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与创新相关的特定类型活动的科学、技术或商业生产力的措施，而没有考虑构成创新链的这些活动的相关序列的整体生产力，从科学发现到商业化。对于不同类型的个人创新活动如何在最终可能产生新的商业产品或工艺的事件链中依次联系起来，目前在理解上存在差距。同样，对于这些创新事件在时间和空间上的关联序列的总体生产力的组织和制度决定因素，我们所知甚少。本项目分析了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，并最终在创新链的每个阶段走向商业上成功的创新。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正在用于构建一个新的数据库，该数据库记录创新链上每个阶段的活动和实体之间的时空联系，并为这些相互关联的事件链内的知识和技术流动制定定性和定量指标。目前正在利用半导体工业的纳米电子创新和制药工业的生物技术创新应用的案例研究来建立这个数据库。这两个成熟的高科技产业都在尝试将新兴技术整合到他们的创新管道中，这一研究重点受到了这些努力的启发。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，可用于制定指标和理解知识和技术流动。更广泛的影响：虽然本研究侧重于半导体和制药行业，但分析也应该对创新过程决定因素的更普遍适用的风格化事实的发展产生更广泛的影响。正在构建的知识和技术流动的指标可用作估计一个给定的研发项目沿创新链成功过渡到各种可能类型的后续项目的概率的计量经济学模型的决定因素的数据，并最终导致商业化。使用结构模型对该数据库进行分析，提供了对经济、组织和结构因素的实证见解，这些因素使创新相关项目的成功概率最大化。这些见解对科学与创新政策的发展做出了重大贡献。此外，公共和私营部门的决策者都应该能够利用这些结果来帮助选择不同可能的组织、制度和资助策略，并提高受支持项目成功创新的几率。

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.