跨界团队网络特征对其颠覆性创新绩效的影响研究  

The Influence of Network Characteristics of Cross-Border Teams on Disruptive Innovation Performance

作　　者：林春培[1,2,3] 朱晓艳 余传鹏[4] 廖杨月 李海林[1,5] Lin Chunpei;Zhu Xiaoyan;Yu Chuanpeng;Liao Yangyue;Li Hailin(Business School of Huaqiao University,Quanzhou 362021;Business Management Research Center,Huaqiao University,Quanzhou 362021;Fujian Xi Jinping Research Center of Socialism with Chinese Characteristics for a New Era,Research Base of Huaqiao University,Quanzhou 362021;Department of Tourism Management,South China University of Technology,Guangzhou 510006;Research Center of Applied Statics and Big Data,Huaqiao University,Xiamen 361021)

机构地区：[1]华侨大学工商管理学院,泉州362021 [2]华侨大学商务管理研究中心,泉州362021 [3]福建省习近平新时代中国特色社会主义思想研究中心华侨大学研究基地,泉州362021 [4]华南理工大学旅游管理系,广州510006 [5]华侨大学现代应用统计与大数据研究中心,厦门361021

出　　处：《情报学报》2024年第4期391-404,共14页Journal of the China Society for Scientific and Technical Information

基　　金：国家自然科学基金面上项目“外部变革情境下企业家矛盾性认知框架对破坏性创新的影响机制研究”(71974059);“创业孵化型平台企业的合法性策略研究”(72074058);教育部人文社会科学研究一般项目“制造企业数字化转型的过程机理研究”(23YJA630124)。

摘　　要：跨界团队在企业等创新主体开展颠覆性创新活动中发挥重要作用,而运用机器学习方法识别其网络特征与颠覆性创新绩效之间殊途同归的组态路径是一个亟待解决的重要问题。本文基于Incopat专利检索平台无人机领域139999条专利数据,采用社区发现算法在专利发明人合作关系数据中识别185个跨界团队,依据社会网络理论遴选跨界团队网络特征变量,利用k-means聚类算法对跨界团队进行类型划分,并运用决策树CART(classification and regression trees)算法挖掘不同类型跨界团队网络特征对其颠覆性创新绩效的影响。研究结果表明,①跨界团队共有二元合作、类完全合作和复杂合作3种合作类型,不同跨界团队类型对颠覆性创新绩效影响具有差异性,即类完全合作团队高颠覆性创新绩效占比最高,二元合作团队高颠覆性创新绩效占比最低;②合作强度具有普适性,它是影响不同跨界团队形成不同水平颠覆性创新绩效的核心因素;③合作强度正向影响二元合作团队颠覆性创新绩效,类完全合作团队的颠覆性创新绩效受聚集系数、合作强度与团队规模的共同影响,而对于合作强度较高的复杂合作团队而言,保持较低的网络密度有利于其提升颠覆性创新绩效。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 char‐acteristics 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-bor‐der teams.Furthermore,we used the decision tree CART algorithm to explore the influence of different types of cross-bor‐der 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 dis‐ruptive 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 com‐plex cooperative teams with high cooperation intensity,maintaining a low network density is conducive to improving dis‐ruptive innovation performance.

关 键 词：颠覆性创新绩效 跨界团队 网络特征 决策规则 聚类分析 

分 类 号：F273.1[经济管理—企业管理]