创新扩散网络结构与创新扩散绩效关系研究

创新扩散网络结构对创新扩散绩效有重要影响。通过设计基于原胞自动机的创新扩散网络仿真模型,模拟不同网络结构的创新扩散过程,依据确定的创新扩散绩效测量指标,分析了创新扩散网络结构参数与创新扩散绩效之间的关系。研究发现,在创新扩散网络中,个体创新信息采纳概率是影响创新扩散速度和规模的最重要因素;创新扩散网络密度越大,创新扩散规模越大,扩散速度越快;初始创新者数量多可以加快创新传播速度,但不能扩大创新扩散规模。     

动态稠密人际网络中创新扩散研究——一个多智能体仿真分析

使用复杂网络模型最新成果——p-GNC网络拟合社交时代的人际网络,运用多智能体仿真法分析了p-GNC网络结构、网络效应强度和社会系统中创新先驱者比例对创新扩散的影响。研究发现,p-GNC网络的平均聚集系数越大、平均度越小,创新扩散规模越大,且平均度是创新扩散规模的决定因素。网络效应强度正向影响创新扩散规模,网络效应强度越高,p-GNC网络结构变化对创新扩散规模的影响越大。创新先驱者比例正向影响创新扩散规模,当网络效应强度较高且p-GNC网络呈现出稀疏状态时,提高创新先驱者比例对创新扩散规模的促进作用随创新先驱者比例的增加而边际递减。     

动态稠密人际网络中创新扩散研究

使用复杂网络模型最新成果——p-GNC网络拟合社交时代的人际网络，运用多智能体仿真法分析了p-GNC网络结构、网络效应强度和社会系统中创新先驱者比例对创新扩散的影响。研究发现，p-GNC网络的平均聚集系数越大、平均度越小，创新扩散规模越大，且平均度是创新扩散规模的决定因素。网络效应强度正向影响创新扩散规模，网络效应强度越高，p-GNC网络结构变化对创新扩散规模的影响越大。创新先驱者比例正向影响创新扩散规模，当网络效应强度较高且p-GNC网络呈现出稀疏状态时，提高创新先驱者比例对创新扩散规模的促进作用随创新先驱者比例的增加而边际递减。     

基于Agent的小世界网络中集中式与分散式创新扩散仿真分析

在小世界网络中,采用节点具有记忆功能的创新扩散模型进行仿真实验,考察了两种不同的创新扩散方式——集中式扩散和分散式扩散在小世界网络中创新扩散效果的优劣。实验结果表明,在小世界网络中采用分散式扩散要比采用集中式扩散的效果好,具体体现在扩散的时间短、范围广,而且在创新抵触型网络中能更有效地进行扩散。同时更进一步发现,通过增加节点的"创新感知"能有效地改进节点在网络中的扩散效果,大大减少节点在扩散前期所花费的时间,使节点避免出现"Lock-in"现象。     

小世界网络结构下的用户创新扩散分析

在阐述用户创新的概念及用户创新相关研究理论的基础上,分析了用户创新的扩散特性。根据WS小世界网络模型,构建了用户创新扩散的网络模型,并进行了模拟分析。模拟分析结果表明,用户创新扩散在不同的网络结构下呈现出不同的结果,创新采纳在小世界区间比例达到最大,随着网络随机化程度的加大,可以更高、更快地达到平均创新知识水平和知识的均匀扩散。     

基于社会网络的服务创新关系与知识扩散模型

服务创新系统状态取决于社会网络中知识主体之间的有效连接,服务创新关系优化表现为社会网络知识链结点之间,知识服务关系和创新过程的协同。通过分析基于社会网络拓扑结构的服务创新关系结构特征,从不同维度的结构关系研究其对创新性知识扩散过程产生影响,解析知识扩散过程和创新价值实现的效率。通过构建基于社会网络的SIR知识扩散模型,进一步对基于服务创新关系的知识扩散过程进行数量界定,提出了优化知识扩散效果、促进区域科技成果转化的对策。     

小世界网络下用户创新扩散效果分析

本文对用户创新的概念及相关研究成果进行了阐述,分析了用户创新扩散的特性;运用WS小世界网络模型构建了用户创新扩散的网络模型,并对扩散效果进行了模拟分析。结果表明:用户创新扩散在不同的网络结构下呈现出不同的结果;创新采纳比例在小世界区间达到最大;随着网络随机化程度的加大,平均创新知识水平变得更高,知识的均匀扩散能够更快实现。最后进行了案例分析。     

知识密集型服务业创新扩散溢出能力、效应及其网络质量评价研究

知识密集型服务业对于区域产业经济的影响已深入到产业结构内部节点互联化发展网络层面。结合网络分析,针对长三角区域知识密集型服务业进行创新溢出网络化层级与动态结构分析,测算创新扩散溢出与吸收在流程和产品维度的网络扩散结构、活动集聚程度及其层级变化态势。结果表明,结构化效益差异是创新扩散对于创新网络式转换及传播的机制作用;创新转移传播与溢出具有从多节点、多部门走向相对单一的科技服务类别的特征,且创新扩散溢出网络分布不均化特征逐渐增强,并具有层级网络扩散特征和相对均衡化的创新溢出吸收扁平化网络层级特征。     

复杂网络视角下技术创新扩散种子选择研究

创新是企业生存发展的根本，企业希望通过创新提升自身竞争力，然而，创新扩散效果不佳会最终制约企业发展。针对技术创新扩散种子用户的选择问题，运用复杂网络方法，构建了技术创新级联扩散非线性模型，通过寻找客户网络中分散于不同社区的重要节点展开技术创新扩散，以达到最大限度的扩散效果。对模拟网络和真实网络的仿真分析发现，该种子选择方法确实能够加快技术创新在整个网络的扩散速度，提升扩散效果，为企业赢得竞争优势，促进企业创新良性发展。     

基于社会网络的创新扩散研究

一种创新产生后并不会马上被掌握,而是通过社会网络逐步扩散。基于社会网络分析,建立了单个用户面对创新决策时的双向概率模型。此外,引入势函数,分析了创新扩散过程中网络整体收益,得出创新扩散过程最终稳定状态的势函数数值比初始状态增加的结论。最后,将建立的数学模型在Von Neumann邻居型网络、Moore邻居型网络和小世界网络3种典型网络中进行仿真验证,并对仿真结果进行比较分析,得出在初始支持者相同的条件下,不支持者与支持者间的距离总和越小,以及所有关联不支持者的边的权重总和越小对创新扩散越有利的另一结论。     

RESEARCH NETWORKS AS INFRASTRUCTURE FOR KNOWLEDGE DIFFUSION IN EUROPEAN REGIONS

This paper concentrates on the role of research network infrastructure in fostering the dissemination of innovation-related knowledge. It examines the structure of collaborative networks and of knowledge transfer between research, innovation and deployment activities in the field of information and communication technology for the European Union as a whole and for several European regions. Research networks complement diffusion networks by providing additional links and by increasing the number of the organisations involved in sharing and exchanging knowledge. Two types of actors are key players in these networks: hubs and gatekeepers. Hubs maintain the bulk of ties in the networks also helping the smaller and more isolated members remain connected. Gatekeepers bridge research and diffusion networks. Such organisations naturally offer greater policy leverage in establishing a European knowledge infrastructure. Moreover, strengthened inter-network connectivity among research and diffusion activities (deployment) would raise the effectiveness of European research in terms of accelerating innovation.     

It’s a match! Simulating compatibility-based learning in a network of networks

In this article, we develop a new way to capture knowledge diffusion and assimilation in innovation networks by means of an agent-based simulation model. The model incorporates three essential characteristics of knowledge that have not been covered entirely by previous diffusion models: the network character of knowledge, compatibility of new knowledge with already existing knowledge, and the fact that transmission of knowledge requires some form of attention. We employ a network-of- networks approach, where agents are located within an innovation network and each agent itself contains another network composed of knowledge units (KUs). Since social learning is a path-dependent process, in our model, KUs are exchanged among agents and integrated into their respective knowledge networks depending on the received KUs’ compatibility with the currently focused ones. Thereby, we are also able to endogenize attributes such as absorptive capacity that have been treated as an exogenous parameter in some of the previous diffusion models. We use our model to simulate and analyze various scenarios, including cases for different degrees of knowledge diversity and cognitive distance among agents as well as knowledge exploitation vs. exploration strategies. Here, the model is able to distinguish between two levels of knowledge diversity: heterogeneity within and between agents. Additionally, our simulation results give fresh impetus to debates about the interplay of innovation network structure and knowledge diffusion. In summary, our article proposes a novel way of modeling knowledge diffusion, thereby contributing to an advancement of the economics of innovation and knowledge.     

复杂网络下产业转移与区域技术创新扩散影响关系研究

在对产业转移与区域技术创新扩散的复杂网络结构进行分析的基础上,结合复杂网络和采纳阈值模型等相关理论,以技术类型为调节变量,构建了基于产业转移的区域技术创新扩散仿真模型,并借助Netlogo仿真平台进行了仿真分析。结果表明,产业转移规模、创新程度及信息整合与技术创新扩散速度均呈正向相关关系,但在技术创新扩散深度上,受阈值机制的影响,二者关系呈现阶段性变化规律,且在不同的技术类型情况下,转移产业规模、创新程度及信息整合对技术创新扩散深度的影响也存在差异性特征。最后为产业转移和区域经济管理者提出了相关对策建议。     

The Organization of Innovation Brokers: An International Review

Research on networks of innovation has typically focused on relationships of competition and collaboration between firms in the network, sometimes extending the analysis to sources of new ideas such as universities. More recently, attention has been paid to the activities of intermediaries in such networks, which facilitate the innovation process, which are positioned between the sources and users of innovations. This paper focuses on the organization of a distinctive type of intermediary, which we have defined as an innovation broker, by providing an analysis of 10 case studies of organizations dedicated to innovation broking from around the world. Innovation brokers in our definition are organizations that are founded especially to undertake an intermediary role, rather than performing that role as a by-product of their principal activities. The results of this analysis suggest that the key role played by innovation brokers in the innovation process is the independent validation of new ideas, thereby facilitating diffusion. In order to carry out this task, innovation brokers are organized on a not-for-profit basis, typically as a public-private partnership. The paper concludes by discussing the implications of the findings for innovation theory and research in networks of innovation.     

The Organization of Innovation Brokers: An International Review

Research on networks of innovation has typically focused on relationships of competition and collaboration between firms in the network, sometimes extending the analysis to sources of new ideas such as universities. More recently, attention has been paid to the activities of intermediaries in such networks, which facilitate the innovation process, which are positioned between the sources and users of innovations. This paper focuses on the organization of a distinctive type of intermediary, which we have defined as an innovation broker, by providing an analysis of 10 case studies of organizations dedicated to innovation broking from around the world. Innovation brokers in our definition are organizations that are founded especially to undertake an intermediary role, rather than performing that role as a by-product of their principal activities. The results of this analysis suggest that the key role played by innovation brokers in the innovation process is the independent validation of new ideas, thereby facilitating diffusion. In order to carry out this task, innovation brokers are organized on a not-for-profit basis, typically as a public–private partnership. The paper concludes by discussing the implications of the findings for innovation theory and research in networks of innovation.     

Riding the wave: explaining cycles in urban mega-project development

This paper examines the complex mix of technological innovation, economics, social networks, ideologies and interest groups that spur the diffusion of certain new mega-project approaches at punctuated moments in time. Situating mega-projects within a context of long-range economic cycles and competitive urban entrepreneurialism on a global scale, cyclical patterns of innovation are identified, where successful early trendsetter mega-projects have stimulated interest groups to promote similar projects in their own cities. The paper concludes that the global diffusion of mega-project innovations are driven by the political and policy lure of achieving major tangible benefits, as well as the potential to convey a powerful set of symbolic messages.     

考虑影响不对称性的创新扩散与网络结构协同演化建模：基于信息熵与累积优势的仿真研究

创新扩散发生于社会系统中并通常与社会系统存在交互作用。网络是体现社会系统的重要手段，但已有研究中的扩散网络多为无向网络且具有静态或外生动态性质，上述处理方式忽视或无法体现扩散与以网络形式出现的社会系统的协同演化以及个体间的不对称影响。基于有向网络，提出能够体现创新扩散与网络协同演化的描述性框架，从信息搜寻与降低认知失调视角出发，基于信息熵与累积优势机制，在个体层面构造网络演化与创新采纳数学模型。在RepastSimphony2.7开发平台下，利用基于Java的智能体建模方法展开系统仿真实验。基于实验数据的描述性结果揭示，动态网络与静态网络下的创新扩散存在显著不同，网络结构演化将强化节点出度的不均衡状况，更优的扩散结果往往与更不均衡的节点出度分布同时出现，其它因素包括大众传播与人际传播等也会影响扩散与网络的协同演化；交互作用存在于控制变量间。推断性结果确认了控制变量影响与交互作用的普遍存在。