1. Introduction
Green supply chains (GSCs) are currently the primary development path due to global resource and environmental restrictions [
1]. Green supply chain management, which emphasizes the triple bottom line principle, has emerged as a cutting-edge topic in contemporary supply chain management [
2]. Numerous manufacturing companies are dedicated to develo** and producing eco-friendly products that minimize emissions and pollution while conserving energy during production, notwithstanding various challenges including international green barriers, government regulations, and heightened consumer awareness of environmental issues [
3]. However, how can a product be made more ecologically friendly? It is imperative that manufacturers continue to develop innovative green solutions. Several studies have shown that green innovation prioritizes minimizing negative environmental effects through product development, innovation, and process optimization to reduce pollutant emissions and energy consumption during the production process. This is in contrast to traditional innovation, which focuses only on financial gains [
4]. Because of their higher eco-efficiency and enhanced environmental reputation, businesses that adopt green innovation frequently have a competitive edge over their rivals [
5].
Nevertheless, the high expenses, risks, and “double externalities” associated with green innovation significantly diminish firms’ incentives to pursue it [
6]. Research indicates that vertical cooperation and synergy between supply chain members are necessary for the highest performance of green innovation [
5], as firms’ efforts alone do not yield the best results [
7]. According to Chen et al. [
8], cooperative green research and development (R&D) can benefit companies, society, and the environment while also reducing or eliminating the negative environmental effects of goods or services. Therefore, collaborating and working together to innovate is the greatest way for supply chain actors to improve the performance of green innovation. How can the manufacturer, who is the centre of the supply chain, inspire suppliers to collaborate in implementing green innovation?
In the era of the digital economy, data has become an indispensable innovation resource [
9] and a critical strategic tool for manufacturing companies [
10]. This has resulted in increased decision accuracy and the creation of global value opportunities for the supply chain [
11,
12], as well as significant changes to most of the planning, production, sales, and supply chain [
13]. Therefore, the ability to obtain, refine, analyze, and integrate data has become necessary for enterprises and supply chains to generate commercial value and enhance competitiveness [
14,
15]. According to dynamic capability theory [
16], digital capability is a dynamic capability that acquires data resources through digital technology and refines, integrates, updates and reconstructs them, cultivating and building the ability to transform data as a factor of production into business model innovation [
17]. It enables companies to gain faster access to the specialized knowledge resources that are required for new product creation [
18] and give business a competitive advantage. Businesses with a strong digital presence can obtain the technical expertise required to successfully innovate and create new goods [
19]. At present, many scholars have reached a consensus on the relationship between digital capabilities and corporate green innovation [
20,
21,
22]. The majority of studies now focus on the positive relationship between the two from the standpoint of empirical research. However, few studies have examined the dynamic impact of digital capabilities on green innovation from a game theory viewpoint.
The purpose of this study is to develop a set of scientific and rational incentives for the manufacturer in the context of enterprises’ digital transformation. These incentives will encourage the supplier to fully engage in collaborative GSC innovation, ultimately contributing to the sustainable development of GSCs. However, incentive strategies of the supply chain proposed in the existing literature mainly focus on traditional contracts, such as cost-sharing mechanisms [
23], two-part tariff contracts [
24], and revenue-sharing mechanisms [
25]. It is worthwhile to explore further the design of incentive contracts from the angle of direct incentivization of target product greenness. Considering the current trend of digital transformation, a quantitative model characterizing the relationship between digitalization and green innovation is needed. Its influence on GSC decision making and incentive contract selection is also inadequate. Furthermore, design of an incentive mechanism for the core manufacturer and supplier has remained unexplored from the standpoint of digitalization.
Based on this, this research examines the optimal equilibrium strategy of each participant in the manufacturer-led GSC from the perspective of game theory and studies the influence of different incentive mechanisms in the dynamic environment. To better describe the dynamic process of product greenness, we constructed a differential equation with the greenness of the intermediate and final products as the state variables and considered digital capability as the driving factor of greenness in the model. On the basis of the standard scenario, we designed three incentive mechanisms from the perspectives of product greenness (direct incentive) and its driving factors (indirect incentives): the greenness reward, the R&D effort reward, and the digital construction reward. The long-term dynamic impact of the three incentive mechanisms on the economic, environmental and social benefits of the GSC were compared and analyzed. This article mainly answers the following three questions:
(1) How have the economic benefits, environmental benefits, and social welfare of the GSC evolved over time as a result of different incentive schemes?
(2) Which is the most advantageous incentive mechanism for the economic benefits, environmental benefits, and social welfare of the GSC, and how should the reward coefficient be determined?
(3) What is the impact of consumers’ green preference and the promotion of digital capability on green product innovation and the economic benefits, environmental benefits, and social welfare of the GSC?
By answering the aforementioned questions, this study offers recommendations for supply chain managers to formulate incentive mechanisms. It also provides a point of reference for businesses implementing digital transformation, green innovation practices, and other useful issues, such as how to make products greener and enhance the performance of the GSC in terms of the economy, the environment, and society.
The primary contributions of this article are as follows:
(1) The differential equation is used to describe the changes in the greenness of intermediate and final products with the green innovation efforts of GSC members. The optimal decision making and incentive contracts of the GSC are analyzed dynamically rather than statically. Green innovation, pollutant emissions, and energy efficiency are all dynamic phenomena [
5,
26,
27]. The product’s greenness and the pertinent decisions made in the subsequent stage will be influenced by the innovation efforts within GSC participators in the preceding stage. As a result, the analysis of dynamic tactics in this study utilizing differential game theory is more accurate.
(2) This study examines the dynamic relationship between enterprises’ digital capability and product green innovation, taking digital capability as a driving factor of product greenness into the model. This leads to a discussion of how optimal decision making in GSC changes dynamically, which not only enriches the theory of digital-driven green innovation but also improves the field of optimal decision making in GSC.
(3) Three reward (or incentive) strategies are proposed, different from previous research on GSCs. According to this study, a reward strategy that successfully motivates suppliers to actively collaborate with manufacturers to creatively sustain long-term efforts on product greenness, as opposed to passively accepting them, may be a more effective and efficient incentive mechanism. Thus, in light of product greenness and its influencing elements, this study suggests three realistic, scientific, and reasonable incentive contracts—the greenness reward mechanism, the R&D effort reward mechanism, and the digital construction reward mechanism. The study of dynamic contracts can accurately characterize the long-term decision-making problem in the GSC, which is an extension and supplement to the research on collaborative supply chain innovation and incentive schemes.
The rest of the paper is organized as follows.
Section 2 reviews the literature and proposes a research gap.
Section 3 describes the problems and puts forward three incentive mechanisms.
Section 4 establishes the models and presents the Stackelberg equilibrium results for different scenarios.
Section 5 makes a comparative analysis of optimal strategies.
Section 6 conducts numerical simulation for further analysis.
Section 7 gives some enlightenment for managers. Finally, the conclusion and future research directions are given in
Section 8. The proof process is detailed in the
Appendix B.
5. Comparative Analysis
This section compares the optimal decisions of the manufacturer and the supplier in four scenarios—the non-incentive scenario, greenness reward scenario, R&D effort reward scenario, and digital construction reward scenario. Additionally, the pros and cons of each incentive mechanism are analyzed in terms of its effects on economic benefits, environmental benefits, and social welfare. Then, the following propositions can be obtained:
Proposition 9. In the scenarios of no incentive, greenness reward, R&D effort reward, and digital construction reward, the relationships of green innovation R&D effort level and digital capability level of the supplier are as follows:
(I) when , ;
(II) when , ;
(III) when , ;
(IV) when , .
Proposition 9 shows that the green innovation R&D effort level and digital capability level of the supplier under the three incentive mechanisms are higher than those in the non-incentive scenario, indicating that the three incentive mechanisms contribute to the development of the supplier’s green innovation R&D and digital contribution. Out of the three incentive mechanisms, the digital construction incentive mechanism provokes the least enthusiasm from the supplier for green innovation R&D, and the R&D effort incentive mechanism gains the lowest digital capability level for the supplier. When choosing an incentive mechanism, the manufacturer must take into account the value of the corresponding incentive coefficient, since this determines which mechanism has the greatest incentive effect on the supplier.
Proposition 10. In the scenarios of no incentive, greenness reward, R&D effort reward, and digital construction reward, there are the following relationships between stable values of demand and the greenness of intermediate and final products, consumer surplus, and environmental benefits:
(I) when , , , , , ;
(II) when , , , , ;
(III) when , , , , ;
(IV) when , , , , ;
(V) when , , , , , ;
(VI) when , , , , .
Proposition 10 demonstrates that, in contrast to the non-incentive scenario, the three incentive mechanisms have a promoting effect on demand and the greenness of intermediate and final products, environmental benefits, and consumer welfare in the long run. The changing trend of the latter three mechanisms and the greenness of products is consistent. Which incentive the manufacturers should choose to achieve the strongest incentive effect (the highest environmental benefits and consumer welfare) depends on the setting of the reward coefficients of the three incentive mechanisms.
Proposition 11. The stable profit of the supplier in the non-incentive scenario is the lowest, and that in the greenness reward mechanism, R&D effort incentive mechanism, and digital construction incentive mechanism is increased, i.e., , , .
Proposition 12. Compared with the non-incentive scenario, the relationships among the stable profits of the manufacturer in the three mechanisms of the greenness reward, the R&D effort reward, and the digital construction reward are as follows:
(I) when , ;
(II) when , ;
(III) when , .
Propositions 11 and 12 examine the long-term incentive effects of the three incentive mechanisms from the standpoint of economic benefits. They reveal that the three incentive mechanisms boost the supplier’s profits over time; consequently, they have effects for the supplier. However, the optimal incentive mechanism for a given supplier depend on the actual parameters, which are further elaborated on in
Section 6. For the manufacturer, the three incentive mechanisms do not always increase profits, in fact, the manufacturer can only benefit from the incentive mechanism if specific conditions of the incentive coefficient are met.
Proposition 13. In the scenarios of no incentive, the greenness reward, the R&D effort reward and the digital construction reward, the stable greenness of intermediate and final products, the green innovation R&D effort level, and the digital capability level of the manufacturer and the supplier are positively correlated with consumer green preference for the intermediate and final products and , the effective coefficient of green innovation R&D efforts by the manufacturer and the supplier and , the effective coefficient of digital construction promoting product green innovation by the manufacturer and the supplier and , and the influence coefficient of the greenness of intermediate products on the greenness of final products However, they are negatively related to consumers’ sensitivity to the retail price of final products and the decay factors of the greenness of intermediate and final products and .
According to Proposition 13, consumers can encourage the manufacturer and the supplier to conduct R&D on green products and to be more willing to carry out digital construction if they have a greater awareness of environmental protection and a greater preference for green products. The same effect can be obtained when greater effective conversion rates of the digital construction and green innovation R&D efforts of the manufacturer and the supplier are achieved, which eventually encourages the supplier and the manufacturer to make greener intermediate and final products. If consumers are more sensitive to the retail price, or the greenness of the intermediate and final products decays quickly, the manufacturer and the supplier will be much less inclined to engage in green innovation R&D efforts and digital construction and will lose their enthusiasm for making green final and intermediate products.
7. Managerial Implications
This study offers insightful information about how various incentives affect GSC members’ optimal decisions as well as the economic, environmental, and social benefits to the supply chain. We discuss the selection of incentive mechanisms and the management of various enterprises in different situations from the standpoint of the manufacturer and the supplier, based on the research findings.
For the manufacturer, we have the following recommendations. First, the manufacturer still needs to carry out green innovation to improve the greenness of final products in the long run for the purpose of fulfilling corporate social responsibility. Second, the manufacturer should implement incentive mechanisms to motivate the supplier to collaborate on green innovation, although the three incentive contracts have no impact on the manufacturer’s optimal decisions in relation to the green innovation R&D effort level and the digital capability level. The reason is that the three incentive contracts can increase the GSC’s environmental and social benefits while also raising the manufacturer’s revenue in most circumstances (when the reward coefficient is below a certain threshold). Third, as the core enterprise of the supply chain, the manufacturer has a good reputation and social status, so he should take on the duty of enhancing consumers’ environmental awareness. The manufacturer needs to increase the green publicity of products, stimulate consumer interest in purchasing eco-friendly goods, and improve the performance of the incentive mechanism. At this stage, paying more attention to green innovation R&D efforts will yield superior outcomes. In addition, the manufacturer should choose the optimal incentive contract according to the different stages of supply chain members’ cooperation to achieve the best incentive effect. Specifically, there is a lack of smooth communication and information transmission between the two players in the early stages of cooperation. At this point, the manufacturer should choose to implement the digital construction incentive mechanism, which can increase their earnings by facilitating the real-time transmission and sharing of information within the GSC. When the cooperation has reached stability, more efforts should be focused on green innovation R&D. The implementation of the R&D effort reward mechanism can achieve collaborative innovation.
The supplier should constantly adjust their tactics as a follower in accordance with the manufacturer’s decision. Whichever incentive mechanism the manufacturer chooses to carry out can improve the supplier’s green innovation R&D effort level, digital capability level, and profits. Thus, the supplier prefers incentive contracts and will actively cooperate with the manufacturer to take action. Secondly, the supplier should pay more attention to increasing the effective conversion rate of digital construction to product greenness to boost their own profits as well as environmental performance and social welfare when engaging in digital construction. At this stage, the digital construction reward mechanism has a greater motivating effect. Additionally, the supplier ought to aggressively increase investment in green innovation R&D, paying especial attention to the effective transformation outcomes of R&D investment in product green innovation, which can help the GSC’s collaborative innovation achieve better performance.
8. Conclusions and Future Research Directions
8.1. Conclusions
This study constructed a two-echelon GSC consisting of upstream suppliers and downstream manufacturers, who are responsible for green innovation of intermediate and final products respectively. To characterize the dynamic change of product greenness over time, this research used a differential equation to describe the greenness variations in intermediate and final products and the green innovation efforts and digital capability level of GSC members. in the research model, the manufacturer implemented three incentive mechanisms for the supplier to improve the greenness of final products. They were the greenness reward mechanism, the R&D effort reward mechanism, and the digital construction reward mechanism, which were all conducive to fostering collaborative innovation between the supplier and the manufacturer. Through constructing a differential game model, the optimal equilibrium decision making of the manufacturer and the supplier and the evolutionary trajectory of the economic benefits, environmental benefits, and social welfare of the GSC under the four scenarios were dynamically analyzed. The optimal decisions were compared and analyzed from the short-term and long-term perspectives through comparing the three incentive mechanisms with the non-incentive situation to identify the best incentive mechanism. Based on the outcomes of the analysis and numerical simulations, the following conclusions are drawn:
(1) On the whole, the greenness reward mechanism, the R&D effort reward mechanism, and the digital reward mechanism all have a motivational effect on the supplier, which is conducive to green collaborative innovation between the supplier and the manufacturer. In contrast to the non-incentive scenario, the three incentive mechanisms can raise the greenness of intermediate and final products, improve the supplier’s green innovation R&D effort level and digital capability level, boost product sales, and increase the economic benefits, environmental benefits, and social welfare of the GSC.
(2) Throughout its evolution, the R&D effort reward mechanism can bring more environmental benefits and social welfare to the GSC. However, in terms of economic benefits, the digital construction reward mechanism has the strongest incentive effect in the initial stage. Therefore, at this point, it is imperative to aggressively promote both parties’ digital construction and work together to strengthen their respective digital capabilities. In the long run, the R&D effort reward mechanism will bring more profits to the manufacturer, supplier, and the whole supply chain than the other two incentive mechanisms. It is more conducive to improving the economic benefits of the GSC through actively promoting the supplier’s green innovative R&D efforts for intermediate products.
(3) An increase in the incentive coefficient can bring more environmental benefits and social welfare to the GSC under the three incentive mechanisms. However, economic benefits have different laws; no matter what kind of incentive mechanism the manufacturer adopts, its profits will decrease and eventually reach zero if the incentive coefficient is too large. Only when the incentive coefficient is within a certain threshold range can the best incentive effect be achieved. Out of the three incentive mechanisms, the R&D effort reward mechanism has a better incentive effect than the other two.
(4) In this study, the stronger the promotion of the digital capability to green innovation, the greater were the economic benefits, environmental benefits, and social welfare of the GSC under the four scenarios. Among them, the value conversion rate of the digital capability to green innovation of the supplier had a stronger impact on the economic benefits of the GSC. Consequently, it is imperative for the manufacturer and the supplier to strengthen their digital construction, which particularly fosters green innovation and enhances the value conversion rate of digital capabilities.
(5) According to the current study results, the economic benefits, environmental benefits, and social welfare of the GSC increased with the strength of the consumer’s preference for greenness under the four scenarios. As a result, the manufacturer and the supplier should increase publicity of the greenness of intermediate and final products, raise consumers’ green awareness, and improve their green preference. When consumers’ preference for greenness is low, the incentive effect of the greenness reward mechanism is optimal. When consumers’ preference for greenness is high, the R&D effort reward mechanism is the best option for the manufacturer.
8.2. Limitations and Future Directions
There are several promising directions in this research that should be examined and investigated more in the future. This study solely examines certain market demand. However, given the impact of numerous uncontrollable factors, the market demand may be ambiguous. In light of this, future research may explore the green collaborative innovation incentive mechanism between manufacturers and suppliers in the context of uncertain demand. In addition, due to the high-risk nature of green innovation and the fact that different supply chain participants have varying attitudes towards risk, it can be argued in future research that manufacturers and suppliers have a risk appetite (risk-averse or risk-averse). Additionally, we consider the manufacturer as the chain’s leader in this study. Further research under different power structures is necessary to see whether the findings of this work are relevant when the supply chain’s power structure varies.