论文部分内容阅读
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。
This paper uses simulation method to study the optimal collective innovation network structure in different stages of industrial life cycle from the perspective of innovation efficiency. The study found that the rule network characterized by a higher average aggregation coefficient has the highest collective innovation efficiency in the lead-in period of the industrial life cycle. During the growth of the industrial life cycle, the small world characterized by a relatively high small-world coefficient The network has the highest collective innovation efficiency. When the industrial life cycle is mature, the random network characterized by the shortest shortest path length has the highest collective innovation efficiency. Through further analysis of the above results, this paper concludes that the above results are caused by the following three levels. First, different stages of the industrial life cycle have different industrial knowledge characteristics and technical opportunities. Second, the characteristics of industrial knowledge affect the internal knowledge flow within industries and the ability of enterprises to reorganize their knowledge. The number of technical opportunities affects the ability of enterprises to search for and discover innovative opportunities. Third, a higher average agglomeration coefficient facilitates the flow of knowledge, while a shorter average shortest path length facilitates business search and discovery of innovative opportunities. Finally, this paper presents some important implications of the above conclusions to policymakers of innovation.