基于企业学习策略的集群持续创新机制及实证研究

由于集群网络的复杂性,集群创新机制的研究目前较为缺乏,有限的研究也往往假设集群企业具有同质性.本文提出,集群企业依据其在集群网络中的位置采用不同的学习策略,领导型企业更加侧重于探索式学习,跟随型企业更加侧重于利用式学习,两者的分工和异质性互动构成了集群持续创新的内在机理.本文在个体和企业层次双重学习研究的基础上,结合集群特有的组织条件,提出集群双重学习的机制,据此将集群企业进行分类,并建立集群中企业学习的理论模型,接着选取国内某地化工产业集群的236家企业作为样本,运用结构方程模型对集群中企业进行分类检验和比较研究.研究结果表明,集群中不同类型企业的学习策略存在差异,集群网络的作用是产生这一差异的可能原因;集群中不同类型企业的学习策略具有互补性,有利于企业间互动和集群的持续创新.

The Sustainable Innovative Mechanism of Cluster Based on Learning Tactics of Firms: An Empirical Study

There were few empirical studies on the innovative mechanisms of industrial clusters recently in the literature because the nature of inter-firm networks of clusters are highly complex and these limited studies commonly assume that firms in the industrial cluster are homogeneous. However, such hypothesis is clearly inconsistent with the economic realities. In this paper, we propose that firms in the industrial cluster adopt different learning tactics according to their network positions in the inter-firm network of cluster. The leading firms in the industrial cluster depend more on explorative learning, while the following firms in the industrial cluster depend more on exploitive learning. The division and heterogeneous interacting of different learning ways between leading firms and following firms forms the sustainable innovative mechanism of the industrial cluster. Based on these, in this paper we first analyze and summarize the existing research achievements of ambidextrous learning at the organizational level of individual and firm in the literature. Second, we deduct the mechanism of ambidextrous learning at the organizational level of cluster based on the research achievements of individual and firm level and the specific conditions of cluster. Third, we classify cluster firms into leading firms and following firms according to the mechanism of ambidextrous learning in the in-dustrial cluster and the number of research and development investment of cluster firms. Based on the above three, we next propose a theoretical model of the relationship among ties strength, ties quality, explorative learning, exploitive learning, and innovative performance of cluster firm. Finally, our theoretical predictions are tested using data collected from 236 cluster firms in the chemical industry in Luhe District of Nanjing City, which is the capital of Jiangsu Province in China, and we use the approach of Structural Equation Model to analyze these samples. Our findings via numerical experiments suggest that the learning tactics of cluster firms are different and the inter-firm network of cluster is an important source for firms learning. Furthermore, it also shows that different tactics of cluster firms complement each other, and are beneficial to the interacting of these firms and the sustainable innovation of industrial clusters.