Research on Enterprise Innovation Persistence Patterns Recognition and Selection Based on BP Neural Network

Yutong  Ma; Yangyi  Meng

doi:10.4236/ajibm.2019.93045

Yutong Ma, Yangyi Meng

Abstract:

This paper divided Chinese manufacturing listed enterprises into six innovation persistence patterns according to the innovation persistence and ownership structure, and identified these six patterns by constructing BP neural network model. Under the condition of considering the scale of the enterprise, we made a further analysis on which innovation persistence pattern should be adopted in different growth stages. The results show that small-scale enterprises mainly adopt the centralized continuous innovation pattern and the centralized interval innovation pattern. Medium-scale enterprises mainly adopt the moderate interval innovation pattern. Large-scale enterprises mainly adopt the loose interval innovation pattern. Through the scale classification, we analyzed the business performance of listed enterprises, and we found that the centralized continuous innovation pattern, the centralized interval innovation pattern and the loose continuous innovation pattern are the most effective patterns respectively for small-scale, medium-scale and large-scale enterprises in manufacturing industry. These show that with the expansion of the scale, the ownership structure will be more reasonable; enterprises will put more resources and funds into substantial innovation. With the strong strength of enterprises, the research will be more in-depth and innovative.

Keywords: Innovation Persistence, Ownership Structure, Pattern Recognition, BP Neural Network

Cite this paper: Ma, Y. and Meng, Y. (2019) Research on Enterprise Innovation Persistence Patterns Recognition and Selection Based on BP Neural Network. American Journal of Industrial and Business Management, 9, 658-679. doi: 10.4236/ajibm.2019.93045.

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