Evolution of innovation and its strategies: from ecological niche models of supply chain clusters

Abstract

Generally, supply chain cluster (SCC) evolution follows innovation and its selection strategies. To further reduce the innovation risk, SCC must seek the feasible innovation strategies to match ecological niche-matching degree; within a competing cluster, it should avoid the hazardous nature of pure competition that affects the innovation output, and within a cooperation innovation cluster it should look carefully for the cooperation innovation mechanism. This paper examines the impact of growth of SCC from a perspective of ecological niche, the innovation strategy and the expansion of ecological niche, and its matching degree on the influence of the growth in SCC; and presents the ecological niche expansion model, growth model and growth evolution model of SCC. The findings of this research suggest that there is a five-stage evolution of which the characteristic is different in different innovation strategies. The ecological niche width of SCC is connected by each member’s ecological niche, and the expansion process is constituted by the members’ ecological niche expansion. In each stage, SCC should identify ecological niche-matching degree between members and innovation modes when the SCC expands the niche width to a certain limit. Finally, a case study demonstrates the application potential of the model and its implications.

Keywords:

• supply chain cluster
• ecological niche
• niche width
• innovation strategy
• growth evolution model.

Acknowledgements

The authors are most grateful to the two anonymous reviewers and the editor for their extremely constructive comments that helped to improve the presentation of the paper considerably. This research was supported by the National Natural Science Foundation of China (NSFC) (Project Nos. 71371159 and 71201138), the National Ministry of Education of China (Humanities and Social Science Project No. 12YJC63026) and the Fujian Provincial Natural Science Foundation (Project No. 2012J01304). Special thanks to Professor Zhaowei Miao and Ms Wei Feng for their assistance in this project.