Knowledge source strategy and enterprise innovation performance: dynamic analysis based on machine learning. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Jin, Xin; Wang, Jie; Chu, Tianshu; Xia, Jinghua
• Is Part Of: Technology analysis & strategic management. Volume 30:Issue 1(2018)
• Page Start: 71
• Page End: 83
• Publication Date: 2018-01-02
• Subjects: Knowledge source strategy -- innovation performance -- machine learning -- dynamic evolution; Technological innovations -- Management -- Periodicals; Research, Industrial -- Management -- Periodicals; High technology industries -- Management -- Periodicals; Strategic planning -- Periodicals; 658.401205
• DOI: 10.1080/09537325.2017.1286011
• Languages: English
• ISSNs: 0953-7325

Abstract

ABSTRACT: By employing ergodic theory and applying the most advanced machine-leaning methods, this study exploits the rules of multi-dimensional, phased and non-linear dynamic evolution between the breadth and depth of knowledge sources and the innovation performance. The following conclusions are obtained. First, regarding explorative innovation, when both the breadth and depth of the knowledge source are at a low level, the enhancement of the breadth of the knowledge source may rapidly lift explorative innovation performance; when the knowledge source is at a high level, the theory of 'ambidexterity balance' is more applicable to find a balance between the breadth and the depth of the knowledge source for the enhancement of explorative innovation performance. Second, in terms of exploitative innovation, 'ambidexterity balance' theory can be applied at all levels. In other words, the balance of the breadth and the depth of the knowledge sources greatly enhances the exploitative innovation performance.