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

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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.

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Notes

  1. TS = ("3D Print*" or "Additive Manufactur*" OR "Three Dimension* Print*" OR "3D Bioprint*" OR "4D print*") Indexes = SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC Timespan = 1990-2016. A more complete or complicated strategy of search will be discussed in the following content.

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Acknowledgements

The authors acknowledge and appreciate all of the experts who were involved in the email survey on 3D printing technology, and are grateful for the constructive comments from two anonymous reviewers. This material is based on work supported by the National Natural Science Foundation of China (No. 71673088), the Foundation of Guangdong Soft Science (No. 2017A070706003), the National Science Foundation under the EAGER Award (No. 1645237) for “Using the ORCID ID and Emergence Scoring to Study Frontier Researchers” project. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. School of Business Administration, South China University of Technology, Guangzhou, 510641, China

    Munan Li

  2. Technology Policy and Assessment Centre, Georgia Institute of Technology, Atlanta, GA, USA

    Alan L. Porter

  3. Search Technology, Norcross, GA, USA

    Alan L. Porter

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  1. Munan Li
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  2. Alan L. Porter
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Correspondence to Munan Li.

Appendix

Appendix

See Tables 10 and 11.

Table 10 38 publications related to the risk analysis of 3D printing
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Table 11 The candidate terms outputted by co-word analysis
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Li, M., Porter, A.L. Facilitating the discovery of relevant studies on risk analysis for three-dimensional printing based on an integrated framework. Scientometrics 114, 277–300 (2018). https://doi.org/10.1007/s11192-017-2570-0

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