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Innovation pathways in additive manufacturing: Methods for tracing emerging and branching paths from rapid prototyping to alternative applications

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  • Robinson, Douglas K.R.
  • Lagnau, Axel
  • Boon, Wouter P.C.

Abstract

In recent years, the Forecasting Innovation Pathway approach (FIP) has shown to be a promising set of tools to capture potential developments in emerging fields through capturing indications of endogenous futures. However, the FIP approach is reliant on a clear demarcated area to study, a challenge for emerging technology fields where uncertainty and rhetoric abound. This paper presents an addition to the FIP toolbox that helps characterise and demarcate boundaries of emerging fields to allow for deeper analysis through other FIP methods. We illustrate this approach through an exercise for 3D printing technology (also known as Additive Manufacturing). We show that 3D printing can be represented by a dominant design: a tri-partite configuration of printer, material and digital design software. In the past decade we have seen significant branching from applications in rapid-prototyping to medical, fashion, aeronautics and supply chain management with a variety of elements coming together in tri-partite configurations. The paper adds to the current FTA literature an approach building on evolutionary theories of technical change to help with such situations – emerging, evolving and branching ‘innovation pathways’. Moreover, we developed a methodology to construct these innovation paths.

Suggested Citation

  • Robinson, Douglas K.R. & Lagnau, Axel & Boon, Wouter P.C., 2019. "Innovation pathways in additive manufacturing: Methods for tracing emerging and branching paths from rapid prototyping to alternative applications," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 733-750.
    • Handle: RePEc:eee:tefoso:v:146:y:2019:i:c:p:733-750
    DOI: 10.1016/j.techfore.2018.07.012
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    References listed on IDEAS

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    2. Robinson, Douglas K.R. & Schoen, Antoine & Larédo, Philippe & Gallart, Jordi Molas & Warnke, Philine & Kuhlmann, Stefan & Ordóñez-Matamoros, Gonzalo, 2021. "Policy lensing of future-oriented strategic intelligence: An experiment connecting foresight with decision making contexts," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    3. Douglas K. R. Robinson & Antoine Schoen & Philippe Larédo & Jordi Molas Gallart & Philine Warnke & Stefan Kuhlmann & Gonzalo Ordóñez-Matamoros, 2021. "Policy lensing of future-oriented strategic intelligence: An experiment connecting foresight with decision making contexts," Post-Print hal-03232913, HAL.
    4. Christopher W. H. Davis & Antonie J. Jetter & Philippe J. Giabbanelli, 2022. "Automatically Generating Scenarios from a Text Corpus: A Case Study on Electric Vehicles," Sustainability, MDPI, vol. 14(13), pages 1-21, June.
    5. Berns, John P. & Jia, Yankun & Gondo, Maria, 2022. "Crowdfunding success in sustainability-oriented projects: An exploratory examination of the crowdfunding of 3D printers," Technology in Society, Elsevier, vol. 71(C).
    6. Marić, Josip & Opazo-Basáez, Marco & Vlačić, Božidar & Dabić, Marina, 2023. "Innovation management of three-dimensional printing (3DP) technology: Disclosing insights from existing literature and determining future research streams," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    7. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Vrontis, Demetris & Thrassou, Alkis, 2022. "SME entrepreneurship and digitalization – the potentialities and moderating role of demographic factors," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    8. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).

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