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The impact of collaboration on the technological performance of regions: time invariant or driven by life cycle dynamics?

An explorative investigation of European regions in the field of Biotechnology

Scientometrics
Authors: Catherine Lecocq and Bart Van Looy

Abstract  

Increasingly, collaboration between firms as well as science-industry interactions are being considered as important for technology development. Yet, few attempts have been made to analyze the contribution of collaboration, taking into account different stages of the technology life cycle. Our analysis, based on a panel of 197 regions in the EU-15 and Switzerland (time period 1978–2001), provides evidence that, in the field of biotechnology, science-industry collaboration contributes to better technological performance of regions both during the emerging phases (1978–1990) and the growth stages (1991–1999) of the life cycle. Collaboration between industrial partners also contributes to the technological performance of regions during the first phase but is less pronounced during later phases of the technology life cycle. Moreover, the analysis reveals that, as technologies develop over time, the impact of local collaboration is mitigated in favor of collaboration that has an international dimension. This holds true for both science-industry interactions and for collaboration between firms. In consequence, our findings underscore the relevance of incorporating life cycle dynamics (of technologies) when studying the nature and impact of collaboration on the technological performance of regions.

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In this study, we examine and validate the use of existing text mining techniques (based on the vector space model and latent semantic indexing) to detect similarities between patent documents and scientific publications. Clearly, experts involved in domain studies would benefit from techniques that allow similarity to be detected—and hence facilitate mapping, categorization and classification efforts. In addition, given current debates on the relevance and appropriateness of academic patenting, the ability to assess content-relatedness between sets of documents—in this case, patents and publications—might become relevant and useful. We list several options available to arrive at content based similarity measures. Different options of a vector space model and latent semantic indexing approach have been selected and applied to the publications and patents of a sample of academic inventors (n = 6). We also validated the outcomes by using independently obtained validation scores of human raters. While we conclude that text mining techniques can be valuable for detecting similarities between patents and publications, our findings also indicate that the various options available to arrive at similarity measures vary considerably in terms of accuracy: some generally accepted text mining options, like dimensionality reduction and LSA, do not yield the best results when working with smaller document sets. Implications and directions for further research are discussed.

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Abstract  

In this pilot study we examine the performance of text-based profiling in recovering a set of validated inventor-author links. In a first step we match patents and publications solely based on their similarity in content. Next, we compare inventor and author names on the highest ranked matches for the occurrence of name matches. Finally, we compare these candidate matches with the names listed in a validated set of inventor-author names. Our text-based profile methodology performs significantly better than a random matching of patents and publications, suggesting that text-based profiling is a valuable complementary tool to the name searches used in previous studies.

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In this paper we investigate — at a country level — the relationship between the science intensity of patents and technological productivity, taking into account differences in terms of scientific productivity. The number of non patent references in patents is considered as an approximation of the science intensity of technology whereas a country’s technological and scientific performance is measured in terms of productivity (i.e., number of patents and publications per capita). We use USPTO patent-data pertaining to biotechnology for 20 countries covering the time period 1992–1999. Our findings reveal mutual positive relationships between scientific and technological productivity for the respective countries involved. At the same time technological productivity is associated positively with the science intensity of patients. These results are confirmed when introducing time effects. These observations corroborate the construct validity of science intensity as a distinctive indicator and suggest its usefulness for assessing science and technology dynamics.

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Abstract

Indicators based on non-patent references (NPRs) are increasingly being used for measuring and assessing science–technology interactions. But NPRs in patent documents contain noise, as not all of them can be considered ‘scientific’. In this article, we introduce the results of a machine-learning algorithm that allows identifying scientific references in an automated manner. Using the obtained results, we analyze indicators based on NPRs, with a focus on the difference between NPR- and scientific non-patent references-based indicators. Differences between both indicators are significant and dependent on the considered patent system, the applicant country and the technological domain. These results signal the relevancy of delineating scientific references when using NPRs to assess the occurrence and impact of science–technology interactions.

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Scientometrics
Authors: Julie Callaert, Bart Van Looy, Arnold Verbeek, Koenraad Debackere, and Bart Thijs

Summary  

The recent developments towards more systemic conceptualizations of innovation dynamics and related policies highlight the need for indicators that mirror the dynamics involved. In this contribution, we assess the role that 'non-patent references', found in patent documents, can play in this respect. After examining the occurrence of these references in the USPTO and EPO patent systems, their precise nature is delineated by means of a content analysis of two samples of nonpatent references (n=10,000). Our findings reveal that citations in patents allow developing nontrivial and robust indicators. The majority of all non-patent references are journal references, which provide ample possibilities for large-scale analyses focusing on the extent to which technological developments are situated within the vicinity of scientific knowledge. Application areas, limitations and directions for future research are discussed.

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Summary  

Today's theories and models on innovation stress the importance of scientific capabilities and science-technology proximity, especially in new emerging fields of economic activity. Inthis contribution we examine the relationship between national scientific capabilities, the science intensity of technology and technological performance within six emergent industrial fields. Our findings reveal that national technological performance is positively associated with scientific capabilities. Countries performing better on a technological level are characterized both by larger numbers of publications and by numbers of involved institutions that exceed average expected values. The latter observation holds for both companies and knowledge generating institutes actively involved in scientific activities. As such, our findings seem to suggest beneficial effects of scientific capabilities shouldered by a multitude of organizations. In addition, higher numbers of patent activity coincide with higher levels of science intensity pointing out the relevance of science 'proximity' when developing technology in newer, emerging fields. Limitations and directions for further research are discussed.

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