), thereby indicating that $M$ has sufficient incentives to provide products with high green levels when greening costs are not high, as a way to boost green product demand and increase green product prices. When $\phi$ increases, $M$ will incur more costs in manufacturing a product of the same greenness, thereby discouraging the production of a high-greenness product. As a competitor, $R$ must provide a high level of service to enhance demand in the face of $M$’s higher-level green product. Supply chain profits in general also show a decreasing trend and have a negative impact on the supply chain when service levels do not meet customer expectations (Figure 2d,e). Furthermore, it can be seen that higher green input costs are not conducive to green production by the manufacturer; thus, the government can provide the manufacturer with certain tax incentives or green subsidies to improve the level of emission reduction [42].

(2) $M$ has a higher product greenness in centralized decision-making, while $R$ has higher service levels in decentralized decision-making, suggesting that increased competition between the dual channels has forced the retailer to undertake more service input. (3) Furthermore, $M$ has a higher product greenness in centralized decision-making; that is, it is friendlier to the environment in a centralized model. $M$ also has higher profits in the decentralized model. Conversely, $R$ has higher profits in the centralized model, and both profits decrease as $\phi$ increases, suggesting that a lower green quality cost factor is beneficial to the supply chain.

5.2.2. Impact of Service Input Cost Coefficient ($\eta$)

This section focuses on the effect of $\eta$ on optimal decisions and profits, assuming the green input cost coefficient to be $\phi =4$. In particular, Figure 3 indicates the effects of $\eta$ on the optimal decisions and profit, where Figure 3a,b,e show the optimal decision and profit when $s_r\ge 0$, while Figure 3c,d,f show the optimal decision and profit when $s_r<0$ ($s_c=18$). (1) As $\eta$ increases, $R$ reduces the service level, which is evident (Figure 3a,c). For example, during the epidemic period, Hema retailers had to reduce the distribution scope and delivery time due to the shortage of human resources and the increase in service costs. Therefore, to compensate for the negative impact of decreasing product demand, $R$ chooses to lower the price to increase demand (Figure 3b,d). To further increase competitive advantage, when the service level of $R$ decreases, $M$ will increase its green input to increase demand for the green product, thus driving $M$ to increase product prices (Figure 3a,c).

(2) As can be seen from Figure 3e,f, the profit of $M$ (${\pi }_1$), the profit of $R$ (${\pi }_2$), and the total supply chain profit ($\pi$) all decrease when the supply chain is decentralized. In contrast to existing studies [43], we find that when $s_r<0$, due to the additional service quality costs incurred, $R$’s profits are not only lower, but the rate of profit reduction accelerates as the service cost ($\eta$) increases. (3) In addition, $M$ has the highest profit when a decentralized decision is made, while $R$ has the highest profit when a centralized decision is made, which is consistent with Corollary 1. Similarly, the whole supply chain has a higher profit in centralized decision-making, which is consistent with Corollary 2.

6. Conclusions and Outlook

To protect the environment and improve green performance, manufacturers must invest in green products in the online channel, thus creating conflicts with retailers in the offline channel, who choose to provide value-added services to attract consumers. Therefore, this paper develops a dual-channel supply chain model that explores the competitive decision and profit variation between a manufacturer’s green input and a retailer’s service input when considering customer service expectations. The centralized, decentralized, and coordinated models of the supply chain are explored, and the effects of the relevant parameters on the equilibrium results are further analyzed.

The findings of this paper are as follows: (1) Customer green preferences and service preferences can have simultaneous effects on supply chain members’ decisions. Manufacturers tend to increase the greenness of their products when the green input costs are not high, and the competition between dual channels and customers’ preferences for services is likely to drive retailers to improve the service level. Conversely, when retailers reduce service levels due to higher service costs, manufacturers increase green inputs to capture market share. (2) The decisions of the supply chain are influenced by customer expectations. In particular, when customer expectations are not high, retailers are motivated to improve service levels to enhance product demand. Conversely, when customer expectations are too high, this situation leads to lower profits for the retailer and further lowers the profit level of the entire supply chain. (3) When the bargaining power of the manufacturer or retailer is low, more green inputs or service inputs are needed to compete with another channel, and when such green or service inputs gradually increase, the additional increase in costs can be compensated for by appropriate increases in product prices. (4) The collaborative model allows supply chain members to share the residual profit and thus obtain higher profits than when the decision is decentralized. Furthermore, the residual profit obtained in the collaborative model is influenced by bargaining power. Compared with the decentralized decision, the centralized decision is friendlier to the environment because manufacturers can use the residual profit for green input and thus produce green products with high greenness.

Therefore, based on the abovementioned findings, we offer the following management insights:

Consumers should actively express their preferences for green products and services. In particular, they should proactively and clearly articulate their preferences for ecofriendly products and services through social media, online platforms, and other channels to encourage manufacturers and retailers to focus on environmental sustainability. In addition, when choosing products and services, consumers should favor brands that excel in green initiatives and services, thus promoting a market culture that values sustainability and environmental responsibility.

Retailers should flexibly adjust their service levels based on consumer service expectations and manufacturers’ green investment strategies to meet customer needs. Such efforts may include adding after-sales projects, extending warranty periods, or offering personalized services. Additionally, retailers should establish a service management system to regularly evaluate service content and optimize service quality through customer satisfaction surveys, market trend analysis, and regular employee training. Furthermore, retailers can collaborate with manufacturers to develop promotional strategies that combine green products with high-quality services, thus strengthening their competitive advantage and enhancing the appeal of offline channels.

Collaborative decision-making is an effective means for supply chain members to achieve mutual benefits and sustainable development. Manufacturers and retailers should band together to jointly develop green products and services through strategies, such as comarketing, environmental technology cooperation, and green certification systems, thus enhancing the overall efficiency of the supply chain and achieving a win–win outcome. Furthermore, manufacturers should fully utilize residual profits for making green investments, improving the greenness of products through green technology R&D, implementing production process improvements, or launching environmental promotion activities, thus enhancing their brands’ green image.

This research has some limitations that must be addressed in future studies. First, it assumes that market demand is deterministic; however, in real-world business environments, market demand is often difficult to predict accurately. Therefore, future research should explore the impact of demand uncertainty on supply chain decision-making and provide more practical strategic recommendations. Second, this study only considers the scenario wherein retailers provide services. As manufacturers undergo servitization, they also start offering services to customers, leading to enhanced competition between manufacturers and retailers in the service domain. Thus, future research should examine the impact of supply chain members’ green investments on consumers’ service channel choices. Finally, future research should focus on the innovation and development of green services, specifically investigating how companies can enhance customer satisfaction and competitive advantage through green services.