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

University Of Texas At Austin, Austin TX

Investigators

Abstract

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.