IDEAS home Printed from https://ideas.repec.org/a/wly/fufsci/v4y2022i1ne99.html
   My bibliography  Save this article

New perspectives for data‐supported foresight: The hybrid AI‐expert approach

Author

Listed:
  • Amber Geurts
  • Ralph Gutknecht
  • Philine Warnke
  • Arjen Goetheer
  • Elna Schirrmeister
  • Babette Bakker
  • Svetlana Meissner

Abstract

This paper outlines new perspectives for data‐supported foresight by combining participatory expert‐based futures dialogues with the power of artificial intelligence (AI) in what we call the hybrid AI‐expert‐based foresight approach. To this end, we present a framework of five typical steps in a fully fledged foresight process ranging from scoping to strategizing and show how AI can be integrated into each of the steps to enable the hybrid AI‐expert foresight approach. Building on this, we present experiences gained from two recent research projects of TNO and Fraunhofer ISI that deal with aspects of the hybrid AI‐expert foresight approach and give insights into the opportunities and challenges of the new perspectives for data‐supported foresight that this approach enables. Finally, we summarize open questions and challenges for future research.

Suggested Citation

  • Amber Geurts & Ralph Gutknecht & Philine Warnke & Arjen Goetheer & Elna Schirrmeister & Babette Bakker & Svetlana Meissner, 2022. "New perspectives for data‐supported foresight: The hybrid AI‐expert approach," Futures & Foresight Science, John Wiley & Sons, vol. 4(1), March.
    • Handle: RePEc:wly:fufsci:v:4:y:2022:i:1:n:e99
    DOI: 10.1002/ffo2.99
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/ffo2.99
    Download Restriction: no
    File URL: https://libkey.io/10.1002/ffo2.99?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Douglas Henrique Milanez & Leandro Innocentini Lopes Faria & Roniberto Morato Amaral & Daniel Rodrigo Leiva & José Angelo Rodrigues Gregolin, 2014. "Patents in nanotechnology: an analysis using macro-indicators and forecasting curves," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1097-1112, November.
    2. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    3. Elna Schirrmeister & Anne‐Louise Göhring & Philine Warnke, 2020. "Psychological biases and heuristics in the context of foresight and scenario processes," Futures & Foresight Science, John Wiley & Sons, vol. 2(2), June.
    4. Baker, Erin & Bosetti, Valentina & Salo, Ahti, 2020. "Robust portfolio decision analysis: An application to the energy research and development portfolio problem," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1107-1120.
    5. Huang, Lu & Zhang, Yi & Guo, Ying & Zhu, Donghua & Porter, Alan L., 2014. "Four dimensional Science and Technology planning: A new approach based on bibliometrics and technology roadmapping," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 39-48.
    6. Victor van Rij, 2010. "Joint horizon scanning: identifying common strategic choices and questions for knowledge," Science and Public Policy, Oxford University Press, vol. 37(1), pages 7-18, February.
    7. Kerstin Cuhls & Ahti Salo, 2003. "Technology foresight-past and future," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(2-3), pages 79-82.
    8. Kerstin E. Cuhls, 2020. "Horizon Scanning in Foresight – Why Horizon Scanning is only a part of the game," Futures & Foresight Science, John Wiley & Sons, vol. 2(1), March.
    9. Mu-Hsuan Huang & Chia-Pin Chang, 2014. "Detecting research fronts in OLED field using bibliographic coupling with sliding window," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1721-1744, March.
    10. Pauliina Krigsholm & Kirsikka Riekkinen, 2019. "Applying Text Mining for Identifying Future Signals of Land Administration," Land, MDPI, vol. 8(12), pages 1-15, November.
    11. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    12. Hussain, M. & Tapinos, E. & Knight, L., 2017. "Scenario-driven roadmapping for technology foresight," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 160-177.
    13. Lehr, Thomas & Lorenz, Ullrich & Willert, Markus & Rohrbeck, René, 2017. "Scenario-based strategizing: Advancing the applicability in strategists' teams," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 214-224.
    14. Samira Ranaei & Arho Suominen & Alan Porter & Stephen Carley, 2020. "Evaluating technological emergence using text analytics: two case technologies and three approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 215-247, January.
    15. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
    16. Young-Joo Lee & Ji-Young Park, 2018. "Identification of future signal based on the quantitative and qualitative text mining: a case study on ethical issues in artificial intelligence," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 653-667, March.
    17. Effie Amanatidou & Maurits Butter & Vicente Carabias & Totti Könnölä & Miriam Leis & Ozcan Saritas & Petra Schaper-Rinkel & Victor van Rij, 2012. "On concepts and methods in horizon scanning: Lessons from initiating policy dialogues on emerging issues," Science and Public Policy, Oxford University Press, vol. 39(2), pages 208-221, March.
    18. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
    19. Small, Henry & Tseng, Hung & Patek, Mike, 2017. "Discovering discoveries: Identifying biomedical discoveries using citation contexts," Journal of Informetrics, Elsevier, vol. 11(1), pages 46-62.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Lu, Kun & Yang, Guancan & Wang, Xue, 2022. "Topics emerged in the biomedical field and their characteristics," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. 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).
    4. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    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).
    6. Xu, Shuo & Hao, Liyuan & An, Xin & Yang, Guancan & Wang, Feifei, 2019. "Emerging research topics detection with multiple machine learning models," Journal of Informetrics, Elsevier, vol. 13(4).
    7. Chiarello, Filippo & Giordano, Vito & Spada, Irene & Barandoni, Simone & Fantoni, Gualtiero, 2024. "Future applications of generative large language models: A data-driven case study on ChatGPT," Technovation, Elsevier, vol. 133(C).
    8. Woo, Seokkyun & Youtie, Jan & Ott, Ingrid & Scheu, Fenja, 2021. "Understanding the long-term emergence of autonomous vehicles technologies," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    9. 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.
    10. Marinković, Milan & Al-Tabbaa, Omar & Khan, Zaheer & Wu, Jie, 2022. "Corporate foresight: A systematic literature review and future research trajectories," Journal of Business Research, Elsevier, vol. 144(C), pages 289-311.
    11. Zhenyu Yang & Wenyu Zhang & Zhimin Wang & Xiaoling Huang, 2024. "A deep learning-based method for predicting the emerging degree of research topics using emerging index," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4021-4042, July.
    12. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    13. Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2024. "The impact of ChatGPT on human skills: A quantitative study on twitter data," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    14. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    15. Peter Sjögårde & Fereshteh Didegah, 2022. "The association between topic growth and citation impact of research publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1903-1921, April.
    16. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    17. 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.
    18. Mengjia Wu & Yi Zhang & Mark Markley & Caitlin Cassidy & Nils Newman & Alan Porter, 2024. "COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7229-7259, November.
    19. 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.
    20. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.

    More about this item

    Statistics

    Access and download statistics

    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:wly:fufsci:v:4:y:2022:i:1:n:e99. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)2573-5152 .

    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.