Please wait a minute...

Frontiers of Engineering Management

Front. Eng    2020, Vol. 7 Issue (2) : 238-247     https://doi.org/10.1007/s42524-019-0062-z
RESEARCH ARTICLE
New technology foresight method based on intelligent knowledge management
Lingling ZHANG1(), Siting HUANG2
1. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China; Research Center on Fictitious Economy & Data Science, Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China
2. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
Download: PDF(383 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

The increasing importance of technology foresight has simultaneously raised the significance of methods that determine crucial areas and technologies. However, qualitative and quantitative methods have shortcomings. The former involve high costs and many limitations, while the latter lack expert experience. Intelligent knowledge management emphasizes human–machine integration, which combines the advantages of expert experience and data mining. Thus, we proposed a new technology foresight method based on intelligent knowledge management. This method constructs a technological online platform to increase the number of participating experts. A secondary mining is performed on the results of patent analysis and bibliometrics. Thus, forward-looking, innovative, and disruptive areas and relevant experts must be discovered through the following comprehensive process: Topic acquisition → topic delivery → topic monitoring → topic guidance → topic reclamation → topic sorting → topic evolution → topic conforming → expert recommendation.

Keywords technology foresight      intelligent knowledge management      technological online platform     
Corresponding Author(s): Lingling ZHANG   
Just Accepted Date: 17 December 2019   Online First Date: 02 January 2020    Issue Date: 27 May 2020
 Cite this article:   
Lingling ZHANG,Siting HUANG. New technology foresight method based on intelligent knowledge management[J]. Front. Eng, 2020, 7(2): 238-247.
 URL:  
http://journal.hep.com.cn/fem/EN/10.1007/s42524-019-0062-z
http://journal.hep.com.cn/fem/EN/Y2020/V7/I2/238
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Lingling ZHANG
Siting HUANG
Fig.1  Transformation process: Data → Rough Knowledge → Intelligent Knowledge → New Knowledge (D: data; i: individual; g: group; K: knowledge).
Fig.2  Technology foresight frame based on intelligent knowledge management.
Fig.3  Relationship among technological online platform, transformation process, and moderators.
1 B P Abraham, S D Moitra (2001). Innovation assessment through patent analysis. Technovation, 21(4): 245–252
https://doi.org/10.1016/S0166-4972(00)00040-7
2 S S Anand, D A Bell, J G Hughes (1996). EDM: A general framework for data mining based on evidence theory. Data & Knowledge Engineering, 18(3): 189–223
https://doi.org/10.1016/0169-023X(95)00038-T
3 A L Antonio, M J Chang, K Hakuta, D A Kenny, S Levin, J F Milem (2004). Effects of racial diversity on complex thinking in college students. Psychological Science, 15(8): 507–510
https://doi.org/10.1111/j.0956-7976.2004.00710.x pmid: 15270993
4 G Z Bai, Y R Zheng, X N Wu, J B Jin, Q Y Liu (2017). Research and demonstration on forecasting method of disruptive technology based on literature knowledge correlation. Journal of Intelligence, 36(9): 38–44 (in Chinese)
5 D Bang, C D Frith (2017). Making better decisions in groups. Royal Society Open Science, 4(8): 170193
https://doi.org/10.1098/rsos.170193 pmid: 28878973
6 K K Brockhoff (2002). Indicators of firm patent activities. In: Technology Management: The New International Language. IEEE, 476–481
7 L Cao (2010). Domain-driven data mining: Challenges and prospects. IEEE Transactions on Knowledge and Data Engineering, 22(6): 755–769
https://doi.org/10.1109/TKDE.2010.32
8 L Cascini, G Fornaro, D Peduto (2009). Analysis at medium scale of low-resolution DInSAR data in slow-moving landslide-affected areas. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6): 598–611
https://doi.org/10.1016/j.isprsjprs.2009.05.003
9 M S Celiktas, G Kocar (2012). Foresight analysis of wind power in Turkey. International Journal of Energy Research, 36(6): 737–748
https://doi.org/10.1002/er.1829
10 J F Courtney (2001). Decision making and knowledge management in inquiring organizations: Toward a new decision-making paradigm for DSS. Decision Support Systems, 31(1): 17–38
https://doi.org/10.1016/S0167-9236(00)00117-2
11 K Czaplicka-Kolarz, K Stańczyk, K Kapusta (2009). Technology foresight for a vision of energy sector development in Poland till 2030. Delphi survey as an element of technology foresighting. Technological Forecasting and Social Change, 76(3): 327–338
https://doi.org/10.1016/j.techfore.2008.05.007
12 E Davenport, B Cronin (2000). The citation network as a prototype for representing trust in virtual environments. In: The Web of Knowledge: A Festschrift in Honor of Eugene Garfield. Medford, NJ: Information Today, 517–534
13 S A W Drew (2006). Building technology foresight: Using scenarios to embrace innovation. European Journal of Innovation Management, 9(3): 241–257
https://doi.org/10.1108/14601060610678121
14 N Elgendy, A Elragal (2014). Big data analytics: A literature review paper. In: Perner P, ed. Advance in Data Mining. Applications and Theoretical Aspects. ICDM 2014. Cham: Springer, 214–227
15 W Fang, X W Cao, X W Gao (2017). Technology forecasting and foresight: Concepts, methods, and practices. Global Science, Technology and Economy Outlook, 32(3): 46–53 (in Chinese)
16 L G Georghiou, P Halfpenny (1996). Equipping researchers for the future. Nature, 383(6602): 663–664
https://doi.org/10.1038/383663a0
17 H Grupp, H A Linstone (1999). National technology foresight activities around the globe: Resurrection and new paradigms. Technological Forecasting and Social Change, 60(1): 85–94
https://doi.org/10.1016/S0040-1625(98)00039-0
18 W E Halal (2013). Forecasting the technology revolution: Results and learnings from the TechCast project. Technological Forecasting and Social Change, 80(8): 1635–1643
https://doi.org/10.1016/j.techfore.2013.02.008
19 L Hong, S E Page (2004). Groups of diverse problem solvers can outperform groups of high-ability problem solvers. Proceedings of the National Academy of Sciences of the United States of America, 101(46): 16385–16389
https://doi.org/10.1073/pnas.0403723101 pmid: 15534225
20 M Hussain, E Tapinos, L Knight (2017). Scenario-driven roadmapping for technology foresight. Technological Forecasting and Social Change, 124: 160–177
https://doi.org/10.1016/j.techfore.2017.05.005
21 S Jun, S Park, D Jang (2015). A technology valuation model using quantitative patent analysis: A case study of technology transfer in big data marketing. Emerging Markets Finance & Trade, 51(5): 963–974
https://doi.org/10.1080/1540496X.2015.1061387
22 D Kanama (2013). Development of technology foresight: Integration of technology roadmapping and the Delphi Method. In: Moehrle M G, Isenmann R, Phaal R, eds. Technology Roadmapping for Strategy and Innovation. Berlin, Heidelberg: Springer, 151–171
23 J E Karlsen (2014). Design and application for a replicable foresight methodology bridging quantitative and qualitative expert data. European Journal of Futures Research, 2(1): 40
https://doi.org/10.1007/s40309-014-0040-y
24 K V Katsikopoulos, A J King (2010). Swarm intelligence in animal groups: When can a collective out-perform an expert? PLoS One, 5(11): e15505
https://doi.org/10.1371/journal.pone.0015505 pmid: 21124803
25 S Liang, X T Ji, Y Li (2015). Application of patent scientometrics methods in technology foresight—Take the new energy automobile as an example. Journal of Intelligence, 34(2): 73–78 (in Chinese)
26 S Liang, Z F Li (2017). Shaping the future: The possibility and reliability of technology foresight. Studies in Dialectics of Nature, 33(7): 25–30 (in Chinese)
27 Y F Liu, Y Zhou, L Liao (2016). Application of big data analysis method in technology foresight for strategic emerging industries. Strategic Study of CAE, 18(4): 121–128 (in Chinese)
28 D L Loyd, C S Wang, K W Phillips, Jr R B Lount (2013). Social category diversity promotes premeeting elaboration: The role of relationship focus. Organization Science, 24(3): 757–772
https://doi.org/10.1287/orsc.1120.0761
29 J Luan (2002). Data mining and knowledge management in higher education, potential applications. In: Workshop Associate of International Conference. Toronto: 1–18
30 A Magruk (2011). Innovative classification of technology foresight methods. Technological and Economic Development of Economy, 17(4): 700–715
https://doi.org/10.3846/20294913.2011.649912
31 B R Martin, R Johnston (1999). Technology foresight for wiring up the national innovation system: Experiences in Britain, Australia, and New Zealand. Technological Forecasting and Social Change, 60(1): 37–54
https://doi.org/10.1016/S0040-1625(98)00022-5
32 K McGarry (2005). A survey of interestingness measures for knowledge discovery. Knowledge Engineering Review, 20(1): 39–61
https://doi.org/10.1017/S0269888905000408
33 Jr J W Murry, J O Hammons (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4): 423–436
https://doi.org/10.1353/rhe.1995.0008
34 I Nonaka, R Toyama, N Konno (2001). SECI, Ba, and leadership: A unified model of dynamic knowledge creation. Long Range Planning, 33(1): 5–34
35 C R Østergaard, B Timmermans, K Kristinsson (2011). Does a different view create something new? The effect of employee diversity on innovation. Research Policy, 40(3): 500–509
https://doi.org/10.1016/j.respol.2010.11.004
36 C Pietrobelli, F Puppato (2016). Technology foresight and industrial strategy. Technological Forecasting and Social Change, 110(3): 117–125
https://doi.org/10.1016/j.techfore.2015.10.021
37 Y Qiao (2013). Application of patent bibliometrics methods in technology foresight—Take the subsection of metallurgy as an example. Journal of Intelligence, 32(4): 34–37+27 (in Chinese)
38 H Y Ren, L T Yu, F F Wang (2016). Hotpots and trends of technology foresight research at home and abroad. Journal of Intelligence, 35(2): 81–87+115 (in Chinese)
39 G J Schaeffer, M A Uyterlinde (1998). Fuel cell adventures. Dynamics of a technological community in a quasi-market of technological options. Journal of Power Sources, 71(1–2): 256–263
https://doi.org/10.1016/S0378-7753(97)02741-9
40 K S Shin, I Han (2001). A case-based approach using inductive indexing for corporate bond rating. Decision Support Systems, 32(1): 41–52
https://doi.org/10.1016/S0167-9236(01)00099-9
41 L M Spinosa, C O Quandt, M P Ramos (2002). Toward a knowledge-based framework to foster innovation in networked organisations. In: The 7th International Conference on Computer Supported Cooperative Work in Design. IEEE, 308–313
https://doi.org/10.1109/CSCWD.2002.1047706
42 E Stiles, X Cui (2010). Workings of collective intelligence within open source communities. In: International Conference on Social Computing, Behavioral Modeling, and Prediction. Berlin, Heidelberg: Springer, 282–289
43 S Takahashi, H Owan, T Tsuru, K Uehara (2014). Multitasking incentives and biases in subjective performance evaluation. Technical Report. Kunitachi, Japan: Institute of Economic Research, Hitotsubashi University
44 D Thorleuchter, D Van den Poel (2013). Web mining based extraction of problem solution ideas. Expert Systems with Applications, 40(10): 3961–3969
https://doi.org/10.1016/j.eswa.2013.01.013
45 G Tichy (2004). The over-optimism among experts in assessment and foresight. Technological Forecasting and Social Change, 71(4): 341–363
https://doi.org/10.1016/j.techfore.2004.01.003
46 P Wack (2017). Shooting the rapids. Historical Evolution of Strategic Management, I and II(1): 121
47 Z L Wang, Q Guan, J Lan (2015). Bibliometric analysis of domestic technology foresight research. Journal of Modern Information, 35(4): 98–101+107 (in Chinese)
48 W Wells, R Spence-Stone, S Moriarty, J Burnett (2008). Advertising Principles and Practice. Australian ed. Sydney, Australia: Pearson Education
49 C H Willyard, C W McClees (1987). Motorola’s technology roadmap process. Research Management, 30(5): 13–19
https://doi.org/10.1080/00345334.1987.11757057
50 J P Yoon, L Kerschberg (1993). A framework for knowledge discovery and evolution in databases. IEEE Transactions on Knowledge and Data Engineering, 5(6): 973–979
https://doi.org/10.1109/69.250080
51 F Zhang, Y Kuang (2016). The implementation and enlightenment of Japan’s 10th science and technology foresight. Journal of Intelligence, 35(12): 12–15+11 (in Chinese)
52 L Zhang, J Li, Y Shi, X Liu (2009a). Foundations of intelligent knowledge management. Human Systems Management, 28(4): 145–161
53 L L Zhang, J Li, A H Li, P Zhang, G L Nie, Y Shi (2009b). A new research field: Intelligent knowledge management. In: 2009 International Conference on Business Intelligence and Financial Engineering. IEEE, 450–454
54 L Zhang, J Li, Q Zhang, F Meng, W Teng (2019a). Domain knowledge-based link prediction in customer-product bipartite graph for product recommendation. International Journal of Information Technology & Decision Making (IJITDM), 18(1): 311–338
https://doi.org/10.1142/S0219622018410031
55 L Zhang, M Zhao, Z Feng (2019b). Research on knowledge discovery and stock forecasting of financial news based on domain ontology. International Journal of Information Technology & Decision Making(IJITDM), 18(3): 953–979
https://doi.org/10.1142/S0219622019500160
56 L L Zhang, M H Zhao, Q Wang (2016). Research on knowledge sharing and transfer in remanufacturing engineering management based on SECI model. Frontiers of Engineering Management, 3(2): 136–143
https://doi.org/10.15302/J-FEM-2016030
57 M H Zhao, L L Zhang, L B Zhang, F Wang (2018). Research on technology foresight method based on intelligent convergence in open network environment. In: International Conference on Computational Science. Cham: Springer, 737–747
58 Y Zhou, H L Liu, L Liao, L Xue (2017). Literature review of quantitative technology foresight methods based on topic modeling. Science and Technology Management Research, 37(11): 185–196 (in Chinese)
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed