摘要
自动驾驶汽车在推动全球汽车产业颠覆性变革的同时,其技术前沿性、复杂性、跨学科性等特征使得跨界融合和协同创新成为带动产业发展的必然选择,而协同创新模式高度影响协同创新效果。利用专利工具和社会网络分析法,结合自动驾驶协同创新网络创新主体类型及其合作关系,将中国自动驾驶汽车产业协同创新模式划分为四大类:亲缘型、地缘型、业缘型和混合型。同时,不同协同创新网络存在不同的网络嵌入特征,与协同创新模式共同影响协同创新绩效。为研究自动驾驶产业协同创新模式和网络嵌入特征组态匹配对协同创新绩效的影响,利用fsQCA方法进行实证检验。研究发现:可将实现自动驾驶汽车产业高协同创新绩效和低协同创新绩效的协同创新模式分别概括为4类,其中亲缘型和亲缘地缘混合型协同创新模式对于自动驾驶汽车产业协同创新影响最大,且目前业缘型模式难以产生较高的协同创新绩效,而低协同创新绩效路径存在业缘关系占比较高等显著特征。基于此,对自动驾驶汽车产业协同创新发展提出4点建议。
With the new round of scientific and technological revolution and industrial transformation in the world,new technologies such as AI,big data,cloud computing,and V2X are developing rapidly.These new technologies are gradually integrated with the automotive industry,giving birth to historic changes in the automotive industry,and opening a new stage of intelligent and networked competition in the automotive industry.In this context,autonomous vehicles are gradually becoming the new highland of global competition.In recent years,China′s autonomous vehicle industry has developed rapidly under the two-wheel drive of policy and market.However,commercialization above Level 3 is not easy,facing many technical and policy obstacles.As a disruptive and innovative technology,autonomous vehicles have the characteristics of intersectionality,integration,complexity,and interdisciplinaryness,and it is difficult to rely on the resources of a single enterprise to solve all the problems in technology,commercial landing and other industrial development.Cross-border integration and collaborative innovation have become the key and inevitable choices for the innovation and development of the autonomous vehicle industry.The collaborative innovation mode of autonomous vehicles has important significance and plays a role in promoting technological innovation and industrial landing,accelerating industrial development and upgrading.Therefore,this study focuses on the collaborative innovation mode of China′s autonomous vehicle industry and its impact on collaborative innovation performance.First,by searching the patents of China′s autonomous vehicle industry,this study extracts 27 networks with large network scales in the Yangtze River Delta,Beijing-Tianjin-Hebei and Pearl River Delta city clusters by using social network tools.Then it analyzes each cooperation relationship in the network from four aspects:kinship,geography,industry and learning relationship,and the proportion of each cooperative relationship in the total number of cooperative relationships is calculated.If the proportion of the two relationships is similar,it is defined as a hybrid mode.Otherwise,the relationship with a higher proportion is the dominant mode.Through the analysis of 27 samples,the collaborative innovation mode of China′s autonomous vehicle industry is divided into four categories:kinship-related collaborative innovation mode,geo-related collaborative innovation mode,industry-related collaborative innovation mode,and hybrid collaborative innovation mode.Furthermore,there are different network embedding characteristics in different collaborative innovation networks,which,together with the collaborative innovation mode,affect the collaborative innovation performance.On this basis,a network embedding perspective is introduced,and 27 collaborative innovation network samples are further analyzed by the fsQCA method to explore the configuration matching between different collaborative innovation modes and network embedding features in the industry,as well as their impact on collaborative innovation performance.The study concludes that(1)there are five paths to high collaborative innovation performance.Combined with the characteristics of network embeddings,the collaborative innovation modes to achieve high collaborative innovation performance in the autonomous vehicle industry can be further summarized into four categories.G1:The kinship-related collaborative innovation mode with extensive and diversified cooperation;G2:The kinship-related collaborative innovation mode with long-term and deep cooperation;G3:The hybrid collaborative innovation mode with extensive and deep cooperation;G4:The geo-related collaborative innovation mode with extensive cooperation.Among them,the kinship-related and kinship-geo hybrid collaborative innovation modes have the greatest impact on the collaborative innovation performance of the autonomous vehicle industry.Some geo-related collaborative innovation modes can also produce higher collaborative innovation performance.But at present,it is difficult to achieve high collaborative innovation performance in the industry-related collaborative innovation mode.(2)There are six paths to achieve low collaborative innovation performance,which can also be summarized into four categories combined with the characteristics of network embeddedness.D1:The geo-related collaborative innovation mode with extensive,diversified and low-intensity cooperation;D2:The geo-related collaborative innovation mode with small-scale,diversified and high-intensity cooperation;D3:The kinship-related collaborative innovation mode with small-scale,diversified and low-intensity cooperation;D4:The collaborative innovation mode is dominated by industry relationship with low cooperation intensity.There are several obvious characteristics of low collaborative innovation performance,such as low cooperation intensity,small cooperation scale,high partner heterogeneity,and high industry relationship.
作者
周菲
刘颖琦
Ari Kokko
Zhou Fei;Liu Yingqi;Ari Kokko(School of Economics and Management,Beijing Jiaotong University,Beijing 100044,China;Department of International Economics,Government and Business,Copenhagen Business School,Copenhagen 2000,Denmark)
出处
《科技进步与对策》
北大核心
2025年第15期43-53,共11页
Science & Technology Progress and Policy
基金
北京交通大学人文社会科学重点培育项目(2023JBW2004)。
作者简介
周菲(1996-),女,安徽合肥人,北京交通大学经济管理学院博士研究生,研究方向为自动驾驶汽车产业创新和政策创新;刘颖琦(1971-),女,山东烟台人,博士,北京交通大学经济管理学院教授、博士生导师,研究方向为战略性新兴产业创新;Ari Kokko(1961-),男,瑞典人,博士,丹麦哥本哈根商学院国际经济、政府与商业系教授、博士生导师,研究方向为创新与发展。
