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Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology

Author

Listed:
  • Fang Han

    (Massachusetts Institute of Technology
    Beijing University of Posts and Telecommunications)

  • Christopher L. Magee

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

Abstract

The relationship of scientific knowledge development to technological development is widely recognized as one of the most important and complex aspects of technological evolution. This paper adds to our understanding of the relationship through use of a more rigorous structure for differentiating among technologies based upon technological domains (defined as consisting of the artifacts over time that fulfill a specific generic function using a specific body of technical knowledge). The key findings of the work are: Firstly, a Pearson correlation of 0.564 is found between technological relatedness among technological domains based upon patents citing other patents and technological relatedness among technological domains based upon patents citing similar scientific papers. This result indicates that a large portion of technological relatedness is due to relatedness of the underlying scientific categories. Secondly, the overall structure of the links found between scientific categories and technological domains is many-to-many rather than focused indicating a science-fostered mechanism for fairly broad “spillover”: Specific technological domains cite a wide variety of scientific categories; some scientific categories are cited in a variety of domains. Thirdly, some evidence is found supporting the co-evolution of science and technology but the evidence is not strong. Prior research that identifies emerging patent clusters and independent prior research identifying emerging scientific topics show statistically significant but qualitatively weak inter-relationships between the clusters and topics. This work also offers evidence that patent cluster emergence can, but does not usually, precede the emergence of related scientific topics. The lack of clear evidence for co-evolution is interpreted as resulting from the documented complex many-to-many relationship of science categories and technological domains and is not considered evidence against co-evolution.

Suggested Citation

  • Fang Han & Christopher L. Magee, 2018. "Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 767-796, August.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:2:d:10.1007_s11192-018-2774-y
    DOI: 10.1007/s11192-018-2774-y
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    as
    1. Christopher L. Benson & Christopher L. Magee, 2015. "Technology structural implications from the extension of a patent search method," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 1965-1985, March.
    2. Balconi, Margherita & Brusoni, Stefano & Orsenigo, Luigi, 2010. "In defence of the linear model: An essay," Research Policy, Elsevier, vol. 39(1), pages 1-13, February.
    3. Meyer, Martin, 2000. "Does science push technology? Patents citing scientific literature," Research Policy, Elsevier, vol. 29(3), pages 409-434, March.
    4. Christopher L. Benson & Christopher L. Magee, 2016. "Using Enhanced Patent Data for Future-Oriented Technology Analysis," Innovation, Technology, and Knowledge Management, in: Tugrul U. Daim & Denise Chiavetta & Alan L. Porter & Ozcan Saritas (ed.), Anticipating Future Innovation Pathways Through Large Data Analysis, chapter 0, pages 119-131, Springer.
    5. Acosta, Manuel & Coronado, Daniel, 2003. "Science-technology flows in Spanish regions: An analysis of scientific citations in patents," Research Policy, Elsevier, vol. 32(10), pages 1783-1803, December.
    6. Ismael Rafols & Alan L. Porter & Loet Leydesdorff, 2010. "Science overlay maps: A new tool for research policy and library management," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(9), pages 1871-1887, September.
    7. van Eck, Nees Jan & Waltman, Ludo, 2014. "CitNetExplorer: A new software tool for analyzing and visualizing citation networks," Journal of Informetrics, Elsevier, vol. 8(4), pages 802-823.
    8. Murray, Fiona, 2004. "The role of academic inventors in entrepreneurial firms: sharing the laboratory life," Research Policy, Elsevier, vol. 33(4), pages 643-659, May.
    9. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    10. Ludo Waltman & Nees Jan van Eck, 2012. "A new methodology for constructing a publication‐level classification system of science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    11. Magee, C.L. & Basnet, S. & Funk, J.L. & Benson, C.L., 2016. "Quantitative empirical trends in technical performance," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 237-246.
    12. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    13. Murray, Fiona, 2002. "Innovation as co-evolution of scientific and technological networks: exploring tissue engineering," Research Policy, Elsevier, vol. 31(8-9), pages 1389-1403, December.
    14. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
    15. Samuel Kortum & Jonathan Putnam, 1997. "Assigning Patents to Industries: Tests of the Yale Technology Concordance," Economic Systems Research, Taylor & Francis Journals, vol. 9(2), pages 161-176.
    16. Lybbert, Travis J. & Zolas, Nikolas J., 2014. "Getting patents and economic data to speak to each other: An ‘Algorithmic Links with Probabilities’ approach for joint analyses of patenting and economic activity," Research Policy, Elsevier, vol. 43(3), pages 530-542.
    17. Klevorick, Alvin K. & Levin, Richard C. & Nelson, Richard R. & Winter, Sidney G., 1995. "On the sources and significance of interindustry differences in technological opportunities," Research Policy, Elsevier, vol. 24(2), pages 185-205, March.
    18. Mowery, David & Rosenberg, Nathan, 1993. "The influence of market demand upon innovation: A critical review of some recent empirical studies," Research Policy, Elsevier, vol. 22(2), pages 107-108, April.
    19. Arthur, W. Brian, 2007. "The structure of invention," Research Policy, Elsevier, vol. 36(2), pages 274-287, March.
    20. Subarna Basnet & Christopher L Magee, 2017. "Artifact interactions retard technological improvement: An empirical study," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-17, August.
    21. Nathan ROSENBERG, 2009. "Why do firms do basic research (with their own money)?," World Scientific Book Chapters, in: Nathan Rosenberg (ed.), Studies On Science And The Innovation Process Selected Works of Nathan Rosenberg, chapter 11, pages 225-234, World Scientific Publishing Co. Pte. Ltd..
    22. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    23. Grupp, Hariolf, 1996. "Spillover Effects and the Science Base of Innovations Reconsidered: An Empirical Approach," Journal of Evolutionary Economics, Springer, vol. 6(2), pages 175-197, May.
    24. Ludo Waltman & Nees Jan Eck, 2012. "A new methodology for constructing a publication-level classification system of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    25. Joel Mokyr, 2016. "A Culture of Growth: The Origins of the Modern Economy," Economics Books, Princeton University Press, edition 1, number 10835.
    26. Richard Klavans & Kevin W. Boyack, 2017. "Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 984-998, April.
    27. Christopher L. Benson & Christopher L. Magee, 2013. "A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 69-82, July.
    28. Anthony F. J. Raan, 2017. "Sleeping beauties cited in patents: Is there also a dormitory of inventions?," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1123-1156, March.
    29. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.
    30. Tijssen, Robert J. W., 2001. "Global and domestic utilization of industrial relevant science: patent citation analysis of science-technology interactions and knowledge flows," Research Policy, Elsevier, vol. 30(1), pages 35-54, January.
    31. Christopher L. Benson & Christopher L. Magee, 2013. "Erratum to: A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 83-83, July.
    32. Narin, Francis & Hamilton, Kimberly S. & Olivastro, Dominic, 1997. "The increasing linkage between U.S. technology and public science," Research Policy, Elsevier, vol. 26(3), pages 317-330, October.
    Full references (including those not matched with items on IDEAS)

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    4. Park, Inchae & Triulzi, Giorgio & Magee, Christopher L., 2022. "Tracing the emergence of new technology: A comparative analysis of five technological domains," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    5. Xiaoli Wang & Yun Liu & Lingdi Chen & Yifan Zhang, 2022. "Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network," SAGE Open, , vol. 12(4), pages 21582440221, December.
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    7. Ardito, Lorenzo & Natalicchio, Angelo & Appio, Francesco Paolo & Messeni Petruzzelli, Antonio, 2021. "The role of scientific knowledge within inventing teams and the moderating effects of team internationalization and team experience: Empirical tests into the aerospace sector," Journal of Business Research, Elsevier, vol. 128(C), pages 701-710.
    8. Johannes van Der Pol & Jean-Paul Rameshkoumar, 2021. "A method to reduce false positives in a patent query [Une méthode pour réduire les faux positifs dans une requête brevet]," Working Papers hal-03287970, HAL.
    9. Kang, Inje & Yang, Jiseong & Lee, Wonjae & Seo, Eun-Yeong & Lee, Duk Hee, 2023. "Delineating development trends of nanotechnology in the semiconductor industry: Focusing on the relationship between science and technology by employing structural topic model," Technology in Society, Elsevier, vol. 74(C).
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    More about this item

    Keywords

    Patent; Scientific citation; Scientific vector; Technology map; Technological domains; Co-evolve;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other

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