企业开放式创新生态系统种群共生关系与演化机理研究

创新生态系统已成为学术界研究热点。已有文献主要聚焦创新生态或开放式创新，将两者结合探讨其演化机理的研究较少。基于国内外开放式创新相关研究，结合种群生态学理论，构建开放式创新生态系统演化模型，并运用MATLAB仿真对企业开放式创新生态系统的演化规律进行深入挖掘。结果表明：企业开放式创新生态系统演化过程包括初创期、成长期和成熟期3个阶段，且演化的4个序参量呈现如下态势：生态网络规模在3个阶段呈现逐步上升趋势，在成熟期达到最大值；生态网络密度在3个阶段呈现缓慢增长的S型趋势；生态网络平均聚类系数在初创期和成熟期增长缓慢，在成长期增长迅速；核心企业在3个阶段均占据优势生态位，并在成熟期达到最高位。

The Population Symbiosis and Evolution Mechanism of Open Innovation Ecosystem of Firms

Research on the innovation ecosystem has become a hot topic in academia. Existing literature mainly focuses on innovation ecology or open innovation, but relatively few studies combine the two topics and explore their evolution mechanism. Furthermore, most of the literature conducts qualitative analysis on the evolution of innovation ecosystem from the theoretical perspective, while the evolution mechanism of open innovation ecosystem remain under researched. To take one step toward addressing this gap in prior research, this study brings a new perspective on the mechanism of the open innovation ecosystem by using the simulation approach.#br#Following the theory of population ecology and the prior research on open innovation at home and abroad, this study analyzes the evolution mechanism of firms' open innovation ecosystem by constructing an evolution model of the open innovation ecosystem. According to population ecology, this study uses four order parameters to measure the evolution mechanism of the open innovation ecosystem, including eco-network size, eco-network density, eco-network clustering coefficient, and ecological niche. By using MATLAB simulation, this study adopts the BA model with the scale-free network to construct the evolution mechanisms inter-period. Moreover, each period is required to go through 90 cycles of evolution. To reduce the fluctuation and random error, this study takes the average value of the same group of parameters repeated 10 times as the final simulation result. The results of visual simulation show the process that the four parameters evolve from generation to development.#br#The findings show that the evolution process of firms’ open innovation ecosystem includes three periods: viz., the start-up period, the growth period, and the mature period. First, there are genetic behaviors among innovation members and commensalism relationships between populations in the open innovation ecosystem. More specifically, great innovation genes (e.g., knowledge, resources, and technology) of the core members constantly spill out and are accepted by other members through imitation learning. Second, there are variation behaviors among the innovation members and symbiotic relationships between the populations in the open innovation ecosystem so as to acquire new resources, knowledge, and technologies by breaking old conventions. Third, according to genetic and variation behaviors, each innovation member will select innovative resources such as new ideas, new technologies, and new models generated in the process of variation. In other words, there are mutualistic symbiosis relationships between populations. Moreover, the findings reveal that the four order parameters of evolution show the following trends: the scale of the ecological network shows a gradual upward trend in three stages and reaches its maximum in the mature period; the density of the ecological network is in a slow-growing “S” trend in the three stages; the average clustering coefficient of the ecological network grows slowly during both the start-up and the mature period, and grow rapidly during the growth period; while core enterprises occupy the dominant ecological niche in all three stages, and reach the highest in the mature period.#br#In addition, this study examines the evolution mechanism of the open innovation ecosystem by establishing a simulation model to analyze the variation of parameters, thus contributing to the literature on the evolution of the open innovation ecosystem.It divides the development of the open innovation ecosystem into three different periods (the start-up period, the growth period, and the mature period), and analyze the evolution behaviors of ecosystem members, the relationships between members, as well as the characteristics of the innovation ecosystem. Hence, the simulation findings shed light on this complex issue about evolution mechanisms and expand the boundary of open innovation ecosystem research. The results also have significance for policymakers and enterprises to promote a more innovative environment in practice. On the one hand, the government should promote open innovation ecosystem by adjusting the industrial structure and distribution, and it should establish new institutions of cross-boundary innovative products and services, encourage new models of open innovation (including universities, research institutes, and enterprises), and improve the new business environment. On the other hand, enterprises need to actively integrate into the open innovation ecosystem by increasing their innovation investment and thus maintain exchanges of knowledge, resources, and technologies with the other members and explore diversified modes of cooperation in the ecosystem.#br#