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Understanding the long-term emergence of autonomous vehicles technologies

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  • Woo, Seokkyun
  • Youtie, Jan
  • Ott, Ingrid
  • Scheu, Fenja

Abstract

Identifying emerging technologies has been of long-standing interest to many scholars and practitioners. Previous studies have introduced methods to capture the concept of emergence from bibliographic records, including the recently proposed Technology Emergence Indicator (Carley et al. 2018). This indicator method has shown to be applicable to various technological fields. However, the indicator uses a limited time window, which can overlook the potential long-term evolution of emerging technologies. Moreover, the existing method suffers from interpretability, because it can be difficult to understand the context in which identified emerging terms are used. In this paper, we propose an improved version of the Technology Emergence Indicator that addresses these issues. In doing so, we examine emerging topics within the field of autonomous vehicles technologies during the period of 1991-2018, guided by a proposition about the long-term diffusion of an emerging technology topic. The results show that different autonomous vehicle technology topics emerge during each of the three 10-year periods under analysis, including an initial period of understanding the surrounding environment and path planning, a second period marked by DARPA Grand Challenge motivated factors associated with the urban environment and communication technologies, and a third period relating to machine learning and object detection. This association with certain emerging technology topics in each decade is also characterized by different trajectories of continued or cyclical carryover across the decades. The results suggest a methodology that practitioners can use in examining research areas to understand which topics are likely to persist into the future.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:tefoso:v:170:y:2021:i:c:s0040162521002845
    DOI: 10.1016/j.techfore.2021.120852
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    References listed on IDEAS

    as
    1. Yun, JinHyo Joseph & Won, DongKyu & Jeong, EuiSeob & Park, KyungBae & Yang, JeongHo & Park, JiYoung, 2016. "The relationship between technology, business model, and market in autonomous car and intelligent robot industries," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 142-155.
    2. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    3. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    4. Milojević, Staša, 2015. "Quantifying the cognitive extent of science," Journal of Informetrics, Elsevier, vol. 9(4), pages 962-973.
    5. Zhang, Yi & Lu, Jie & Liu, Feng & Liu, Qian & Porter, Alan & Chen, Hongshu & Zhang, Guangquan, 2018. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding," Journal of Informetrics, Elsevier, vol. 12(4), pages 1099-1117.
    6. 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.
    7. Schmoch, Ulrich, 2007. "Double-boom cycles and the comeback of science-push and market-pull," Research Policy, Elsevier, vol. 36(7), pages 1000-1015, September.
    8. Hohenberger, Christoph & Spörrle, Matthias & Welpe, Isabell M., 2017. "Not fearless, but self-enhanced: The effects of anxiety on the willingness to use autonomous cars depend on individual levels of self-enhancement," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 40-52.
    9. 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.
    10. Utterback, James M & Abernathy, William J, 1975. "A dynamic model of process and product innovation," Omega, Elsevier, vol. 3(6), pages 639-656, December.
    11. Penmetsa, Praveena & Adanu, Emmanuel Kofi & Wood, Dustin & Wang, Teng & Jones, Steven L., 2019. "Perceptions and expectations of autonomous vehicles – A snapshot of vulnerable road user opinion," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 9-13.
    12. Youtie, Jan & Porter, Alan L. & Shapira, Philip & Woo, Seokkyun & Huang, Yayun, 2017. "Autonomous systems: A bibliometric and patent analysis," Studien zum deutschen Innovationssystem 14-2018, Expertenkommission Forschung und Innovation (EFI) - Commission of Experts for Research and Innovation, Berlin.
    13. Skeete, Jean-Paul, 2018. "Level 5 autonomy: The new face of disruption in road transport," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 22-34.
    14. Jan Youtie & Stephen Carley & Philip Shapira & Elizabeth A Corley & Dietram A Scheufele, 2011. "Perceptions and actions: relationships of views on risk with citation actions of nanotechnology scientists," Research Evaluation, Oxford University Press, vol. 20(5), pages 377-388, December.
    15. Wolfgang Glänzel & Bart Thijs, 2012. "Using ‘core documents’ for detecting and labelling new emerging topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 399-416, May.
    16. Stephen F. Carley & Nils C. Newman & Alan L. Porter & Jon G. Garner, 2017. "A measure of staying power: Is the persistence of emergent concepts more significantly influenced by technical domain or scale?," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 2077-2087, June.
    17. Matthew Gentzkow & Bryan Kelly & Matt Taddy, 2019. "Text as Data," Journal of Economic Literature, American Economic Association, vol. 57(3), pages 535-574, September.
    18. 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.
    19. Huang, Ying & Porter, Alan L. & Cunningham, Scott W. & Robinson, Douglas K.R. & Liu, Jianhua & Zhu, Donghua, 2018. "A technology delivery system for characterizing the supply side of technology emergence: Illustrated for Big Data & Analytics," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 165-176.
    20. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    21. Icíar Dominguez Lacasa & Hariolf Grupp & Ulrich Schmoch, 2003. "Tracing technological change over long periods in Germany in chemicals using patent statistics," Scientometrics, Springer;Akadémiai Kiadó, vol. 57(2), pages 175-195, June.
    22. Geels, Frank W. & Kemp, René, 2007. "Dynamics in socio-technical systems: Typology of change processes and contrasting case studies," Technology in Society, Elsevier, vol. 29(4), pages 441-455.
    23. 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.
    24. Stephen F. Carley & Nils C. Newman & Alan L. Porter & Jon G. Garner, 2018. "An indicator of technical emergence," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 35-49, April.
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