Modeling Innovation Chains Using Case-Based Econometrics: Nano-electronics and Biotechnology Applications

许多关于技术创新的文献侧重于孤立地分析特定类型的创新相关活动的科学、技术或商业生产率的衡量标准，而没有考虑构成创新链的这些活动从科学发现到商业化的相互联系的序列的总体生产率。对于不同类型的个别创新活动如何在一系列事件中依次联系起来，最终可能产生新的商业产品或流程，目前还存在着理解上的差距。同样，人们对这种创新活动在时间和空间上相互关联的序列的总体生产力的组织和体制决定因素知之甚少。本项目分析了在创新链的沿着的每个阶段，这些创新事件之间成功过渡的可能性的经济、组织和制度决定因素，并最终走向商业上成功的创新。正在利用从专利、科学出版物、文献和专利引用、许可协议、研究资金和合作协议中收集的数据，建立一个创新链沿着各阶段活动和实体之间时空联系的新数据库，并制定这些相互联系的事件链中知识和技术流动的定性和定量指标。正在利用半导体工业中纳米电子创新的案例研究和制药工业中生物技术创新的应用建立数据库。这两个成熟的高科技行业的努力，实验与新的战略，将新兴技术整合到他们的创新管道的启发，这一研究重点。用于协调这些行业创新活动的组织、机构和筹资战略的多样性和广度提供了一套丰富多样的数据，可用于制定指标和了解知识和技术流动。更广泛的影响：虽然这项研究的重点是半导体和制药行业，分析也应该有更广泛的影响，在发展更普遍适用的程式化的事实的决定因素的创新过程。正在构建的知识和技术流动的指标可用作数据，在一个给定的研发项目成功地过渡到各种可能类型的后续项目的概率的决定因素的计量经济学模型的估计沿着创新链，并最终导致商业化。使用结构模型对该数据库进行分析，可以从经验上深入了解使创新相关项目成功概率最大化的经济、组织和结构因素。这些见解是对科学和创新政策科学发展的重大贡献。此外，公共和私营部门的决策者应该能够利用这些结果来帮助选择不同的可能的组织，机构和融资战略，并提高支持项目的成功创新的几率。

Much of the literature on technological innovation focuses on analyzing measures of the scientific, technical, or commercial productivity of specific types of innovation-related activities in isolation, and does not consider the overall productivity of the linked sequences of these activities that constitute an innovation chain, from scientific discovery through commercialization. There currently is a gap in understanding of how different types of individual innovative activities come to be linked sequentially in a chain of events that ultimately may produce new commercial products or processes. Similarly, little is known about the organizational and institutional determinants of the overall productivity of linked sequences of such innovative events over time and space. This project analyzes the economic, organizational, and institutional determinants of the probability of a successful transition between such innovative events, and ultimately, toward commercially successful innovation, at each stage along the innovation chain. Data gleaned from patents, scientific publications, literature and patent citations, licensing agreements, research funding, and collaborative agreements are being used to construct a novel database of spatial and temporal linkages between activities and entities at each stage along an innovation chain, and to develop qualitative and quantitative indicators of knowledge and technology flows within these chains of linked events. The database is being constructed using case studies of nano-electronic innovations in the semiconductor industry, and applications of biotechnology innovations in the pharmaceutical industry. This research focus is inspired by efforts within these two mature high technology industries to experiment with novel strategies to integrate emerging technologies into their innovation pipelines. The variety and breadth of organizational, institutional, and funding strategies used to coordinate innovative activities in these industries provides a rich and varied set of data to be used in developing indicators and understanding of knowledge and technology flows. Broader Impacts: Although this research focuses on the semiconductor and pharmaceutical industries, the analysis should also have a broader impact in the development of more generally applicable stylized facts about determinants of the innovative process. The indicators of knowledge and technology flows being constructed are available for use as data in estimation of econometric models of determinants of the probability of a given R&D project successfully transitioning to various possible types of successor projects along an innovation chain, and ultimately, resulting in commercialization. Analysis of this database using a structural model provides empirical insights into the economic, organizational and structural factors that maximize the probability of success for innovation-related projects. These insights are a significant contribution to the development of a science of science and innovation policy. Further, decision makers in both the public and private sectors should be able to use these results to help choose among different possible organizational, institutional, and funding strategies, and improve the odds of successful innovation resulting from supported projects.