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| The Influence of Network Characteristics of Cross-Border Teams on Disruptive Innovation Performance |
| Lin Chunpei1,2,3, Zhu Xiaoyan1, Yu Chuanpeng4, Liao Yangyue4, Li Hailin1,5 |
1.Business School of Huaqiao University, Quanzhou 362021
2.Business Management Research Center, Huaqiao University, Quanzhou 362021
3.Fujian Xi Jinping Research Center of Socialism with Chinese Characteristics for a New Era, Research Base of Huaqiao University, Quanzhou 362021
4.Department of Tourism Management, South China University of Technology, Guangzhou 510006
5.Research Center of Applied Statics and Big Data, Huaqiao University, Xiamen 361021 |
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Abstract Cross-border teams play an important role in the disruptive innovation activities of innovation entities such as enterprises, and the application of machine learning methods to identify the configuration path between their network characteristics and disruptive innovation performance is an important problem to be solved. Based on 139,999 patent data in the UAV field of the Incopat patent search platform, this study uses the community discovery algorithm to identify 185 cross-border teams from the cooperation relationship data of patent inventors, selects the network characteristic variables of cross-border teams according to social network theory, and uses the k-means clustering algorithm to classify cross-border teams. Furthermore, we used the decision tree CART algorithm to explore the influence of different types of cross-border team network characteristics on disruptive innovation performance. The results show that (1) there are three types of cross-border teams: binary cooperation, quasi-perfect cooperation, and complex cooperation, and different cross-border team types have different effects on disruptive innovation performance; the quasi-perfect cooperation team has the highest proportion of highly disruptive innovation performance, while the dualistic cooperation team has the lowest proportion of highly disruptive innovation performance; (2) cooperation intensity is universal, which is the core factor that affects the disruptive innovation performance of different cross-border teams at different levels; and (3) cooperation intensity positively affects the disruptive innovation performance of binary cooperative teams. The disruptive innovation performance of quasi-perfect cooperative teams is jointly affected by the aggregation coefficient, cooperation intensity, and team size. For complex cooperative teams with high cooperation intensity, maintaining a low network density is conducive to improving disruptive innovation performance.
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Received: 18 July 2023
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