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Facilitating the discovery of relevant studies on risk analysis for three-dimensional printing based on an integrated framework

Author

Listed:
  • Munan Li

    (South China University of Technology)

  • Alan L. Porter

    (Georgia Institute of Technology
    Search Technology)

Abstract

In an accurate and timely manner, capturing the risk signals for a specific emerging technology from academic publications is important to facilitate risk governance and to reduce the potential negative impact on socioeconomic systems. In the past decade, three-dimensional printing (3D printing) has become a promising emerging technology. To identify the relevant research on risk analysis for 3D printing, “term clumping” on “risk analysis” is explored using a quantitative method, and an integrated framework for risk identification is proposed with regard to 3D printing. This method involves a variation of TF*IDF and several new metrics for a Boolean query of the literature. The empirical results for the risk analysis studies of 3D printing show that, to date, very little attention has been paid to the relevant topics. However, although the risk signals of 3D printing are still weak and dispersed in many different categories, the potential threats to human health, cyber-security, and the environment are revealed in some facets. This enables initiation of strategies for anticipatory governance, involving science and technology policies and regulations.

Suggested Citation

  • Munan Li & Alan L. Porter, 2018. "Facilitating the discovery of relevant studies on risk analysis for three-dimensional printing based on an integrated framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 277-300, January.
    • Handle: RePEc:spr:scient:v:114:y:2018:i:1:d:10.1007_s11192-017-2570-0
    DOI: 10.1007/s11192-017-2570-0
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    References listed on IDEAS

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    1. Ortwin Renn & Christina Benighaus, 2013. "Perception of technological risk: insights from research and lessons for risk communication and management," Journal of Risk Research, Taylor & Francis Journals, vol. 16(3-4), pages 293-313, April.
    2. Ying Huang & Donghua Zhu & Yue Qian & Yi Zhang & Alan L. Porter & Yuqin Liu & Ying Guo, 2017. "A hybrid method to trace technology evolution pathways: a case study of 3D printing," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 185-204, April.
    3. Philip Shapira & Jan Youtie & Alan L. Porter, 2010. "The emergence of social science research on nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(2), pages 595-611, November.
    4. Sheetal Gavankar & Sarah Anderson & Arturo A. Keller, 2015. "Critical Components of Uncertainty Communication in Life Cycle Assessments of Emerging Technologies," Journal of Industrial Ecology, Yale University, vol. 19(3), pages 468-479, June.
    5. An, Hyoung Joon & Ahn, Sang-Jin, 2016. "Emerging technologies—beyond the chasm: Assessing technological forecasting and its implication for innovation management in Korea," Technological Forecasting and Social Change, Elsevier, vol. 102(C), pages 132-142.
    6. Mojtaba Khorram Niaki & Fabio Nonino, 2017. "Additive manufacturing management: a review and future research agenda," International Journal of Production Research, Taylor & Francis Journals, vol. 55(5), pages 1419-1439, March.
    7. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    8. Jun Yang & Wanmeng Hao & Lei Chen & Jiejun Chen & Jing Jin & Feng Wang, 2016. "Risk Assessment of Distribution Networks Considering the Charging-Discharging Behaviors of Electric Vehicles," Energies, MDPI, vol. 9(7), pages 1-20, July.
    9. Nick Pidgeon & Barbara Harthorn & Terre Satterfield, 2011. "Nanotechnology Risk Perceptions and Communication: Emerging Technologies, Emerging Challenges," Risk Analysis, John Wiley & Sons, vol. 31(11), pages 1694-1700, November.
    10. Stephen Carley & Alan L Porter, 2011. "Measuring the influence of nanotechnology environmental, health and safety research," Research Evaluation, Oxford University Press, vol. 20(5), pages 389-395, December.
    11. Jeong, Yujin & Park, Inchae & Yoon, Byungun, 2016. "Forecasting technology substitution based on hazard function," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 259-272.
    12. André O Laplume & Bent Petersen & Joshua M Pearce, 2016. "Global value chains from a 3D printing perspective," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 47(5), pages 595-609, June.
    13. Munan Li, 2015. "A novel three-dimension perspective to explore technology evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1679-1697, December.
    14. Howard Kunreuther, 2002. "Risk Analysis and Risk Management in an Uncertain World," Risk Analysis, John Wiley & Sons, vol. 22(4), pages 655-664, August.
    15. Rayna, Thierry & Striukova, Ludmila, 2016. "From rapid prototyping to home fabrication: How 3D printing is changing business model innovation," Technological Forecasting and Social Change, Elsevier, vol. 102(C), pages 214-224.
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    Cited by:

    1. Yujin Jeong & Hyejin Jang & Byungun Yoon, 2021. "Developing a risk-adaptive technology roadmap using a Bayesian network and topic modeling under deep uncertainty," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3697-3722, May.
    2. Wang, Zhinan & Porter, Alan L. & Wang, Xuefeng & Carley, Stephen, 2019. "An approach to identify emergent topics of technological convergence: A case study for 3D printing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 723-732.
    3. Karlijn Muiderman & Aarti Gupta & Joost Vervoort & Frank Biermann, 2020. "Four approaches to anticipatory climate governance: Different conceptions of the future and implications for the present," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(6), November.
    4. Munan Li, 2018. "Classifying and ranking topic terms based on a novel approach: role differentiation of author keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 77-100, July.
    5. 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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