New technology foresight method based on intelligent knowledge management

doi:10.1007/s42524-019-0062-z

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)