Disruptive Technology Forecasting based on Gartner Hype Cycle

被引:9
作者
Chen, Xiaoli [1 ]
Han, Tao [1 ]
机构
[1] Chinese Acad Sci, Natl Sci Liabrary, Beijing, Peoples R China
来源
2019 IEEE TECHNOLOGY & ENGINEERING MANAGEMENT CONFERENCE (TEMSCON) | 2019年
关键词
Disruptive technology; Gartner Hype Cycle; supervised machine learning; forecasting; IDENTIFICATION; EMERGENCE;
D O I
10.1109/temscon.2019.8813649
中图分类号
F [经济];
学科分类号
02 ;
摘要
The continuous breakthrough of scientific research and the emergence of new technologies have made our list of "technologies of tomorrow" longer and longer[1]-[9], and only some of these technologies have the potential to change existing patterns, change people's lifestyles, construct the new value system and bring new products or services to human beings. This part of technology is disruptive technology. Identifying potential disruptive technologies, on the one hand, allows policy makers to predict technology landscape in a timely manner and efficiently allocate resources and funding to the R&D of these technologies. On the other hand, it has universal guiding significance for all kinds of innovators and consumers. In this paper, we propose a discriminate model based on machine learning to identify potential disruptive technologies. Through feature engineering, we extract most characteristic of disruptive technology. We use Gartner Hype Cycle technologies as training data, train our model to identify new disruptive technologies. The model demonstrates how to do technology forecasting in a more controllable, repeatable and verifiable way.
引用
收藏
页数:6
相关论文
共 50 条
  • [1] Adner R, 2005, RAND J ECON, V36, P229
  • [2] When are technologies disruptive? A demand-based view of the emergence of competition
    Adner, R
    [J]. STRATEGIC MANAGEMENT JOURNAL, 2002, 23 (08) : 667 - 688
  • [3] Bishop C., 2007, Pattern recognition and machine learning (information science and statistics), V1st
  • [4] Five Disruptive Technology Directions for 5G
    Boccardi, Federico
    Heath, Robert W., Jr.
    Lozano, Angel
    Marzetta, Thomas L.
    Popovski, Petar
    [J]. IEEE COMMUNICATIONS MAGAZINE, 2014, 52 (02) : 74 - 80
  • [5] BOWER JL, 1995, HARVARD BUS REV, V73, P43
  • [6] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [7] Buchanan B., 2010, TOOLKIT SYSTEMATIC A
  • [8] Future-oriented technology analysis: Its potential to address disruptive transformations
    Cagnin, Cristiano
    Havas, Attila
    Saritas, Ozcan
    [J]. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2013, 80 (03) : 379 - 385
  • [9] Committee on Defense Intelligence Agency Technology Forecasts and Reviews, 2005, AVOIDING SURPRISE ER, V48
  • [10] Committee on Forecasting Future Disruptive Technologies