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Dynamics of scientific knowledge bases as proxies for discerning technological emergence — The case of MEMS/NEMS technologies

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  • Ávila-Robinson, Alfonso
  • Miyazaki, Kumiko

Abstract

Emerging technologies embrace the very early stages of socio-technological evolution. Despite their appealing nature, they have been loosely defined and operationalized. In particular, operationalization approaches based on bibliometric methods have often tended to emphasize the exponential growth and the potential impacts of emerging technologies while overlooking their inherent uncertainty and ‘fluidity’. The purpose of this paper is to contribute to the operationalization of emerging technologies by presenting an approach for quantitatively interpreting technologies of an emerging nature along both dimensions. We do so by looking into the dynamic properties of scientific knowledge bases in terms of their rates and directions of change. Our approach integrates bibliometric indicators, social network analysis and multivariate statistical methods on scientific publications, and their citing and cited references. The empirical case of micro/nanoelectromechanical systems technologies (MEMS/NEMS), which embrace micro- and nano-sensors and actuators, is used. A total of thirteen MEMS/NEMS technologies are evaluated. Overall, our results provide a quantitative framework for discerning technological emergence through the evaluation of the dynamics of scientific knowledge bases. These results highlight the coupled intense patterns of growth and cognitive fluidity characterizing emerging technologies. We also provide a glimpse into the difficulties encountered by specific nanotechnology fields in bringing forward nano-enabled devices.

Suggested Citation

  • Ávila-Robinson, Alfonso & Miyazaki, Kumiko, 2013. "Dynamics of scientific knowledge bases as proxies for discerning technological emergence — The case of MEMS/NEMS technologies," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1071-1084.
    • Handle: RePEc:eee:tefoso:v:80:y:2013:i:6:p:1071-1084
    DOI: 10.1016/j.techfore.2012.07.012
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    Cited by:

    1. Mariia Ostapchuk & Claire Auplat & Pierre Boucard, 2023. "Economic Growth and Scientific Knowledge as Determinants of Innovation Uptake in a Situation of Uncertainty About Environmental or Health Risk," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(2), pages 1602-1634, June.
    2. Alfonso Ávila-Robinson & Shintaro Sengoku, 2017. "Tracing the knowledge-building dynamics in new stem cell technologies through techno-scientific networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1691-1720, September.
    3. Euiseok Kim & Yongrae Cho & Wonjoon Kim, 2014. "Dynamic patterns of technological convergence in printed electronics technologies: patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 975-998, February.
    4. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    5. Daim, Tugrul U. & Yoon, Byung-Sung & Lindenberg, John & Grizzi, Robert & Estep, Judith & Oliver, Terry, 2018. "Strategic roadmapping of robotics technologies for the power industry: A multicriteria technology assessment," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 49-66.
    6. Francesco Paolo Appio & Luigi de Luca & Robert Morgan & Antonella Martini, 2019. "Patent portfolio diversity and firm profitability: A question of specialization or diversification?," Post-Print halshs-02292360, HAL.
    7. Gilsing, Victor A. & Cloodt, Myriam & Bertrand–Cloodt, Danielle, 2016. "What makes you more central? Antecedents of changes in betweenness-centrality in technology-based alliance networks," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 209-221.
    8. Porter, Alan L. & Chiavetta, Denise & Newman, Nils C., 2020. "Measuring tech emergence: A contest," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    9. June Young Lee & Sejung Ahn & Dohyun Kim, 2021. "Deep learning-based prediction of future growth potential of technologies," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.
    10. Alfonso Ávila-Robinson & Cristian Mejia & Shintaro Sengoku, 2021. "Are bibliometric measures consistent with scientists’ perceptions? The case of interdisciplinarity in research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7477-7502, September.
    11. Gustafsson, Robin & Kuusi, Osmo & Meyer, Martin, 2015. "Examining open-endedness of expectations in emerging technological fields: The case of cellulosic ethanol," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 179-193.
    12. Li, Munan & Wang, Wenshu & Zhou, Keyu, 2021. "Exploring the technology emergence related to artificial intelligence: A perspective of coupling analyses," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    13. Ávila-Robinson, Alfonso & Islam, Nazrul & Sengoku, Shintaro, 2019. "Co-evolutionary and systemic study on the evolution of emerging stem cell-based therapies," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 324-339.
    14. Serhat Burmaoglu & Olivier Sartenaer & Alan Porter & Munan Li, 2019. "Analysing the theoretical roots of technology emergence: an evolutionary perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 97-118, April.
    15. Zhou, Yuan & Dong, Fang & Kong, Dejing & Liu, Yufei, 2019. "Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 205-220.
    16. Kwon, Seokbeom & Liu, Xiaoyu & Porter, Alan L. & Youtie, Jan, 2019. "Research addressing emerging technological ideas has greater scientific impact," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    17. Li, Munan & Porter, Alan L. & Suominen, Arho & Burmaoglu, Serhat & Carley, Stephen, 2021. "An exploratory perspective to measure the emergence degree for a specific technology based on the philosophy of swarm intelligence," Technological Forecasting and Social Change, Elsevier, vol. 166(C).

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