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A bibliometric method for assessing technological maturity: the case of additive manufacturing

Author

Listed:
  • René Lezama-Nicolás

    (Tecnologico de Monterrey)

  • Marisela Rodríguez-Salvador

    (Tecnologico de Monterrey)

  • Rosa Río-Belver

    (University of the Basque Country UPV/EHU, Basque Country)

  • Iñaki Bildosola

    (University of the Basque Country UPV/EHU, Basque Country)

Abstract

While novel technologies have tremendous competitive potential, they also involve certain risks. Maturity assessment analyzes how well a technological development can fulfill an expected task. The technology readiness level (TRL) has been considered to be one of the most promising approaches for addressing technological maturity. Nonetheless, its assessment requires opinions of the experts, which is costly and implies the risk of personal bias. To fill this gap, this paper presents a Bibliometric Method for Assessing Technological Maturity (BIMATEM). It is a repeatable framework that assesses maturity quantitatively. Our method is based on the assumption that each technology life cycle stage can be matched to technology records contained in scientific literature, patents, and news databases. The scientific papers and patent records of mature technologies display a logistic growth behavior, while news records follow a hype-type behavior. BIMATEM determines the maturity level by curve fitting technology records to these behaviors. To test our approach, BIMATEM was applied to additive manufacturing (AM) technologies. Our results revealed that material extrusion, material jetting, powder bed fusion and vat photopolymerization are the most mature AM technologies with TRL between 6 and 7, followed by directed energy deposition with TRL between 4 and 5, and binder jetting and sheet lamination, the least mature, with TRL between 1 and 2. BIMATEM can be used by competitive technology intelligence professionals, policymakers, and further decision makers whose main interests include assessing the risk of implementing new technologies. Future research can focus on testing the method with regard to altmetrics.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:117:y:2018:i:3:d:10.1007_s11192-018-2941-1
    DOI: 10.1007/s11192-018-2941-1
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    References listed on IDEAS

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    1. 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.
    2. Yoshiko Okubo, 1997. "Bibliometric Indicators and Analysis of Research Systems: Methods and Examples," OECD Science, Technology and Industry Working Papers 1997/1, OECD Publishing.
    3. Gao, Lidan & Porter, Alan L. & Wang, Jing & Fang, Shu & Zhang, Xian & Ma, Tingting & Wang, Wenping & Huang, Lu, 2013. "Technology life cycle analysis method based on patent documents," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 398-407.
    4. 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.
    5. Chan-Yuan Wong & Kim-Leng Goh, 2010. "Modeling the behaviour of science and technology: self-propagating growth in the diffusion process," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 669-686, September.
    6. Marco Campani & Ruggero Vaglio, 2015. "A simple interpretation of the growth of scientific/technological research impact leading to hype-type evolution curves," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 75-83, April.
    7. Haupt, Reinhard & Kloyer, Martin & Lange, Marcus, 2007. "Patent indicators for the technology life cycle development," Research Policy, Elsevier, vol. 36(3), pages 387-398, April.
    8. Dedehayir, Ozgur & Steinert, Martin, 2016. "The hype cycle model: A review and future directions," Technological Forecasting and Social Change, Elsevier, vol. 108(C), pages 28-41.
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    6. Sinigaglia, Tiago & Eduardo Santos Martins, Mario & Cezar Mairesse Siluk, Julio, 2022. "Technological evolution of internal combustion engine vehicle: A patent data analysis," Applied Energy, Elsevier, vol. 306(PA).

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