| 网络整体结构与合作强度对创新绩效的影响 |
| |
| 引用本文： | 网络整体结构与合作强度对创新绩效的影响.网络整体结构与合作强度对创新绩效的影响[J].科学学研究,2023,41(1):168-180. |
| |
| 作者姓名： | 网络整体结构与合作强度对创新绩效的影响 |
| |
| 作者单位： | 1. 华侨大学工商管理学院;2. ;3. 华侨大学;4. 华侨大学工商管理学院;; |
| |
| 基金项目： | 国家社会科学基金重大项目;国家自然科学基金面上项目;国家自然科学基金面上项目 |
| |
| 摘 要： | 发现协同创新网络类型、分析网络整体结构特征及其创新绩效影响因素,为提高协同合作效率具有重要的现实意义。基于协同创新视角,以新能源汽车技术的共同专利权人为研究对象,用Louvain算法识别协同创新网络。根据网络整体结构特征,用K-means聚类算法划分协同创新网络类型,并深入分析每种网络。用专利数量和专利质量衡量网络创新绩效,以合作强度和网络整体结构特征为条件属性,网络创新绩效为决策属性,用CART决策树分析创新绩效的影响关系。新方法以数据驱动为视角,借助数据挖掘技术深入剖析网络结构特征、合作强度和创新绩效之间的非线性知识规则。研究结果表明：(1)共有四种协同创新网络,即二元协同创新网络、星型协同创新网络、完全协同创新网络和复杂协同创新网络,不同网络类型的整体结构特征和创新绩效差异化明显,证实了分类讨论的合理性与科学性。(2)二元协同创新网络和完全协同创新网络没有明显的核心成员,星型协同创新网络和复杂协同创新网络存在核心成员。(3)在不受其他因素的共同影响下,复杂协同创新网络和星型协同创新网络的合作强度分别对网络创新绩效具有正向和负向影响;在二元协同创新网络中,过高或过低的合作强度均有助...
|
| 关 键 词： | 协同创新 网络结构 合作强度 创新绩效 K-means聚类 CART决策树 |
| 收稿时间： | 2021-11-15 |
The impact of the overall network structure and cooperation intensity on innovation performance |
| |
| Abstract: | The article is divided into two major parts, namely "collaborative innovation network classification" and "analysis of network innovation capabilities". The first part of the main solution is "What types of collaborative innovation networks?" and "How is the overall feature of each network type?". The second part is how the analysis factor affects network innovation performance? In order to solve the above problems, this paper is based on collaborative innovation perspectives. According to the research objectives and social network analysis theory, the common patent author of new energy automobile technology is the research object, first identify collaborative innovation networks with the Louvain community division algorithm, and calculate each network Network structural features such as network density, network scale, network clustering coefficient, and network average path length. According to the network structure characteristics, the K-Means clustering algorithm is used to find a collaborative innovation network with similar structures, and further analyze the overall characteristics of the network. Take the number of inventors holding patents, the number of IPCs, the number of patent citations, the number of claims, and the number of patent rights as basic indicators, and use the entropy method to comprehensively evaluate the quality of patents, and measure the performance of network innovation together with the number of patents. For each type of network, with network cooperation intensity and network structural characteristics, network innovation capability is a decision attribute, using CART decision tree to extract the conditions for decision attributes on decision attributes, answer "What factors affect each network type How it affects the problem”. From the perspective of data driven, the new method deeply analyzes the nonlinear relationship between the structural characteristics of collaborative innovation, cooperation intensity and innovation performance with the help of data mining technology. The results show that：(1) There are 4 types of the networks, namely the binary collaborative innovation network, star collaborative innovation network, completely collaborative innovation network and complex co-innovation network, different network types and its innovation ability is obvious, confirmed The rationality and scientificity of classification discussions. Binary cooperation innovation network cooperation relationship is simple; the star collaborative innovation network has obvious core members, non-core members have not produced cooperative relations; compared to the first three types, complex co-innovation network has more complex cooperative relationships. (2) Binary collaborative innovation networks are more cooperation with mother subsidiaries, and the network structure is simple; the non-core members of the star collaborative innovation network rely on core members, reduces the update speed of heterogeneity knowledge in the network. The innovation subjects of complete collaborative innovation networks are close; complex collaboration innovation networks are large, resulting in a complex partnership between collaborative subjects, and the membership relationship is relatively low. (3) Without the common influence of other factors, the cooperation intensity of the complex collaborative innovation network and the star-shaped collaborative innovation network have a positive and negative impact on the network innovation performance respectively; in the dual collaborative innovation network, the high or low The intensity of cooperation has a positive impact on the performance of network innovation. For collaborative innovation networks with large cooperation intensity, the average path length has a negative impact on network innovation capabilities. |
| |
| Keywords: | |
|
| 点击此处可从《科学学研究》浏览原始摘要信息 |
| 点击此处可从《科学学研究》下载免费的PDF全文 |
|
