Technology Transfer System

The concept of “technology transfer” was first proposed at the United Nations Conference on Trade and Development in 1964. Since the 1980s, the theory of technology transfer systems has been widely discussed by governments, universities, and enterprises around the world. Yu et al. [10] believed that technology transfer is the transfer from one place to another. In different countries, regions, industries, enterprises, and other fields, people and information are used as carriers to carry out the input and output of technical knowledge. At present, scholars’ research on technology transfer systems mainly revolves around the following two levels: First of all, relevant research is carried out around its connotation and function. Carayannis et al. [11] explored the development process of the theoretical system, policies, and practices of technology transfer. However, Barros et al. [12] reviewed the literature from 2013 to 2018 and found that most of the coupling relationship between knowledge management and technology transfer occurred in the private sector, and academic and research institutions were an important force to stimulate industrial technology transfer. System regulation of technology transfer can be realized through university-enterprise R&D cooperation [13]. This creates a dynamic equilibrium relationship between independent innovation, technology transfer, and economic growth. From a market-oriented perspective, the effect on short-term economic growth is not significant, but it has a significant effect on long-term economic growth [14]. Wang et al. [15] believed that there is still room for further exploration in the development of China’s technology transfer system, pointing out that the development law of China’s technology transfer system shows a trend of functional separation from the initial stage to the development stage of functional integration, and then to the mature stage of functional collaborative evolution.

In addition, based on the relevant research on domestic and international technology transfer systems, Wei [16] believed that China urgently needs to promote changes in technology legislation as soon as possible in order to promote the evolution of China’s technology transfer system towards an ecosystem. And with the development of heterogeneity in the technology transfer system, China’s technology transfer institutions are becoming increasingly specialized [17]. As an important carrier of technology transformation, the technology market is an important part of China’s technology transfer system. Qiu [18] emphasized that the development of China’s technology transfer system should be strengthened from the perspectives of the technology supply side, market standardization, institutional guarantee, and strengthening talent support. Taking the international technology transfer experience of typical countries (regions) such as the United States, the European Union, Southeast Asia, and the Middle East as inspiration, it can provide important insights for the development of China’s technology transfer system [19]. Töpfer et al. [20] analyzed the structural dynamics of the German cluster innovation network, forming a technology transfer system that affects other enterprises in the cluster through government support for a central enterprise. Of course, due to the significant impact of social technology on the success of technology transfer systems, the technology transferred by developed countries needs to be modified in order to be promoted and applied in specific developing countries [21]. Through smooth technology transfer channels, economic circulation and development will be promoted. Yu et al. [22] incorporated public expectations and expert guidance into the model to study the impact of technology transfer on the economic circulation model from the perspective of the resource base and concluded that technological cooperation, digital capacity development, technology sharing, and stakeholder participation are the core influencing factors for realizing technology transfer.

Digital Innovation Ecosystem

Digital empowerment has broken through the traditional unidirectional linear ‘chain innovation’ process of green innovation from knowledge accumulation, basic research, and applied research [23]. Many scholars at home and abroad have shown that the transformation of the innovation ecosystem towards digitalization has taken on a new organizational form [24]. Digital empowerment has promoted the overall evolution of the green innovation system to a higher level of value creation, integrating multi-dimensional aspects such as innovation, ecological space, resources, functions, and relationships, realizing the leap in technological innovation capabilities [25]. With the development of digitalization, networking, and intelligent technologies, the fourth industrial revolution represented by the new generation of information technology is rapidly rising; many enterprises have built digital innovation ecosystems in the process of carrying out green innovation activities, such as Alibaba’s e-commerce innovation ecosystem, Huawei’s HarmonyOS application innovation center, Baidu’s Apollo autonomous driving innovation ecosystem platform, et al. [26]. Corporate green innovation needs to use digital logic, driven by existing digital technology and empowered by the digital innovation ecosystem, proactively cutting into the industries where the companies are located to achieve green innovation [27].

As the digital innovation ecosystem becomes richer, it promotes resource flow and exchange through open innovation ecology, realizes data openness in an ecological way, fully releases the value of data elements, and promotes the orderly opening of green resources [28]. Focusing on the real scenario requirements of the co-development of multiple innovative entities, relying on the role of digital empowerment, promoting the deep integration of the Internet, big data, artificial intelligence, and corporate green innovation, and providing development plans for green innovation systems in enterprise R&D design, production manufacturing, operation and maintenance, and business management. At the same time, digitization empowers green innovation, which can deepen innovative cooperation among enterprises, universities, research institutions, and governments, and promote cross-border communication and collaboration using digital technology, accelerating the networking and system integration of the green technology innovation process [29]. Therefore, digital empowerment of green innovation is conducive to enhancing the concentration of green innovation and promoting the realization of knowledge and value in the innovative green system.

Existing Research Gaps and Insights

Existing research on the evolution law of knowledge diffusion in digital innovation ecosystems, collaborative innovation, multi-subject technology diffusion, and other studies is relatively abundant, but there are the following shortcomings: (1) Multi-subject technology transfer in the scenario of digital innovation ecosystems presents new features, but few studies have explored the specific impacts of digital innovation ecosystems and have not yet analyzed the impact relationship and operation mechanism in depth with specific cases. (2) Multi-subject technology transfer considers the collaboration and matching between core enterprises and other subjects, but the existing literature does not explore the interactive behavior of multi-subjects in depth and lacks the comprehensive operation mechanism of system integration, which limits the theoretical explanatory ability to a certain extent. (3) Multi-subject technology transfer relies on the diffusion of knowledge in the system; the existing research is mostly carried out from a single perspective, such as the perspective of resource sharing, and does not fully take into account the synergistic role of multiple elements in the ecosystem. With the development of digital innovation ecosystems, the flow of resources and technology sharing is more frequent, the main body of cooperation is closer, the efficiency of innovation is significantly improved, and then building the knowledge diffusion game model to study the transfer of technology is a better framework. In view of this, this paper takes Xiaomi’s digital innovation ecosystem as a case object and constructs a game model based on knowledge diffusion to carry out research on the coordination and management of multi-subject technology transfer.

Game Model of Technology Transfer Between Xiaomi and Partner Enterprises

a. Model hypothesis

Hypothesis 1: Xiaomi can effectively promote the realization of national strategic needs, industry leadership, and user centrism through joint technology breakthroughs and green technology sharing. This method not only promotes the development of green innovation activities but also facilitates the transfer of green technology.

Hypothesis 2: From the perspective of bounded rationality, Xiaomi will tend to choose the option that maximizes benefits when faced with multiple choices. This may lead them to choose the green technology that maximizes their own interests for transfer during green innovation activities.

Hypothesis 3: Combining evolutionary game theory, Xiaomi will continuously adjust and optimize their strategies in the process of green innovation activities, seeking to maximize benefits while trying to find the best combination of strategies to achieve their goals.

Hypothesis 4: In the face of the laws of digital economy development, Xiaomi’s green innovation activities and their results will be influenced by the market environment and competitive situation, necessitating adaptation and optimization of its innovation strategy.

These hypotheses provide a framework that can be used to explore in depth the strategies and methods of Xiaomi’s green innovation activities against the backdrop of the digital economy. The specific parameter settings are as follows:

(1) Let Xiaomi be denoted as A₁. The collaborative innovation enterprise is A₂.

(2) There are two choices in the collaborative innovation process of A₁ and A₂ technology sharing and non-sharing. The technical resources of A₁ and A₂ are H₁ and H₂. The resource prices before sharing are P₁ and P₂, P₁>P₂>0.

(3) The degree of resource sharing between A₁ and A₂ is α and β. L₁ and L₂ are resource income distribution coefficients. The green innovation system, constituted by A₁ and A₂, has facilitated technology transfer through the sharing of resources, yielding the respective benefits of L1H1αH2β and L2H1αH2β.

(4) The cost of resource sharing for A₁ and A2 is C₁ and C₂ respectively.

(5) If A₁ shares and A₂ does not share, A₂ gets extra revenue of _p2H2^β . Similarly, if A₂ shares and A₁ does not share, A₁ can obtain additional revenue p₁H^α₁ . Of course, A₁ and A₂ have certain unknown risks in the process of cooperative innovation, and the risk factor is F₁ and F_2. The payoff matrix for A₁ and A₂ is shown in Table 1.

b. Model solution

Let’s assume that the return of A₁ is E₁ when it is shared and E₂ when it is not shared:

A₁ income can be obtained:

Then the replication dynamic equation of A₁ is:

Similarly, the replication dynamic equation of A₂ is:

According to the stability principle of the replicated dynamic differential equation and the properties of the evolutionary stability strategy, the points of evolutionary stability are (0,0), (0,1), (1,0), (1,1).

The Jacobian matrix can be used to analyze its stability as shown in Table 2.

Fig. 4.

Time dependent variation of y at x = 0.1.

As can be deduced from Table 2, only the points (0,0) and (1,1) represent ESS states; that is, within the green innovation system, the choice of enterprises to either share technology or refrain from doing so constitutes a Nash equilibrium.

c. Simulation analysis

In this paper, Matlab software is used to analyze the built model. According to the research results of He et al. [30], the simulation is assigned as shown in Table 3.

Firstly, this study fixes the changing x values and sets x = 0.1 and x = 0.9, respectively, representing A₁’s unshared and shared states. The y threshold is set at 0.2, 0.4, 0.6, and 0.8, respectively, corresponding to the size of A₂’s sharing tendency. The details are shown in Figs. 4-5. Fig. 4 shows that A₁ has a higher tendency not to share when x = 0.1. The y threshold is set at 0.2, 0.4, 0.6, and 0.8, respectively, corresponding to the size of A₂’s sharing tendency. That is, the larger the value of y, the slower the decline trend will be. If A₁ chooses not to share, then the optimal strategy for A₂ is to also not share resources. Thus, the equilibrium point of the game between both sides is stable at (0,0). As shown in Fig. 5, A₁ has a higher tendency to share when x = 0.9. The relationship between the changes in the image of y and time shows a positive correlation, meaning the longer the cooperation time, the more obvious the degree of image degradation. A₂ prefers sharing strategies, which can yield more benefits. At this point, the equilibrium point of the game between the two parties is (1,1).

Fig. 5.

Time dependent variation of y at x = 0.1.

Fig. 6.

Time dependent variation of x at y = 0.1.

Fig. 7.

Time dependent variation of x at y = 0.9.

Similarly, when y = 0.1 and y = 0.9, they represent A₂ unshared and shared states. The x threshold is set at 0.2, 0.4, 0.6 and 0.8, which respectively correspond to the size of the A₁ sharing tendency. The details are shown in Figs. 6-7. Fig. 6 shows that when y = 0.1, A₂ has a higher non-sharing tendency. The threshold changes of y, under the strategic goal of maximizing profits pursued by the enterprise, becomes more severe as the threshold increases over time. At this point, the optimal strategy for A₁ is not to share. Therefore, the equilibrium point of the game between both parties is stable at (0,0). Similarly, in Fig. 7, when y = 0.9, A₂ has a higher sharing tendency, and A₁ tends to choose the sharing strategy. The equilibrium point of the game is (1,1). Combining the sharply declining image trajectories in Figs. 6-7, it is necessary to comprehensively consider the game relationship between both parties. In the green innovation system, both parties are prone to trust crises and the risk of technology leakage during the cooperation process. At this time, the optimal strategy of both parties in the initial cooperative innovation is (1,1). With the passage of time, the distrust between the two parties increases, and the optimal strategy becomes (0,0). The above simulation process further verifies the inference of the game model.

Technology Transfer Game between Xiaomi and Academic and Research Institutions

a. Game hypothesis

Hypothesis 1: Xiaomi and academic research institutions can more effectively promote the development of the green innovation system through collaborative innovation. This type of collaborative innovation not only promotes technology transfer but also improves R&D efficiency and innovation quality.

Hypothesis 2: Xiaomi and academic research institutions, based on bounded rationality, will tend to choose the option that maximizes benefits when faced with multiple options. This may lead them to choose the technology that maximizes their own interests for transfer during the collaborative innovation process.

Hypothesis 3. In the collaborative innovation process of Xiaomi and academic research institutions, evolutionary game theory may play a key role. That is that both parties are always pursuing maximum benefits while trying to find the best strategy combination to achieve this goal.

Hypothesis 4: The evolution and optimization of the green innovation system will depend on how Xiaomi and academic research institutions adjust and optimize their collaborative innovation strategies to adapt to the constantly changing market environment and competitive situation.

The above assumptions will serve as the theoretical foundation and guiding principles of this study. By conducting in-depth analysis and discussion, we hope to provide valuable theoretical and practical guidance for enterprise-academic institution collaborative innovation in the digital economy. The specific parameter settings are as follows:

(1) Xiaomi is represented as A₁ in this study, while academic and research institutions are represented as A₃.

(2) Academic and research institutions have

technological research teams and research advantages, while Xiaomi has advantages in the market, information technology, and knowledge. There are two types of cooperative innovation: low-level collaboration and high-level collaboration.

(3) The cost of low-level collaborative innovation is C₃, while the cost of high-level collaborative innovation is C₄.

(4) The cost of low-level collaborative innovation for the Xiaomi Company is C₅, while the cost of high-level collaborative innovation is C₆.

(5) Both parties choose I₁ for the benefit of low water collaborative innovation, and I₄ for the benefit of high-level collaborative cooperation. The academic and research institutions adopt low-level collaborative cooperation, while Xiaomi adopts high-level collaborative cooperation; the income is I₂. The academic and research institutions adopt high-level cooperation, while Xiaomi adopts low-level cooperation, the income is I₃(I₄>I₃>I₁, I₄>I₂>I₁).

(6) Both parties agree on the content of the technology transfer agreement in advance. In the process of establishing a green innovation system, academic and research institutions can transfer technology to Xiaomi Company. The proportion of benefits distributed by academic and research institutions is ω. The proportion of benefits allocated by Xiaomi Company is υ. ω, υ>0, ω + υ = 1.

Based on the above assumptions, the payoff matrix of the game can be deduced, as shown in Table 4.

b. Model solution

Let the low-level collaborative innovation benefit function for Xiaomi Company be E₄, and the high-level collaborative innovation benefit be E₅.

Therefore, the replication dynamic equation W(x) of the Xiaomi Company is:

Similarly, the replication dynamic equation W(y) of academic and research institutions is:

According to the stability principle of the replicated dynamic differential equation and the properties of the evolutionary stability strategy, the ESS points of the evolutionary stability strategy are (0,0), (0,1), (1,0), (1,1),

The stability of formulas (8) and (9) can be analyzed by using the Jacobian matrix, as shown in Table 5.

As can be seen from Table 5, (1,1) becomes the only stable ESS point and the optimal Nash equilibrium solution.

c. Simulation analysis

In this paper, Matlab software is used to analyze the built model. According to the research results of Sun et al. [31], the simulation is assigned as shown in Table 6.

For the convenience of research, the changing x value is fixed. In this paper, the constants x = 0.1 and x = 0.9 respectively represent the low and high synergy states of A₃. The threshold value of y is set at 0.2, 0.4, 0.6 and 0.8, respectively corresponding to the degree of A₁ cooperative cooperation. The details are shown in Figs. 8-9.

From Fig. 8, it can be seen that when x = 0.1, academic and research institutions adopt low-level collaboration. The threshold change of y evolves over time, and the trend of y is positively correlated with the threshold size. At this point, Xiaomi can achieve the maximum benefits by choosing a high-level collaborative cooperation strategy. As can be seen from Fig. 9, when x = 0.9, academic and research institutions adopt high-level collaborative cooperation. The trend of y shows a positive correlation between time and the degree of collaboration. At this point, the Xiaomi Company chooses a high-level collaborative cooperation strategy to maximize profits. In summary, no matter whether x = 0.1 or x = 0.9, Xiaomi’s strategy is high-level collaborative cooperation. By comparing the returns in Figs. 8-9, it can be concluded that the Nash equilibrium solution of the game between the two parties is (1,1).

Fig. 8.

Time dependent variation of y at x = 0.1.

Fig. 9.

Time dependent variation of y at x = 0.9.

Similarly, when y = 0.1 and y = 0.9 are set respectively, it represents the low synergy and high synergy states of A₁. The threshold value of x is set at 0.2, 0.4, 0.6 and 0.8, respectively corresponding to the degree of A₃ cooperative cooperation. The details are shown in Figs 10-11.

From Fig. 10, when y = 0.1, the x curve shows a positive correlation between collaborative intensity and time evolution. At this point, academic and research institutions can maximize their benefits by choosing high-level collaboration. In Fig. 11, the trend is similar, and academic and research institutions can also maximize their benefits by choosing high-level collaboration. Therefore, it can be concluded that when y = 0.1 and y = 0.9, the optimal strategies for academic and research institutions are both high-level collaborative cooperation. Based on the above analysis, the Nash equilibrium solution of the game between the two parties is (1,1). These results further validate the derived game model.

Fig 10.

Time dependent variation of x at y = 0.1.

Fig. 11.

Time dependent variation of x at y = 0.9.

Research Conclusion

In the era of the digital economy, the innovation ecosystem tends towards multi-entity collaborative innovation, which not only accelerates the transfer of digital technology, but also promotes the knowledge flow in aspects such as processes, techniques, and business models under the background of digital technology, forming a strong digital innovative vitality. On the basis of constructing the technology transfer mechanism of the digital economy’s empowering green innovation system, this paper explores the impact of technology transfer in the digital economy environment by focusing on the competitive game relationships among multiple entities within Xiaomi’s green innovation system. The main conclusions are as follows:

(1) The digital economy empowers the green innovation system to promote the development of technology transfer. Under the environment of the digital economy, innovators can use the digitization level of the digital technology green innovation system to map the portraits of green innovation entities and match suitable technological achievements, innovation needs, and applicable policies. This fully mobilizes various innovation entities and technology transfer carriers, increases knowledge flow between entities within the system, promotes technology transfer digital links between multiple entities, and thus promotes the continuous development of the green innovation system.

(2) Within the green innovation ecosystem, the intensity of sharing willingness between enterprises and cooperation time shows a negative correlation. It is found that under the influence of businesses pursuing a strategy of maximizing profits, technology transfer behavior within the green innovation system empowered by the digital economy shows a decreasing trend with the development of cooperation time. When one party has a smaller sharing intention, the quality of technology transfer between enterprises will face a trust crisis, causing the cooperation innovation time to shorten. By adjusting the intensity of the sharing willingness threshold, it is found that the trust crisis in green technology transfer is delayed, and the benefits obtained are greater than those with a smaller sharing intention, which can promote the sustainable development of technology transfer in the digital economy.

(3) In the green innovation system, the intensity of sharing willingness between enterprises and academic research institutions and the time of collaborative cooperation show a positive correlation. Research shows that the stronger the resource sharing willingness between enterprises and academic research institutions, the longer the cooperation time between the two parties. This association indicates that in the digital economy environment, thanks to the widespread application of digital technology, the digital economy has improved the accessibility and shareability of information within the green innovation system, thereby accelerating the flow of knowledge between enterprises and academic research institutions and the transfer of green technology. This will contribute to the realization of green technology innovation and environmental sustainability within the green innovation system.

Research Implications

(1) Strengthening the linkage between enterprises and government departments around the transfer and transformation of enterprises’ digital innovation technologies. Combined with the actual situation of enterprise green innovation, technology transfer, and transformation, strengthen coordination and optimize process management. At the same time, the government and other relevant departments should strengthen communication between the different enterprises, address the requirements of different enterprises, and formulate specific institutional measures to guide the development of enterprise digital innovation, and to promote the healthy development of the enterprise innovation ecosystem.

(2) Improve the support of financial institutions and information intermediaries for enterprise digital technology transfer. By absorbing the development experience of advanced digital innovation ecosystems and taking into account the actual characteristics of the regions where different enterprises are located, it will formulate special support measures to promote the digital development of enterprises and guide the transfer of financial and information resources to the field of digital innovation.

(3) Establish a sound assessment system for the transfer of enterprise digital innovation technologies and realize the implementation of policies and standardized management of enterprise digital technology transfer and transformation. There should also be a focus on incentivizing government departments to carry out supervision and innovation effect tracking and evaluation to better promote the implementation of policies, as well as giving full play to the role of universities and research institutes in the field of digital innovation to better empower the transfer of digital technology among multiple actors.