Measuring science-based science linkage and non-science-based linkage of patents through non-patent references
Introduction
Research has shown that invention and innovation originate in basic and applied research, progressing into technological and economic growth (Rosenberg and Birdzel, 1990, Narin and Olivastro, 1992, Narin et al., 1995, Narin, 2000, Acosta and Coronado, 2003, Klitkou and Gulbrandsen, 2010). This positive relationship between important technological advances and economic outcomes has been statistically measured by high citations between science (using papers as a proxy) and technology (using patents as a proxy).
Science linkage (SL) was first proposed and defined by Narin in 1991 as ‘the average number of “other references cited” on the front pages of U.S. patents’. However, the SL formula and meaning were not clarified in detail until the publication of the Tech-Line® Background Paper in 2000. While its importance and popularity are evidenced in the literature, there is no common agreement as to what counts as a “science-based reference” for the purpose of SL calculations. In Narin's (2000) definition of the SL indicator, he excluded non-science-based references from non-patent references (NPRs). Similarly, Meyer, 2000a, Meyer, 2000b, who explored the different motivations for citing non-patent literature, found that only some are science-related. In other words, not all NPRs are science-based references.
It is argued that counting all NPRs as science-based references cannot reflect real linkages between science and technology. It is also observed that science-based references include more than Thomson Reuters Science Citation Index (SCI) papers. Only considering SCI papers for SL calculations arguably cannot reflect real science–technology linkages either. The past two decades have seen an increasing growth of citations to non-science-based references, with the number of such citations overtaking the number of citations to science-based references. To this end, this study aims to compare the differences between science-based SL and non-science-based linkage (NSL).
This paper begins with a review of the literature on characteristics of SL, comparisons between science-based NPRs and non-science-based NPRs, and SL as an indicator of technological innovation. It goes on to explain the methodology employed in the empirical study. The patent data analysed are presented in diagrams and tables with textual explanations. Finally, implications, based on field- and firm-specific differences in the linkages between science and technology, are discussed in Section 4.
While substantial research has studied the links between science and technology, a review of the literature observed various methods of statistically measuring such links. A common quantitative method used to measure the relationship between science and technology is citation analysis (Coward and Franklin, 1989, Hicks et al., 2000, Tijssen, 2001, Acosta and Coronado, 2003, Bhattacharya and Meyer, 2003, Chen and Hicks, 2004, Gupta, 2006, Lo, 2010). Other methods used include: inventor-author name matches (Coward and Franklin, 1989, Boyack and Klavans, 2008), author–inventor co-publications (Breschi and Catalini, 2009, Klitkou and Gulbrandsen, 2010) and peer opinions through interviews and surveys (Fagerberg, 1987, Tijssen, 2002).
Among the various citation analysis techniques, SL has been widely adopted, to a varying extent, in the literature to measure the links between science and technology. The central idea of the indicator of SL is to use the “other references cited” (Narin & Olivastro, 1992) or “non-patent references” (Narin, 2000) cited on the front page of granted U.S. patents to directly measure the link. According to CHI Research, Inc. (Narin, 2000), SL was defined as “the average number of science papers referenced on the front page of the company's patents” to indicate “how leading edge [the] company's technology [is]”.
When Narin (2000) first proposed the SL indicator, he included only science-based references and excluded other non-patent literature references from the SL calculations. A complicated issue resides in what counts as science-based references, as not all NPRs cited on the front page of granted U.S. patents belong to science-based references. Although complicated, this distinction is considered to be essential, because linkage to science is the driving force behind many important areas of technology.
As mentioned in the previous section, the SL definition (Narin, 2000) clearly pointed out the necessity of distinguishing science-based references from non-science-based references among the NPRs. For example, science-based NPRs include scientific journal papers, meetings and books, whereas non-science-based NPRs include industrial standards, technical disclosures, engineering manuals and every other conceivable kind of published material (Narin, Hamilton, & Olivastro, 1997).
However, the application of SL in the following publications varied, with two distinctive approaches observed in the literature. When selecting science-based references for the SL calculations, one approach included all NPRs (e.g. Gupta, 2006, Lo, 2010) and the other used only papers listed in the SCI database (e.g. Tijssen et al., 2000, Tijssen, 2001, Verbeek et al., 2002, Hullmann and Meyer, 2003, Han, 2007). Somewhere between the two approaches are, for example, Park and Kang (2009), including academic journals and conference proceedings as science-based references.
It is clear that the indicator of SL has been widely applied in the literature after Narin (2000). Different from Narin's (2000) approach, many authors did not exclude non-science-based references from NPRs when calculating SL. However, it is observed that Narin (2000) included patent reports as science-based references. In order to reflect real linkages between science and technology, this study adopts Narin's definition of SL but argues that a more rigid criterion to distinguish science-based SL and NSL is needed.
Generally speaking, a lot of research employed the SL indicator and concluded that there is a positive relationship between technology and research (Narin et al., 1997, Sorenson and Fleming, 2004, Klitkou and Gulbrandsen, 2010). It was identified in the literature that the SL indicator was widely applied at different levels, e.g. at the firm level (Narin, 2000), at the country level (Narin, 1991, Narin, 1994, Narin and Olivastro, 1992, Narin et al., 1997, Narin et al., 2000, Gupta, 2006, Klitkou and Gulbrandsen, 2010, Park and Kang, 2009) and at the level of technological fields (Narin and Olivastro, 1992, Anderson et al., 1996, Hullmann and Meyer, 2003, Chen and Hicks, 2004, Lo, 2010). This study builds upon the work of Narin and other scholars and further explores the SL comparisons between firms, countries and technological fields.
Despite its popularity in the literature, some debates in the literature concern whether the science–technology relationship is causal. For example, Sirilli (1998) stated that “we have no explicit model capable of determining causal relations between science, technology, economy and society in a single synthesis.” In line with this, Meyer (1998) also argued that “it is necessary to corroborate these measures more carefully first and investigate the question whether a citation linkage really equals a causal link.” Such authors saw a need for a balanced analysis between quantitative and qualitative measurement of science–technology linkages.
Furthermore, SL was developed as a quantitative indicator to measure science–technology linkages. However, to what extent does it really reflect the linkages between science and technology? As discussed in the previous section, a key issue resides in the distinction between science-based SL and NSL.
Section snippets
Patent data and technological fields
In this study, patent data were collected solely from the United States Patent and Trademark Office (USPTO) database, which is generally accepted and is accessible to the researchers. While there exist different categories of patents (e.g. plant patents, design patents, reissues and continuations), this study, based on the recommendation by Narin (2000), collected the number of regular U.S. utility patents to keep the focus of the database on the key category of patents which contributes to
Results
This section starts with the citation trend of various types of NPRs, and proceeds to compare differences between science-based SL and NSL. It then examines the SL changes and growth rates of the top 20 patent assignees in the C&C field.
Conclusion and discussion
This study has met the aims set at the outset of this paper. Specifically, this study divided NPRs into five categories and classified books, conference papers and journal papers into the group of science-based NPRs. It further considered not only science-based SL but also the ratio of science-based SL to NSL compared to the overall performance in the field. Interesting observations were also made regarding rapidly growing NSL of patents and differences of science-based SL and NSL at the levels
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2019, Journal of InformetricsCitation Excerpt :The non-patent literature cited by a patent document measures the translation from basic research to technology invention (Narin & Noma, 1985), while patent documents cited by scientific articles reveal the penetration of technology innovation into basic research (Glänzel & Meyer, 2003). Several studies have suggested that the biomedical patents cited more basic research publications and thus the biomedical industry relied on public science more than other industries (Jibu, 2014; McMillan, Narin, & Deeds, 2000; Sung, Wang, Huang, & Chen, 2015). Measuring citations to scholarly works in the global patent literature enables assessment of the influence of published research on invention, industry and enterprise, at the individual and institutional level.