With the rapid development and wide popularisation of new technologies such as artificial intelligence, big data and internet, the links between innovation subjects have become closer. Open innovation is increasingly becoming the mainstream paradigm. In this environment, the boundary of participants in the process of innovation input, output and commercialisation is increasingly blurred [1]. At present, innovation has entered the Innovation 3.0 paradigm with the innovation ecosystem as the core [2,3,4]. In the process of building an innovation ecosystem, organisational resources, scientific and technological resources, network resources and system resources interact and coevolve [5]. The process is not determined by a single mechanism but by all the sub-mechanisms and their interactions and linkages [6]. Among them, the relationship is complex; competition and cooperation blend mutually. The innovation ecosystem combines new ideas and technologies with products, services and production processes. It makes full use of technology, talents and other scientific and technological resources and makes use of technology innovation platform technology to conduct data mining and intelligent matching, which greatly improves the efficiency of resource utilisation and diffusion. In China, economic development entered the new normal, innovation-driven development has become the most fundamental driving force for development. At present, the economics of China aggregate ranks top in the world, but the technological level is generally not high. The contribution of science and technology to economic development is far lower than that of developed countries [7, 8].
To change the backward situation of science and technology, seize the development opportunities brought by the new round of scientific and technological revolution, China proposes the sharing economy model. This raises higher requirements for the development of enterprises. As an essential factor for enterprises to maintain competitive advantages, technological resources, especially heterogeneous technological resources, are of strategic significance to enterprises’ innovation activities. More and more enterprises choose resource sharing strategy to seek additional benefits and market competitiveness. As a key element of innovation, technology has become what you want it to be. In the innovation ecosystem, the main innovation entities include enterprises, universities, scientific research institutions, and so on.
The supporting entities include government, financial and intermediary agencies. They gain technological advantages through competition and cooperation with each other.
In view of the importance of technology sharing, scholars have conducted a lot of research. From the perspective of patent, some scholars believe that technology sharing is difficult to be realised due to the monopoly of patent. This not only hinders the transfer and implementation of innovation achievements but also brings obstacles to the development of existing shared projects; therefore, it is necessary to build a patent open licence platform in combination with internet and other technologies so as to not only reserve patent rights for technology owners but also promote technology transfer and implementation [9]. In view of the research of technology sharing process, most scholars advocate the idea of evolutionary game. For example, Chen [10] studied the ecological niche evolution process of strategic emerging industries by using evolutionary game theory and found that cost, government subsidy, expected revenue and so on have an important influence on the niche evolution of strategic emerging industries. Zhao et al. [11] believed that resource sharing should be analysed as a decision-making process, which is embodied in the military–civilian integration and collaborative innovation. Other scholars believe that it is also reflected in cross-regional and cross-disciplinary innovation [12]. By means of evolutionary game theory, Wang [13] analysed the influencing factors of the evolution path of knowledge sharing among employees in innovative organisations and pointed out that appropriate incentives are conducive to knowledge sharing within organisations. Ho et al. [14] studied the direct correlation between knowledge sharing behaviour and strategy by using evolutionary game theory.
Some scholars assume different scenarios, such as Dan et al. [15] studied the competitive and cooperative game strategy of inventory technology sharing retailers’ joint purchasing alliance by using inverse induction method under the stochastic demand situation, or from the perspective of weighing the relationship between patents and technology secrets, like Shen [16] studied patent synergies based on the evolutionary game model and proposed a way to share patents that could achieve better financial returns. Zhao [17] analysed and verified the impact of institutional constraints and inter-firm interaction learning on the protection strategy of evolutionary stability innovation under different situations through the evolutionary game and multi-agent modelling and simulation.
The current era is no longer the competition between enterprises but the competition between innovation ecosystems. If an enterprise only pursues temporary profit maximisation, it is likely to fail to keep up with its future development potential. In the end, it will be difficult to achieve sustainability and will inevitably end up in recession and bankruptcy. Based on the comprehensive analysis of the existing literature, it is found that the acquisition of technical resources is regarded as the benefit behaviour of enterprises, while the influence of the heterogeneity of enterprises is ignored. This is a potential impact, but it is also not negligible. Due to the difference of the enterprise itself, the technology absorption, adaptation, transformation and risk-bearing capacity of the enterprise are different. The accumulation of previous behaviours also leads to a different image of the enterprise in the system. The conditions for different enterprises to choose technology sharing strategy will be different. The government needs to guide and support different situations, which is more conducive to the stable development of the innovation ecosystem; therefore, this article analyses the technology sharing strategies of enterprises in the innovation ecosystem of technology-intensive enterprises combined with multiple implicit influencing factors.
There is no core enterprise in the flat innovation ecosystem and no big strength gap between enterprises. Therefore, we extract two stakeholders: enterprise 1 and enterprise 2. They both play producer roles in the innovation ecosystem, mainly by virtue of technology to obtain product advantages and market competitiveness. The total amount of enterprise technology includes stock and trading. In the innovation ecosystem, technology sharing platforms are the most efficient and convenient way to trade. The enterprise can select from two strategies: sharing technology (we can call ‘sharing’) and not sharing technology, just get technology from the platform (‘non-sharing’). In the case of ‘sharing’, the enterprises will share their technologies to the platform in a certain proportion and search complementary or additional technologies that they need. In the situation of ‘non-sharing’, the enterprises only gain but do not contribute, which is referred to as ‘hitchhiking’.
The innovation ecosystem includes
In the flat technology-intensive innovation ecosystem, there are two players.
Game players are willing to share technology because for technology-intensive enterprises, technology stock is in direct proportion to their competitiveness. However, after acquiring new technologies, enterprises will absorb and adapt them first and apply them to R&D and finally put new products into the market to obtain benefits. The whole process takes time to complete; we call this ‘time delay’. Considering the different absorptive capacity, adaptive capacity and application ability of the enterprise to technology, the time-delay coefficient will be different.
Enterprise 1 has more advantages in technical resources, knowledge and information than enterprise 2, but not much.
When both enterprises choose to share, they will attain more additional benefits than the situation where a single enterprise chooses to share.
When one enterprise shares technology and another does not, the non-sharing enterprise will be punished by ecosystem maintainers such as the government. In view of the enterprise image, penalties will increase as ‘hitchhiking’ increases.
The parameters and connotations of the game model are shown in Table 1.
Game model parameters and connotations.
Connotations | |
---|---|
Probability that enterprise 1 chooses to share technology | |
Probability that enterprise 2 chooses to share technology | |
New technical value after sharing | |
Original technical value of enterprise 1 | |
Original technological value of enterprise 2 | |
Technical value distribution coefficient after sharing 1 | |
Technical value distribution coefficient after sharing 2 | |
The technical stock of enterprise 1 | |
The technical stock of enterprise 2 | |
Proportion of technology shared by enterprise 1 | |
Proportion of technology shared by enterprise 2 | |
The time-delay coefficient of enterprise 1 | |
The time-delay coefficient of enterprise 2 | |
Punishment for enterprise 1 | |
Punishment for enterprise 2 | |
Constant | |
The image factor of enterprise 1 | |
The image factor of enterprise 2 | |
Technology sharing costs in enterprise 1, |
|
Technology sharing costs in enterprise 2, |
Annotation:
The payoff matrix among two players is established in Table 2. In Table 2, suppose that the willingness that enterprise 1 chooses the sharing strategy is
Payoff matrix.
Enterprise 2 | |||
---|---|---|---|
Sharing ( |
Non-sharing (1 − |
||
Enterprise 1 | Sharing ( |
||
Non-sharing (1 − |
|||
Supposing that
Since the local equilibrium point is not necessarily the evolution stability strategy (ESS) of system, the local stability analysis method of the Jacobi matrix is selected in this paper [18].
The Jacobi matrix of the evolutionary system is
Based on the Jacobi matrix, when an equilibrium point makes
Value of the Jacobi matrix at the local equilibrium
Stability point | ||||
---|---|---|---|---|
− |
0 | 0 | − |
|
0 | 0 | |||
0 | 0 | |||
− |
0 | 0 | − |
|
0 | 0 |
Among Table 3,
From Table 3, is the saddle point. It is not the stable strategy point because the trace of the determinant is 0. At the equilibrium points
The evolutionary stability analysis under different conditions.
Equilibrium point | Condition 1 | Condition 2 | Condition 3 |
---|---|---|---|
ESS | ESS | ESS | |
Instability | Instability | Instability | |
Instability | Instability | Instability | |
Instability | Instability | ESS | |
Saddle point | Saddle point | Saddle point |
ESS, evolution stability strategy.
when
when
To study the evolution process and rules of technology sharing strategy of enterprises in technology-intensive innovation ecosystem more clearly and deeply, we use a MATLAB simulation tool to simulate the evolution process [19,20,21,22,23,24]. The horizontal axis represents time (
According to the information of the actual production data of relevant enterprises, suppose that the technology stock of enterprise is
Enterprise has more technical advantages than enterprise 2, so suppose the technology transformation time of enterprise 1 is less than that of enterprise 2 and
When the time-delay coefficient of enterprise 2 is 0.5, and
When the time-delay coefficient of each enterprise is lower than its corresponding threshold, the enterprise will finally choose the sharing strategy. In the situation, the technology innovation platform will play its biggest role, and the innovation ecosystem will present a good situation of cooperation and symbiosis, promoting the further development of technology and economy; therefore, the ability of enterprise technology transformation plays an important role in technology transfer in the whole innovation ecosystem. All relevant parties should attach importance to improving the transformation level of technological achievements of enterprises, encourage enterprises to make good use of intelligent means, acquire complementary technologies and talents in various ways and strive to improve their ability to adapt to and absorb new technologies. At the same time, it is necessary to reduce the hesitation of enterprise decision-makers to avoid missing the opportunity of obtaining the best technology and marketing investment.
The image factor represents the impression an enterprise leaves on the outside world. The smaller the image factor, the better the image of the enterprise in the whole ecosystem. This factor is related to the previous behaviour of the enterprise, for example, enterprises often break promises, default or ‘hitchhiking’ and so on, then the system is bound to be very difficult to tolerate their bad behaviour again. Enterprises have also been punished more severely. In the principle of encouraging in the system, if an enterprise with a clean bill of health accidentally ‘hitchhiking’, the system will treat it lightly. The enterprise will be less penalised. The image factor increases with the increase in the number of bad behaviours.
Supposing that
When
By contrast, we find that when choosing strategy, superior enterprises have lower requirements on the technology sharing proportion of the other party. Due to its high technology stock and value, it is better able to take risks. Once the other enterprise gets ‘hitchhiking’, the enterprise can also bear the losses caused by the situation. On the contrary, weak enterprises will pay more attention to the technology sharing proportion of the superior enterprises. Only when the proportion is high enough, will be at ease to put technology on the shared platform, to share with the superior enterprises.
About the researches of sharing technology just focus on the dominant factors, and few of them can delve into the recessive factors (included time-delay and image factor). In this paper, the explicit and implicit factors are taken into account, and the mathematical model is constructed. We study the evolution process of enterprise technology sharing strategy in technology-intensive innovation ecosystem based on the evolutionary game model, supposing there two kinds of enterprises in the system. Then we discuss the equilibrium strategies under different conditions. Finally, we simulate the process of selecting a technology sharing strategy to study the influence of influencing factors on willingness. According to the game analysis and results, we can summarise as follows:
First, the shorter the time delay of technology transformation, the more likely the enterprise is to conduct technology transaction with other enterprises. This includes sharing technology and acquiring technology from technology innovation platforms. Because if an enterprise's ability to absorb and adapt to foreign technologies cannot keep up with the speed of technology flow in the innovation ecosystem, enterprises will not be able to bring expected benefits after sharing technologies with other enterprises, thus reducing their enthusiasm.
Second, the better the impression that the enterprise has made on other participants in the system due to its previous behaviour, the less punishment it will receive in the case of ‘hitchhiking’. Because the innovation ecosystem is not immutable, but in the evolution of balance, balance evolution has been in a dynamic state. Moreover, considering the limited rationality and the change of the enterprise's situation, there is no absolute cooperation or non-cooperation among the participants; therefore, the degree of punishment will change with the change of corporate image.
Finally, the proportion of technology sharing that is determined before an enterprise makes a decision critical. Especially for the early stage of the innovation ecosystem, the technology innovation platform is not mature because, at this point, companies are willing to wait-and-see. This is also affected by factors such as the size of decision-making enterprises, technology stock and degree of risk-taking. Enterprises with more technological advantages have lower requirements for other enterprises in decision-making.
In the context of the sharing economy, this study can help the enterprises carry on the strategy choice more clearly and help them more suitable to compete in the mode of innovation ecosystem. Also, some limited aspects should be considered in the future work. We will continue our exploration in the next step.