IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v148y2019ics0040162518315099.html
   My bibliography  Save this article

Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles

Author

Listed:
  • Li, Shuying
  • Garces, Edwin
  • Daim, Tugrul

Abstract

During the last years, new technologies have been developing at a rapid pace; however, new technologies carry risks and uncertainties. Technology forecasting by analogy has been used in the case of emerging technologies; nevertheless, the use of analogies is subject to several problems such as lack of inherent necessity, historical uniqueness, historically conditioned awareness, and casual analogies. Additionally, the natural process of selecting the analogy technology is based on subjective criteria for technological similarities or inductive inference. Since many analogies are taken qualitatively and rely on subjective assessments, this paper presents a quantitative comparison process based on the Social Network Analysis (SNA) and patent analysis for selecting analogous technologies. In this context, the paper presents an analysis of complex patent network structures using centrality and density metrics in order to reduce the lack of information or the presence of uncertainties. The case of Autonomous Vehicles (AVs) is explored in this paper, comparing three candidate technologies which have been chosen based on the similarities with the target technologies. The best candidate technology is selected based on the analysis of two main centrality metrics (average degree and density).

Suggested Citation

  • Li, Shuying & Garces, Edwin & Daim, Tugrul, 2019. "Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
  • Handle: RePEc:eee:tefoso:v:148:y:2019:i:c:s0040162518315099
    DOI: 10.1016/j.techfore.2019.119731
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162518315099
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2019.119731?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Daniel Kahneman & Dan Lovallo, 1993. "Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk Taking," Management Science, INFORMS, vol. 39(1), pages 17-31, January.
    2. 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.
    3. Green, Kesten C. & Armstrong, J. Scott, 2007. "Structured analogies for forecasting," International Journal of Forecasting, Elsevier, vol. 23(3), pages 365-376.
    4. Chen, Ssu-Han & Huang, Mu-Hsuan & Chen, Dar-Zen, 2012. "Identifying and visualizing technology evolution: A case study of smart grid technology," Technological Forecasting and Social Change, Elsevier, vol. 79(6), pages 1099-1110.
    5. Hanlin You & Mengjun Li & Keith W. Hipel & Jiang Jiang & Bingfeng Ge & Hante Duan, 2017. "Development trend forecasting for coherent light generator technology based on patent citation network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 297-315, April.
    6. Giacomo Marzi & Marina Dabić & Tugrul Daim & Edwin Garces, 2017. "Product and process innovation in manufacturing firms: a 30-year bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 673-704, November.
    7. Yun, JinHyo Joseph & Won, DongKyu & Park, KyungBae & Jeong, EuiSeob & Zhao, Xiaofei, 2019. "The role of a business model in market growth: The difference between the converted industry and the emerging industry," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 534-562.
    8. J. Scott Armstrong, 1984. "Forecasting by Extrapolation: Conclusions from 25 Years of Research," Interfaces, INFORMS, vol. 14(6), pages 52-66, December.
    9. Bettencourt, Luís M.A. & Kaiser, David I. & Kaur, Jasleen, 2009. "Scientific discovery and topological transitions in collaboration networks," Journal of Informetrics, Elsevier, vol. 3(3), pages 210-221.
    10. Jyun-Cheng Wang & Cheng-hsin Chiang & Shu-Wei Lin, 2010. "Network structure of innovation: can brokerage or closure predict patent quality?," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 735-748, September.
    11. Jun, Seung-Pyo & Sung, Tae-Eung & Park, Hyun-Woo, 2017. "Forecasting by analogy using the web search traffic," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 37-51.
    12. Lee, Jongsu & Lee, Chul-Yong & Lee, Kichun Sky, 2012. "Forecasting demand for a newly introduced product using reservation price data and Bayesian updating," Technological Forecasting and Social Change, Elsevier, vol. 79(7), pages 1280-1291.
    13. Behkami, Nima A. & U. Daim, Tugrul, 2012. "Research Forecasting for Health Information Technology (HIT), using technology intelligence," Technological Forecasting and Social Change, Elsevier, vol. 79(3), pages 498-508.
    14. Sangsung Park & Seung-Joo Lee & Sunghae Jun, 2015. "A Network Analysis Model for Selecting Sustainable Technology," Sustainability, MDPI, vol. 7(10), pages 1-16, September.
    15. Park, Sang Yong & Kim, Jong Wook & Lee, Duk Hee, 2011. "Development of a market penetration forecasting model for Hydrogen Fuel Cell Vehicles considering infrastructure and cost reduction effects," Energy Policy, Elsevier, vol. 39(6), pages 3307-3315, June.
    16. Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
    17. Massiani, Jérôme & Gohs, Andreas, 2015. "The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies," Research in Transportation Economics, Elsevier, vol. 50(C), pages 17-28.
    18. Chen-Yuan Liu & Jhen-Cheng Wang, 2010. "Forecasting the development of the biped robot walking technique in Japan through S-curve model analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 21-36, January.
    19. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    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. Yadav, Jitendra & Yadav, Rambalak & Sahore, Nidhi & Mendiratta, Aparna, 2023. "Digital social engagements and knowledge sharing among sports fans: Role of interaction, identification, and interface," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    2. Naeini, Ali Bonyadi & Zamani, Mehdi & Daim, Tugrul U. & Sharma, Mahak & Yalcin, Haydar, 2022. "Conceptual structure and perspectives on “innovation management”: A bibliometric review," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    3. Alvarez León, Luis F. & Aoyama, Yuko, 2022. "Industry emergence and market capture: The rise of autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    4. Isada Fumihiko, 2021. "The Partnership Network Structure of Automakers under Radical Technological Change," Business Systems Research, Sciendo, vol. 12(2), pages 95-113, December.
    5. Christian Ulrich & Benjamin Frieske & Stephan A. Schmid & Horst E. Friedrich, 2022. "Monitoring and Forecasting of Key Functions and Technologies for Automated Driving," Forecasting, MDPI, vol. 4(2), pages 1-24, May.
    6. Runbo Zhao & Huiying Zhang & Marina Yue Zhang & Fei Qu & Yunlong Xu, 2023. "Competitor-Weighted Centrality and Small-World Clusters in Competition Networks on Firms’ Innovation Ambidexterity: Evidence from the Wind Energy Industry," IJERPH, MDPI, vol. 20(4), pages 1-18, February.
    7. Su, Yu-Shan & Huang, Hsini & Daim, Tugrul & Chien, Pan-Wei & Peng, Ru-Ling & Karaman Akgul, Arzu, 2023. "Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    8. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    9. Xiao Feng & Chang Pan & Fengying Xu, 2024. "The Spatial Structure and Influencing Factors of the Tourism Economic Network in the Yangtze River Delta Urban Agglomeration," Tourism and Hospitality, MDPI, vol. 5(1), pages 1-20, February.
    10. Han, Xiaotong & Zhu, Donghua & Lei, Ming & Daim, Tugrul, 2021. "R&D trend analysis based on patent mining: An integrated use of patent applications and invalidation data," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    11. Yang, Zaoli & Zhang, Weijian & Yuan, Fei & Islam, Nazrul, 2021. "Measuring topic network centrality for identifying technology and technological development in online communities," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    12. Li, Shuying & Zhang, Xian & Xu, Haiyun & Fang, Shu & Garces, Edwin & Daim, Tugrul, 2020. "Measuring strategic technological strength :Patent Portfolio Model," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    13. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    14. Leminen, Seppo & Rajahonka, Mervi & Wendelin, Robert & Westerlund, Mika & Nyström, Anna-Greta, 2022. "Autonomous vehicle solutions and their digital servitization business models," Technological Forecasting and Social Change, Elsevier, vol. 185(C).

    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. Jun, Seung-Pyo & Sung, Tae-Eung & Park, Hyun-Woo, 2017. "Forecasting by analogy using the web search traffic," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 37-51.
    2. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    3. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    4. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    5. Dell'Era, Claudio & Di Minin, Alberto & Ferrigno, Giulio & Frattini, Federico & Landoni, Paolo & Verganti, Roberto, 2020. "Value capture in open innovation processes with radical circles: A qualitative analysis of firms’ collaborations with Slow Food, Memphis, and Free Software Foundation," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    6. Reda Cherif & Fuad Hasanov & Aditya Pande, 2021. "Riding the Energy Transition: Oil beyond 2040," Asian Economic Policy Review, Japan Center for Economic Research, vol. 16(1), pages 117-137, January.
    7. Hwang, Seonho & Shin, Juneseuk, 2019. "Extending technological trajectories to latest technological changes by overcoming time lags," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 142-153.
    8. Mauksch, Stefanie & von der Gracht, Heiko A. & Gordon, Theodore J., 2020. "Who is an expert for foresight? A review of identification methods," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    9. Zhang, Jingjing & Groen, Aard, 2021. "Informal and formal open activities: Innovation protection methods as antecedents and innovation outputs as consequences," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    10. Takahashi, Carlos Kazunari & Figueiredo, Júlio César Bastos de & Scornavacca, Eusebio, 2024. "Investigating the diffusion of innovation: A comprehensive study of successive diffusion processes through analysis of search trends, patent records, and academic publications," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    11. Huang, Ying & Li, Ruinan & Zou, Fang & Jiang, Lidan & Porter, Alan L. & Zhang, Lin, 2022. "Technology life cycle analysis: From the dynamic perspective of patent citation networks," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    12. Nelly S. Kolyan & Alexander E. Plesovskikh & Roman V. Gordeev, 2023. "Predictive Assessment of the Potential Electric Vehicle Market and the Effects of Reducing Greenhouse Gas Emissions in Russia," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 22(3), pages 497-521.
    13. Kesten C. Green & J. Scott Armstrong, 2007. "The Ombudsman: Value of Expertise for Forecasting Decisions in Conflicts," Interfaces, INFORMS, vol. 37(3), pages 287-299, June.
    14. Park, Changeun & Lim, Sesil & Shin, Jungwoo & Lee, Chul-Yong, 2022. "How much hydrogen should be supplied in the transportation market? Focusing on hydrogen fuel cell vehicle demand in South Korea," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    15. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    16. Culot, Giovanna & Orzes, Guido & Sartor, Marco & Nassimbeni, Guido, 2020. "The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    17. Chih-cheng Lo & Hsin-Chuan Cho & Pin-Wei Wang, 2020. "Global R&D Collaboration in the Development of Nanotechnology: The Impact of R&D Collaboration Patterns on Patent Quality," Sustainability, MDPI, vol. 12(15), pages 1-12, July.
    18. Tsouchnika, Maria & Smolyak, Alex & Argyrakis, Panos & Havlin, Shlomo, 2022. "Patent collaborations: From segregation to globalization," Journal of Informetrics, Elsevier, vol. 16(1).
    19. Huang, Youlin & Qian, Lixian & Soopramanien, Didier & Tyfield, David, 2021. "Buy, lease, or share? Consumer preferences for innovative business models in the market for electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    20. Etienne Theising & Dominik Wied & Daniel Ziggel, 2023. "Reference class selection in similarity‐based forecasting of corporate sales growth," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1069-1085, August.

    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:eee:tefoso:v:148:y:2019:i:c:s0040162518315099. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.