产业生命周期不同阶段的最优集体创新网络结构

本文采用仿真方法从创新效率的角度对产业生命周期不同阶段下的最优集体创新网络结构进行了研究。研究发现,在产业生命周期的导入期,以较高的平均聚集系数为特征的规则网络具有最高的集体创新效率;在产业生命周期的成长期,以较高的小世界系数为特征的小世界网络具有最高的集体创新效率;当产业生命周期进入成熟期以后,以较短的最短路径长度为特征的随机网络具有最高的集体创新效率。本文通过对上述结果的进一步分析得出,上述结果是由以下三个层次的原因造成的。第一,产业生命周期的不同阶段具有不同的产业知识特征和技术机会。第二,产业知识特征会影响产业内部的知识流动和企业实现知识重组的能力,而技术机会的多少会影响企业搜寻并发现创新机会的能力。第三,较高的网络平均聚集系数有利于促进知识流动,而较短的平均最短路径长度有利于企业搜寻并发现创新机会。最后,本文提出了以上结论对创新政策制定者的一些重要启示。

Optimistic Structures of Collective Innovation Networks in Different Stages ofIndustry Life Circle

The optimistic structures of collective innovation networks in different stages of industry life circle with simulation methods are explored in this paper. The result shows that in the initial stage of industry life circle, regular networks with high average clustering coefficient are the most efficient for innovation; In the growth stage of industry life circle, small-world networks with high small-world coefficient are the most efficient for innovation; In the developed stage of industry life circle, random networks with low average path lengths are the most efficient for innovation. With further analysis, the reasons of these results are discovered,which are divided into three hierarchies as below. Firstly, knowledge features and technical opportunities are different in different stages of industry life circle. Secondly, knowledge features influence the flows of knowledge and determines enterprises' ability of recombination of knowledge, whereas technical opportunities influence the seeking and discovering of innovation opportunities by enterprise. Thirdly, high average clustering coefficients of structures conduce to better knowledge flows, whereas short average shortest path lengths make innovation opportunities easier to find. At last, some important advices are proposed to innovation policy makers.