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Scientometric and patentometric analyses to determine the knowledge landscape in innovative technologies: The case of 3D bioprinting

Author

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  • Marisela Rodríguez-Salvador
  • Rosa María Rio-Belver
  • Gaizka Garechana-Anacabe

Abstract

This research proposes an innovative data model to determine the landscape of emerging technologies. It is based on a competitive technology intelligence methodology that incorporates the assessment of scientific publications and patent analysis production, and is further supported by experts’ feedback. It enables the definition of the growth rate of scientific and technological output in terms of the top countries, institutions and journals producing knowledge within the field as well as the identification of main areas of research and development by analyzing the International Patent Classification codes including keyword clusterization and co-occurrence of patent assignees and patent codes. This model was applied to the evolving domain of 3D bioprinting. Scientific documents from the Scopus and Web of Science databases, along with patents from 27 authorities and 140 countries, were retrieved. In total, 4782 scientific publications and 706 patents were identified from 2000 to mid-2016. The number of scientific documents published and patents in the last five years showed an annual average growth of 20% and 40%, respectively. Results indicate that the most prolific nations and institutions publishing on 3D bioprinting are the USA and China, including the Massachusetts Institute of Technology (USA), Nanyang Technological University (Singapore) and Tsinghua University (China), respectively. Biomaterials and Biofabrication are the predominant journals. The most prolific patenting countries are China and the USA; while Organovo Holdings Inc. (USA) and Tsinghua University (China) are the institutions leading. International Patent Classification codes reveal that most 3D bioprinting inventions intended for medical purposes apply porous or cellular materials or biologically active materials. Knowledge clusters and expert drivers indicate that there is a research focus on tissue engineering including the fabrication of organs, bioinks and new 3D bioprinting systems. Our model offers a guide to researchers to understand the knowledge production of pioneering technologies, in this case 3D bioprinting.

Suggested Citation

  • Marisela Rodríguez-Salvador & Rosa María Rio-Belver & Gaizka Garechana-Anacabe, 2017. "Scientometric and patentometric analyses to determine the knowledge landscape in innovative technologies: The case of 3D bioprinting," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-22, June.
  • Handle: RePEc:plo:pone00:0180375
    DOI: 10.1371/journal.pone.0180375
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    References listed on IDEAS

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    1. Gaizka Garechana & Rosa Rio-Belver & Ernesto Cilleruelo & Jaso Larruscain Sarasola, 2015. "Clusterization and mapping of waste recycling science. Evolution of research from 2002 to 2012," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(7), pages 1431-1446, July.
    2. Gaizka Garechana & Rosa Río-Belver & Iñaki Bildosola & Marisela Rodríguez Salvador, 2017. "Effects of innovation management system standardization on firms: evidence from text mining annual reports," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1987-1999, June.
    3. Peng Hui Lv & Gui-Fang Wang & Yong Wan & Jia Liu & Qing Liu & Fei-cheng Ma, 2011. "Bibliometric trend analysis on global graphene research," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 399-419, August.
    4. Pao-Long Chang & Chao-Chan Wu & Hoang-Jyh Leu, 2010. "Using patent analyses to monitor the technological trends in an emerging field of technology: a case of carbon nanotube field emission display," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 5-19, January.
    5. Ronald N. Kostoff & Raymond G. Koytcheff & Clifford G. Y. Lau, 2007. "Global nanotechnology research metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 70(3), pages 565-601, March.
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    Cited by:

    1. Bicudo, Edison & Faulkner, Alex & Li, Phoebe, 2021. "Sociotechnical alignment in biomedicine: The 3D bioprinting market beyond technology convergence," Technology in Society, Elsevier, vol. 66(C).
    2. René Lezama-Nicolás & Marisela Rodríguez-Salvador & Rosa Río-Belver & Iñaki Bildosola, 2018. "A bibliometric method for assessing technological maturity: the case of additive manufacturing," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1425-1452, December.
    3. Block, Carolin & Wustmans, Michael & Laibach, Natalie & Bröring, Stefanie, 2021. "Semantic bridging of patents and scientific publications – The case of an emerging sustainability-oriented technology," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    4. Shuto Miyashita & Shogo Katoh & Tomohiro Anzai & Shintaro Sengoku, 2020. "Intellectual Property Management in Publicly Funded R&D Program and Projects: Optimizing Principal–Agent Relationship through Transdisciplinary Approach," Sustainability, MDPI, vol. 12(23), pages 1-17, November.
    5. Shu-Hao Chang & Chin-Yuan Fan, 2020. "Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    6. Thabang Lazarus Bambo & Anastassios Pouris, 2020. "Bibliometric analysis of bioeconomy research in South Africa," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 29-51, October.

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