Identifying the Science-Technology Interface: Matching Patent Data to a Bibliometric Model

Abstract

Downloaded fromRecent developments have heightened interest in understanding the science-technology interface. The increased rate of turnover in new knowledge and the diminishing distinction between what has been thought of as &dquo;basic&dquo; and &dquo;applied&dquo; research have changed conceptions of how science and technology develop and interact.4 Moreover, the internationalization of global markets, the growing importance of science and technology to national competitiveness, the increased cost of performing leading-edge research in certain fields, and the relative scarcity of resources for science have put pressure on the environment for research funding. The pressure is to view research funding as investment and to concentrate on what is now commonly referred to in science policy circles as &dquo;enabling&dquo; or &dquo;strategic&dquo; science. The concept of &dquo;strategic&dquo; science is already embedded in the language of science policy discussions. Consider the following statement from a 1987 U.S. National Science Foundation (NSF) document:That certain areas of basic research are &dquo;strategic science&dquo; has considerable implications for the establishment of government R&D priorities. If early identification of promising areas of strategic science is possible, targeted support by government can increase future economic and technological benefits. 5 Yet there has been relatively little detailed consideration of the nature of &dquo;strategic&dquo; science; how it might be identified and pursued; or what the implications are of pursuing it, for the institution of science, for the research system, and for the society.6 6 Within the context of these complex issues, the objectives of this study were modest and exploratory. Most generally, the objective was to investigate what might be learned about the science-technology interface by combining two sets of quantitative data. An attempt was made to identify and characterize the nexus between science-to the extent that it is represented by a literature-based &dquo;model&dquo; of the research front-and related technical applications-to the extent they are represented by a complementary body of patents. This was done by &dquo;matching&dquo; patent data to scientific literature data from a co-citation model. (Co-citation modeling is explained in the &dquo;Data Sources and Methodology&dquo; section.) &dquo;Intersects&dquo; between the two data sets-indicated mainly by matching patent inventors and paper authorswere used to characterize two types of research topic areas defined by the literature-based model: those with few or no patent intersects and those with many patent intersects. The former group was taken to represent science that has not been technologically applied, and the latter to represent science that has been.

Tracking techno-science networks: A case study of fuel cells and related hydrogen technology R&D in Norway

“…Several methodological approaches have been developed to explore the links between science and technology symbolised by university inventors or 'inventor-authors' -knowledge generators who both publish and patent (e.g. TIJSSEN, 2004;MEYER et al, 2003, COWARD & FRANKLIN, 1989.…”

Measuring science–technology interaction using rare inventor–author names

“…Bonaccorsi and Thoma (2007) explore some of the reasons why individuals are useful as a unit of study as opposed to using citations. Coward and Franklin (1989) were among the first to match inventors with authors using literature and patent data. They explored the field of semiconductor-related science using a database of over 100,000 papers and 2452 patents, with the objective of matching patent data to their bibliometric model of the field.…”

Measuring science-based science linkage and non-science-based linkage of patents through non-patent references

“…This finding resonated with Coward and Franklin (1989) and Klitkou and Gulbrandsen (2010). SL performance has also been discussed within specific technological fields, for instance, nanotechnology (Hullmann & Meyer, 2003), tissue engineering (Chen & Hicks, 2004) and genetic engineering technology (Lo, 2010).…”

“…A common quantitative method used to measure the relationship between science and technology is citation analysis (Coward & Franklin, 1989;Hicks, Breitzman, Hamilton, & Narin, 2000;Tijssen, 2001;Acosta & Coronado, 2003;Bhattacharya & Meyer, 2003;Chen & Hicks, 2004;Gupta, 2006;Lo, 2010). Other methods used include: inventor-author name matches (Coward & Franklin, 1989;Boyack & Klavans, 2008), author-inventor co-publications (Breschi & Catalini, 2009;Klitkou & Gulbrandsen, 2010) and peer opinions through interviews and surveys (Fagerberg, 1987;Tijssen, 2002).…”