图书情报工作 ›› 2016, Vol. 60 ›› Issue (10): 67-75.DOI: 10.13266/j.issn.0252-3116.2016.10.010

• 情报研究 • 上一篇    下一篇

企业竞争弱信号的特征提取与定量识别研究

邓胜利1, 林艳青1, 王野2   

  1. 1. 武汉大学信息资源研究中心 武汉 430072;
    2. 天津大学管理与经济学部 天津 300072
  • 收稿日期:2016-03-30 修回日期:2016-05-03 出版日期:2016-05-20 发布日期:2016-05-20
  • 作者简介:邓胜利(ORCID:0000-0001-7489-4439),教授,博士,博士生导师,E-mail:victorydc@sina.com;林艳青(ORCID:0000-0002-5956-034X),硕士研究生;王野(ORCID:0000-0003-2532-5437),硕士研究生
  • 基金资助:
    本文系教育部人文社会科学重点研究基地重大项目"我国服务业信息化推进与保障机制研究"(项目编号:15JJD870001)研究成果之一。

Research on the Features and Quantitative Identification of Competitive Weak Signals in Firms

Deng Shengli1, Lin Yanqing1, Wang Ye2   

  1. 1. Center for Studies of Information Resources, Wuhan University, Wuhan 430072;
    2. College of Management and Economics, Tianjin University, Tianjin 300072
  • Received:2016-03-30 Revised:2016-05-03 Online:2016-05-20 Published:2016-05-20

摘要: [目的/意义] 针对当前对企业弱信号定量研究不足的现状,通过层次分析和隶属度函数构建新方法对其进行定量识别,为企业战略决策管理和危机预警提供服务。[方法/过程] 首先,基于波特的五力模型构建七力模型,结合实证分析提取企业竞争弱信号的特征;其次,结合层次分析法构建影响因素指标体系,并计算各指标的具体权重;最后,结合隶属度函数对弱信号进行定量识别。[结果/结论] 通过层次分析法和隶属度函数,构建了企业竞争弱信号的定量识别方法,为企业战略决策管理和危机管理提供服务。

关键词: 大数据, 竞争弱信号, 特征提取, 层次分析, 隶属度函数

Abstract: [Purpose/significance] Since there existed the lack of quantitative research on weak signals of competitive intelligence in firms, this paper constructed a new method to identify weak signals quantitatively through Analytic Hierarchy Process (AHP) and Degree of Membership Function.[Method/process] Firstly, based on Potter's five forces model, seven forces model has been proposed, and the characteristics of weak signals were extracted combined with an empirical analysis. Then the index matrix is constructed based on the AHP, and the weight of each index are calculated. After that, this paper conducted the quantitative identification of weak signals via Degree of Membership Function.[Result/conclusion] The quantitative identification method of weak signals in firm competition was successfully established through AHP and Degree of Membership Function.

Key words: big data, weak signal in CI, feature extraction, AHP, membership functions

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