Analysing knowledge capture mechanisms: Methods and a stylised bioventure case |
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| Affiliation: | 1. Rathenau Institute, Science System Assessment, Anna van Saksenlaan 51, 2593 HW The Hague, The Netherlands;2. VU University Amsterdam, Network Institute & Department of Organization Science, De Boelelaan 1105, Amsterdam, The Netherlands;3. GRIPS – National Graduate Institute for Policy Studies, 7-22-1 Roppongi, Minato-ku, Tokyo 106-867, Japan;4. Université Paris-Est, ESIEE – LATTS – IFRIS, 2, bd Blaise Pascal, Noisy le Grand 93160, France;5. CNRS – Aix-Marseille Université, LEST UMR 7317, 35 Avenue Jules Ferry, 13626 Aix en Provence Cedex 01, France;1. The Graduate School of Public Policy and Information Technology, Seoul National University of Science and Technology, 172 Gongreung 2-dong, Nowon-gu, Seoul 139-746, Republic of Korea;2. Department of Systems Management Engineering, Sungkyunkwan University, 300 Chunchun-dong, Jangan-gu, Kyunggi-do 440-746, Republic of Korea;1. Laboratory for Studies of Research and Technology Transfer, Institute for System Analysis and Computer Science (IASI-CNR), National Research Council of Italy, Italy;2. Italian National Agency for the Evaluation of Universities and Research Institutes (ANVUR), Italy;3. Department of Management and Engineering University of Rome “Tor Vergata”, Italy;1. Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Kloveniersburgwal 48, 1012 CX Amsterdam, The Netherlands;2. Division for Science and Innovation Studies, Administrative Headquarters of the Max Planck Society, Hofgartenstr. 8, 80539 Munich, Germany;3. Max Planck Institute for Solid State Research, Heisenbergstraße 1, D-70569 Stuttgart, Germany;4. School of Informatics and Computing, Indiana University, Bloomington 47405-1901, United States;1. School of Management, Harbin Institute of Technology, 92 West Dazhi Street, Nan Gang District, Harbin 150001, PR China;2. College of Information and Computer Engineering, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin 150040, PR China |
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| Abstract: | Knowledge transfer between science and technology has been studied at micro- and macro-levels of analysis. This has contributed to the understanding of the mechanisms and drivers, but actual transfer mechanism and process, be they through codified or tacit sources, have very rarely been mapped and measured to completeness and remain, to a large extent, a black box. We develop a novel method for mapping science–technology flows and introduce ‘concept clusters’ as an instrument to do so. Using patent and publication data, we quantitatively and visually demonstrate the flows of knowledge between academia and industry. We examine the roles of exogenous and endogenous knowledge sources, and of co-inventors and co-authors in the application of university-generated knowledge. When applied to a stylised case, we show that the method is able to trace the linkages between base knowledge and skill sets and their application to a technology, which in some instances span over twenty-five years. |
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| Keywords: | Absorptive capacity Knowledge transfer Concept clusters Non-patent literature references Patent applications |
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