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的相互关联的序列的整体生产率，从科学发现到商业化。目前，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过程的一系列事件中顺序地联系起来，存在着理解上的差距。同样，对于这种创新事件的关联序列在时间和空间上的总体生产力的组织和制度决定因素也知之甚少。该项目分析了在创新链上的每个阶段，在此类创新事件之间成功过渡并最终走向商业成功的可能性的经济、组织和制度决定因素。正在利用从专利、科学出版物、文献和专利引文、许可协议、研究资助和合作协议中收集的数据，建立一个创新链上每个阶段的活动和实体之间的空间和时间联系的新型数据库，并在这些相互关联的事件链中制定知识和技术流动的定性和定量指标。该数据库正在利用半导体行业中纳米电子创新的案例研究以及生物技术创新在制药行业中的应用来构建。这一研究重点的灵感来自于这两个成熟的高科技行业内部试验新战略，将新兴技术整合到他们的创新管道中。用于协调这些行业的创新活动的组织、机构和资金战略的多样性和广度为制定指标和了解知识和技术流动提供了丰富多样的数据。更广泛的影响：虽然这项研究的重点是半导体和制药行业，但分析也应该对开发更普遍适用的关于创新过程决定因素的风格化事实产生更广泛的影响。正在构建的知识和技术流动指标可用作计量经济学模型中的数据，这些计量模型是特定研发项目成功过渡到创新链上各种可能类型的后续项目并最终实现商业化的可能性的决定因素。使用结构模型对该数据库的分析提供了对经济、组织和结构因素的经验性见解，这些因素最大限度地提高了与创新有关的项目的成功概率。这些见解对科学和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应该能够利用这些结果来帮助在不同可能的组织、机构和资金战略中进行选择，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.