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New Technology of Library and Information Service  2008, Vol. 24 Issue (6): 56-60    DOI: 10.11925/infotech.1003-3513.2008.06.11
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Application Research on Monitoring & Analysis in Hi-Tech Industry’s Technology Innovation
Zhang Cheng1  Zhu Donghua2  Xu Zhijun1
1(Zhuhai Branch of Guangdong Co., Ltd., China Mobile Group, Zhuhai 519015, China)
2(School of Management & Economics, Beijing Institute of Technology, Beijing 100081, China)
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Abstract  

 According to the characteristics of the hi-tech industry, this paper researches on monitoring & analysis of technology innovation, sets up an index system of analysis. And it takes the carbon nanotubes industry as the research object to carry on the empirical research. The purpose is to understand the hi-tech industry’s technology innovation present condition and the future development directions, draw up the strategy to provide the decision reference for the government and business enterprise.

Key wordsHi-tech industry      Technology innovation      Monitoring &      analysis      Carbon nanotubes     
Received: 12 March 2008      Published: 25 June 2008
: 

F276.44

 
Corresponding Authors: Zhang Cheng     E-mail: taishanking@139.com
About author:: Zhang Cheng,Zhu Donghua,Xu Zhijun

Cite this article:

Zhang Cheng,Zhu Donghua,Xu Zhijun. Application Research on Monitoring & Analysis in Hi-Tech Industry’s Technology Innovation. New Technology of Library and Information Service, 2008, 24(6): 56-60.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2008.06.11     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2008/V24/I6/56

[1] 李维胜,秦长文.高技术企业发展的现状分析及对策建议[J].中国科技论坛,2005(4):70-73.
[2] 张德贤,陈中慧,戴桂林. 高新技术产业化协同过程探讨[J].中国管理科学,1997,5(4):47-51.
[3] Lee H,Deguchi H.Technological Innovation of High-tech Industry and Patent Policy - Agent Based Simulation with Double Loop Learning[C].  Proceeding of 4th Pacific Rim International Workshop on Multi-Agents, Intelligent Agents: Specification Modeling and Application, 2001:168-82.
[4] Ku  Y  L, Liau  S J, Hsing W C. The High-tech Milieu and Innovation-oriented Development[J].Technovation,2005,25(2):145-153.
[5] 穆荣平. 中国高新技术产业国际竞争力评价指标研究[J]. 中国科技论坛,2000(3):28-31.
[6] 赵兰香,吴灼亮. 中国高新技术产业竞争力分析[J].中国创业投资与高科技,2006(2):46-47.
[7] 朱东华, 袁军鹏. 基于数据挖掘的科技监测方法研究 [J]. 管理工程学报,2004(4):135-139.
[8] Zhu D, Porter A L. Automated Extraction and Visualization of Information for Technological Intelligence and Forecasting[J].Technological Forecasting & Social Change, 2002(69):495-506.
[9] 叶苏,顾新,杨早林,等. 国外高技术企业运用专利制度的策略及趋势研究[J]. 软科学,2005,19(4):84-87.
[10] 马晓光,沈全锋,于浩. 高技术领域急需专利保驾护航[J].科研管理,2002,23(5):20-25.
[11] 汤才祥. 透过中国专利文献分类信息看我国高新技术产业[J].电子知识产权,2003(6):11-15.
[12] 郭韬. 高新技术企业成长生命周期中的组织创新[J]. 工业技术经济,2005,24(7):72-76.
[13] 任智军,朱东华,荆雷. 基于可视化数据挖掘的管理科学科技文本分析研究[J]. 科学学与科学技术管理,2006(1):8-12.
[14] 余翔,蒋文光. 世界纳米专利比较分析和我国纳米专利战略研究[J].研究与发展管理,2004,16(4):85-90.
[15] 李亚青,贾杲,邢润川. 技术创新与纳米技术产业化问题[J]. 科学学研究,2000,18(1):83-90.

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