IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i17p9890-d628062.html
   My bibliography  Save this article

Analysis of Uncertainties and Levels of Foreknowledge in Relation to Major Features of Emerging Technologies—The Context of Foresight Research for the Fourth Industrial Revolution

Author

Listed:
  • Andrzej Magruk

    (Faculty of Engineering Management, Bialystok University of Technology, 45A, Wiejska Street, 15-351 Bialystok, Poland)

Abstract

One of the key roles in the development of Industry 4.0 systems is played by “emerging technologies” as new tools with promising—though with a high level of uncertainty—capabilities. The management of such systems should be based on a comprehensive—future-oriented—research approach. Such activities are enabled by the foresight methodology. The main purpose of this publication is to attempt to answer the following research question: “What levels of foreknowledge and knowledge in the context of the development of emerging technologies—in relation to their features in Industry 4.0—should be taken into account during the analysis of uncertainties in the sense of foresight research based on different anticipated options?” In detail, the examination covered the relationship of classes of research foresight methods with regard to types of future, scopes of uncertainty, cycles of knowledge and original levels of foreknowledge in the field of the development of emerging technologies in Industry 4.0. Emerging technologies combined with the research on foreknowledge and uncertainties is an interesting research area with many theoretical and practical potential implications. The study uses the results of the analysis and criticism of the literature, mental experiments, and the intuitive method as the main research methods. This provides a basis for performing conceptual modeling.

Suggested Citation

  • Andrzej Magruk, 2021. "Analysis of Uncertainties and Levels of Foreknowledge in Relation to Major Features of Emerging Technologies—The Context of Foresight Research for the Fourth Industrial Revolution," Sustainability, MDPI, vol. 13(17), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9890-:d:628062
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/17/9890/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/17/9890/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nick Pidgeon, 2014. "Complexity, uncertainty and future risks," Journal of Risk Research, Taylor & Francis Journals, vol. 17(10), pages 1269-1271, November.
    2. Saurabh Mishra & Bilal M. Ayyub, 2019. "Shannon Entropy for Quantifying Uncertainty and Risk in Economic Disparity," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2160-2181, October.
    3. Minkkinen, Matti & Auffermann, Burkhard & Ahokas, Ira, 2019. "Six foresight frames: Classifying policy foresight processes in foresight systems according to perceived unpredictability and pursued change," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    4. Sarpong, David & Eyres, Eve & Batsakis, Georgios, 2019. "Narrating the future: A distentive capability approach to strategic foresight," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 105-114.
    5. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    6. Corrocher Nicoletta & Malerba Franco & Montobbio Fabio, 2003. "The emergence of new technologies in the ICT field: main actors, geographical distribution and knowledge sources," Economics and Quantitative Methods qf0317, Department of Economics, University of Insubria.
    7. Dirk Meissner & Leonid Gokhberg & Alexander Sokolov (ed.), 2013. "Science, Technology and Innovation Policy for the Future," Springer Books, Springer, edition 127, number 978-3-642-31827-6, September.
    8. Furqan Jameel & Uzair Javaid & Wali Ullah Khan & Muhammad Naveed Aman & Haris Pervaiz & Riku Jäntti, 2020. "Reinforcement Learning in Blockchain-Enabled IIoT Networks: A Survey of Recent Advances and Open Challenges," Sustainability, MDPI, vol. 12(12), pages 1-23, June.
    9. Kerstin Cuhls, 2003. "From forecasting to foresight processes-new participative foresight activities in Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(2-3), pages 93-111.
    10. Boon, Wouter & Moors, Ellen, 2008. "Exploring emerging technologies using metaphors - A study of orphan drugs and pharmacogenomics," Social Science & Medicine, Elsevier, vol. 66(9), pages 1915-1927, May.
    11. Warren E. Walker & Vincent A. W. J. Marchau & Jan H. Kwakkel, 2013. "Uncertainty in the Framework of Policy Analysis," International Series in Operations Research & Management Science, in: Wil A. H. Thissen & Warren E. Walker (ed.), Public Policy Analysis, edition 127, chapter 0, pages 215-261, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rafał Trzaska & Adam Sulich & Michał Organa & Jerzy Niemczyk & Bartosz Jasiński, 2021. "Digitalization Business Strategies in Energy Sector: Solving Problems with Uncertainty under Industry 4.0 Conditions," Energies, MDPI, vol. 14(23), pages 1-21, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Inchae Park & Byungun Yoon, 2018. "Identifying Promising Research Frontiers of Pattern Recognition through Bibliometric Analysis," Sustainability, MDPI, vol. 10(11), pages 1-32, November.
    2. Ad van den Oord & Arjen van Witteloostuijn, 2018. "A multi-level model of emerging technology: An empirical study of the evolution of biotechnology from 1976 to 2003," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-27, May.
    3. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    4. Petros Gkotsis & Antonio Vezzani, 2016. "Technological diffusion as a recombinant process," JRC Working Papers on Corporate R&D and Innovation 2016-07, Joint Research Centre.
    5. 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.
    6. Porter, Alan L. & Garner, Jon & Carley, Stephen F. & Newman, Nils C., 2019. "Emergence scoring to identify frontier R&D topics and key players," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 628-643.
    7. Dirk Meissner & Alexander Sokolov, 2013. "Foresight and science, technology and innovation indicators," Chapters, in: Fred Gault (ed.), Handbook of Innovation Indicators and Measurement, chapter 16, pages 381-402, Edward Elgar Publishing.
    8. Chao Yang & Donghua Zhu & Xuefeng Wang & Yi Zhang & Guangquan Zhang & Jie Lu, 2017. "Requirement-oriented core technological components’ identification based on SAO analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1229-1248, September.
    9. Block, Carolin & Wustmans, Michael & Laibach, Natalie & Bröring, Stefanie, 2021. "Semantic bridging of patents and scientific publications – The case of an emerging sustainability-oriented technology," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    10. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    11. Bojovic, Neva, 2022. "Strategic framing of enabling technologies: Insights from firms digitizing smell and taste," Research Policy, Elsevier, vol. 51(3).
    12. Xiaoyu Liu & Alan L. Porter, 2020. "A 3-dimensional analysis for evaluating technology emergence indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 27-55, July.
    13. Wooseok Jang & Yongtae Park & Hyeonju Seol, 2021. "Identifying emerging technologies using expert opinions on the future: A topic modeling and fuzzy clustering approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6505-6532, August.
    14. Ozcan Saritas & Derrick Ababio Anim, 2017. "The Last and Next 10 Years of Foresight," HSE Working papers WP BRP 77/STI/2017, National Research University Higher School of Economics.
    15. Waßenhoven, Anna & Rennings, Michael & Laibach, Natalie & Bröring, Stefanie, 2023. "What constitutes a “Key Enabling Technology” for transition processes: Insights from the bioeconomy's technological landscape," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    16. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    17. Li, Munan & Porter, Alan L. & Suominen, Arho, 2018. "Insights into relationships between disruptive technology/innovation and emerging technology: A bibliometric perspective," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 285-296.
    18. Moehrle, Martin G. & Caferoglu, Hüseyin, 2019. "Technological speciation as a source for emerging technologies. Using semantic patent analysis for the case of camera technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 776-784.
    19. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    20. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9890-:d:628062. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.