|
|
|
| Review of Weak Signal Identification Based on Technology Foresight Perspective |
| Cao Haiyan1,2, Wang Nuanchen2, Mu Ge2, Li Wanhong1 |
1.School of Economic and Management, Harbin Engineering University, Harbin 150001
2.Systems Engineering Institute, Academy of Military Science, Beijing 100141 |
|
|
|
|
Abstract Weak signals serve as early indicators of future technological opportunities and are crucial for innovation risk mitigation. Thus, they hold significant value in seizing strategic initiatives. However, current academic research results on how to identify weak signals are somewhat scattered. Especially, weak signal identification based on the perspective of technology foresight faces problems such as overlapping research fields and fragmented knowledge, thereby lacking effective support for predicting future technological development trends. In this paper, through a systematic literature review, combined with the results of keyword clustering and weak signal identification processes, a three-layer research framework was constructed, consisting of trend monitoring, signal cognition, and value construction layers. First, the themes, fields, and data of weak signal identification in the trend-monitoring layer were analyzed. Second, the characteristics and methods of weak-signal identification in the signal cognition layer were deconstructed. Third, the representation, verification, and interpretation of weak-signal identification results in the value-construction layer were sorted. Finally, by integrating the research results of the three layers, the overall framework of weak signal identification was constructed based on the perspective of technology foresight. The outlook for future research is proposed accordingly, with the aim of providing theoretical and practical references for exploring future technological opportunities and promoting the development of emerging industries by clarifying the research work on weak signal identification from the perspective of technology foresight.
|
|
Received: 17 February 2025
|
|
|
|
1 Kaivo-oja J. Weak signals analysis, knowledge management theory and systemic socio-cultural transitions[J]. Futures, 2012, 44(3): 206-217.
2 Horton A. A simple guide to successful foresight[J]. Foresight, 1999, 1(1): 5-9.
3 Martin B R. Foresight in science and technology[J]. Technology Analysis & Strategic Management, 1995, 7(2): 139-168.
4 F?rster B. Technology foresight for sustainable production in the German automotive supplier industry[J]. Technological Forecasting and Social Change, 2015, 92: 237-248.
5 Li M N. An exploration to visualise the emerging trends of technology foresight based on an improved technique of co-word analysis and relevant literature data of WOS[J]. Technology Analysis & Strategic Management, 2017, 29(6): 655-671.
6 Reger G. Technology foresight in companies: from an indicator to a network and process perspective[J]. Technology Analysis & Strategic Management, 2001, 13(4): 533-553.
7 Kim J, Park Y, Lee Y. A visual scanning of potential disruptive signals for technology roadmapping: investigating keyword cluster, intensity, and relationship in futuristic data[J]. Technology Analysis & Strategic Management, 2016, 28(10): 1225-1246.
8 Mühlroth C, Grottke M. A systematic literature review of mining weak signals and trends for corporate foresight[J]. Journal of Business Economics, 2018, 88(5): 643-687.
9 Kaivo-oja J, Lauraéus T, Knudsen M S. Picking the ICT technology winners-longitudinal analysis of 21st century technologies based on the Gartner hype cycle 2008-2017: trends, tendencies, and weak signals[J]. International Journal of Web Engineering and Technology, 2020, 15(3): 216-264.
10 Rossel P. Weak signals as a flexible framing space for enhanced management and decision-making[J]. Technology Analysis & Strategic Management, 2009, 21(3): 307-320.
11 Gaponenko N. In search of sectoral foresight methodology: bridging foresight and sectoral system of innovation and production[J]. Futures, 2022, 135: 102859.
12 Bzhalava L, Kaivo-oja J, Hassan S S. Digital business foresight: keyword-based analysis and CorEx topic modeling[J]. Futures, 2024, 155: 103303.
13 Mendon?a S, Cardoso G, Cara?a J. The strategic strength of weak signal analysis[J]. Futures, 2012, 44(3): 218-228.
14 Schoemaker P J H, Day G S, Snyder S A. Integrating organizational networks, weak signals, strategic radars and scenario planning[J]. Technological Forecasting and Social Change, 2013, 80(4): 815-824.
15 El Akrouchi M, Benbrahim H, Kassou I. End-to-end LDA-based automatic weak signal detection in web news[J]. Knowledge-Based Systems, 2021, 212: 106650.
16 Yoo S H, Won D K. Simulation of weak signals of nanotechnology innovation in complex system[J]. Sustainability, 2018, 10(2): 486.
17 Ebadi A, Auger A, Gauthier Y. Detecting emerging technologies and their evolution using deep learning and weak signal analysis[J]. Journal of Informetrics, 2022, 16(4): 101344.
18 Yoon J. Detecting weak signals for long-term business opportunities using text mining of Web news[J]. Expert Systems with Applications, 2012, 39(16): 12543-12550.
19 Thorleuchter D, Scheja T, van den Poel D. Semantic weak signal tracing[J]. Expert Systems with Applications, 2014, 41(11): 5009-5016.
20 Griol-Barres I, Milla S, Millet J. Improving strategic decision making by the detection of weak signals in heterogeneous documents by text mining techniques[J]. AI Communications, 2019, 32(5/6): 347-360.
21 Griol-Barres I, Milla S, Cebrián A, et al. Detecting weak signals of the future: a system implementation based on text mining and natural language processing[J]. Sustainability, 2020, 12(19): 7848.
22 Ha T, Yang H, Hong S. Automated weak signal detection and prediction using keyword network clustering and graph convolutional network[J]. Futures, 2023, 152: 103202.
23 Zhao D Y, Tang Z J, He D K. A systematic literature review of weak signal identification and evolution for corporate foresight[J]. Kybernetes, 2024, 53(10): 3160-3188.
24 韩盟, 陈悦, 王玉奇, 等. 弱信号识别研究综述: 寻找微弱的未来信号[J]. 情报学报, 2023, 42(8): 996-1008.
25 Ansoff H I. Implanting strategic management[M]. Englewood Cliffs: Prentice Hall International, 1984.
26 Hiltunen E. The future sign and its three dimensions[J]. Futures, 2008, 40(3): 247-260.
27 Julien P A, Andriambeloson E, Ramangalahy C. Networks, weak signals and technological innovations among SMEs in the land-based transportation equipment sector[J]. Entrepreneurship & Regional Development, 2004, 16(4): 251-269.
28 Yoon J, Kim K. Detecting signals of new technological opportunities using semantic patent analysis and outlier detection[J]. Scientometrics, 2012, 90(2): 445-461.
29 邵波, 宋继伟. 反竞争情报流程中的早期预警[J]. 情报杂志, 2007, 26(7): 43-45, 48.
30 邓胜利, 林艳青, 王野. 企业竞争弱信号的特征提取与定量识别研究[J]. 图书情报工作, 2016, 60(10): 67-75.
31 翟东升, 夏军, 张杰, 等. 基于专利新兴技术弱信号识别方法研究[J]. 情报杂志, 2015, 34(8): 31-36.
32 贾军, 魏洁云. 新兴产业核心技术早期识别方法与应用研究[J]. 科学学研究, 2018, 36(7): 1206-1214.
33 苗红, 郭鑫, 吴菲菲. 产业融合弱信号识别研究——以老年智能家居领域为例[J]. 情报杂志, 2020, 39(11): 54-60, 89.
34 Holopainen M, Toivonen M. Weak signals: ansoff today[J]. Futures, 2012, 44(3): 198-205.
35 Ilmola L, Kuusi O. Filters of weak signals hinder foresight: monitoring weak signals efficiently in corporate decision-making[J]. Futures, 2006, 38(8): 908-924.
36 党倩娜. 新兴技术弱信号监测模型及其特点[J]. 中国科技论坛, 2017(11): 158-164.
37 韩盟, 陈悦, 王玉奇, 等. 基于异类数据和语义建构的新兴技术弱信号识别研究[J]. 情报学报, 2024, 43(3): 302-312.
38 Vicente Gomila J M, Marro F P. Combining tech-mining and semantic-TRIZ for a faster and better technology analysis: a case in energy storage systems[J]. Technology Analysis & Strategic Management, 2013, 25(6): 725-743.
39 Kim J, Lee C. Novelty-focused weak signal detection in futuristic data: assessing the rarity and paradigm unrelatedness of signals[J]. Technological Forecasting and Social Change, 2017, 120: 59-76.
40 刘俊婉, 庞博, 徐硕. 基于弱信号的颠覆性技术早期识别研究[J]. 情报学报, 2023, 42(12): 1395-1411.
41 余辉, 吴昀璟, 夏文蕾, 等. “政府—市场—企业”三维视角下技术需求弱信号感知研究——以新能源汽车领域为例[J]. 情报理论与实践, 2025, 48(3): 106-116.
42 韩盟, 陈悦, 王玉奇, 等. 新兴技术弱信号识别: 理论模型与测度方法[J]. 科学学研究, 2024, 42(11): 2262-2274.
43 吴柯烨, 孙建军, 张力, 等. 弱链接突变视角下的技术机会识别研究[J]. 图书情报工作, 2024, 68(10): 81-96.
44 陈伟, 王莉晓. 颠覆性技术弱信号识别漏斗模型研究[J]. 图书情报工作, 2024, 68(8): 97-111.
45 王莉晓, 陈伟, 邱含琪. 基于机器学习的颠覆性技术弱信号识别模型研究[J]. 数据分析与知识发现, 2024, 8(8/9): 63-75.
46 Ma M, Mao J, Li G. Discovering weak signals of emerging topics with a triple-dimensional framework[J]. Information Processing & Management, 2024, 61(5): 103793.
47 Thorleuchter D, van den Poel D. Weak signal identification with semantic web mining[J]. Expert Systems with Applications, 2013, 40(12): 4978-4985.
48 Schwarz J O, Kroehl R, von der Gracht H A. Novels and novelty in trend research—using novels to perceive weak signals and transfer frames of reference[J]. Technological Forecasting and Social Change, 2014, 84: 66-73.
49 Moreira A L M, Hayashi T W N, Coelho G P, et al. A clustering method for weak signals to support anticipative intelligence[J]. International Journal of Artificial Intelligence and Expert Systems, 2015, 6(1): 1-14.
50 Guo Y, Sun G L, Zhang L L, et al. A new model based on patent data for technology early warning research[J]. International Journal of Technology Management, 2018, 77(4): 210-234.
51 史敏, 张圆, 罗建. 基于弱信号的潜在竞争对手识别方法研究[J]. 现代情报, 2022, 42(6): 104-111.
52 张慧玲, 许海云, 刘春江, 等. 科技创新弱信号早期感知方法探究与前瞻[J]. 情报学报, 2024, 43(10): 1129-1141.
53 Song C H, Elvers D, Leker J. Anticipation of converging technology areas—a refined approach for the identification of attractive fields of innovation[J]. Technological Forecasting and Social Change, 2017, 116: 98-115.
54 Park C, Cho S, Heo W G. Study on the future sign detection in areas of academic interest related to the digitalization of the energy industry[J]. Journal of Cleaner Production, 2021, 313: 127801.
55 Miao H, Guo X, Yuan F. Research on identification of potential directions of artificial intelligence industry from the perspective of weak signal[J]. IEEE Transactions on Engineering Management, 2024, 71: 465-480.
56 万劲波, 周小玲. 发展基于竞争情报系统的技术预见[J]. 研究与发展管理, 2004, 16(6): 97-102.
57 陈峰. 开展竞争情报与技术预见交叉研究的若干发现[J]. 图书情报工作, 2007, 51(2): 26-29.
58 黄彦敏, 孙成权, 吴新年. 国内外科技战略情报研究现状及我国的发展建议[J]. 图书与情报, 2007(1): 86-88.
59 仪德刚, 齐中英. 从技术竞争情报、技术预见到技术路线图——构建企业自主创新的内生模型[J]. 科技管理研究, 2007(3): 13-14, 18.
60 谭思明, 王淑玲, 王春玲. 面向政府决策的竞争情报服务平台的构建[J]. 现代情报, 2007(11): 6-9.
61 陈峰. 技术预见的战略情报研究属性及启示[J]. 图书情报工作, 2007, 51(7): 25-27, 53.
62 沈固朝. 信号分析: 竞争情报研究的又一重要课题[J]. 图书情报工作, 2009, 53(20): 11-14, 59.
63 沈固朝. 情报预测和预警研究要关注信号分析[J]. 图书情报工作, 2009, 53(20): 10.
64 赵小康. 基于弱信号的企业竞争情报搜集[J]. 图书情报工作, 2009, 53(20): 20-24.
65 Heinonen S, Karjalainen J, Ruotsalainen J, et al. Surprise as the new normal–implications for energy security[J]. European Journal of Futures Research, 2017, 5(1): Article No.12.
66 Mühlroth C, K?lbl L, Grottke M. Innovation signals: leveraging machine learning to separate noise from news[J]. Scientometrics, 2023, 128(5): 2649-2676.
67 Thorleuchter D, van den Poel D. Idea mining for web-based weak signal detection[J]. Futures, 2015, 66: 25-34.
68 Rotolo D, Hicks D, Martin B R. What is an emerging technology?[J]. Research Policy, 2015, 44(10): 1827-1843.
69 张慧玲, 许海云, 王超, 等. 弱信号环境下情报感知方法框架研究[J]. 情报理论与实践, 2023, 46(11): 9-19.
70 许明珠, 王克平, 范颜铄, 等. 企业信息迷雾弱信号识别与情报感知[J]. 图书情报工作, 2024, 68(19): 66-77.
71 Kim S, Kim Y E, Bae K J, et al. NEST: a quantitative model for detecting emerging trends using a global monitoring expert network and Bayesian network[J]. Futures, 2013, 52: 59-73.
72 Markmann C, Darkow I L, von der Gracht H. A Delphi-based risk analysis—identifying and assessing future challenges for supply chain security in a multi-stakeholder environment[J]. Technological Forecasting and Social Change, 2013, 80(9): 1815-1833.
73 Park C, Cho S. Analysis on trends and future signs of smart grids[J]. International Journal of Smart Grid and Clean Energy, 2020, 9(3): 533-543.
74 Xu H Y, Luo R, Winnink J, et al. A methodology for identifying breakthrough topics using structural entropy[J]. Information Processing & Management, 2022, 59(2): 102862.
75 Roh S, Choi J Y. Exploring signals for a nuclear future using social big data[J]. Sustainability, 2020, 12(14): 5563.
76 董悦, 赵小康, 刘彦祖. 技术预见中的信号分析[J]. 图书情报工作, 2009, 53(20): 25-29. |
|
|
|
