Examining Competitive Intelligence Using External and Internal Data Sources: A Text Mining Approach

被引:0
|
作者
Xue, Yun [1 ]
Zhou, Yilu [2 ]
Dasgupta, Subhasish [1 ]
机构
[1] George Washington Univ, Washington, DC 20052 USA
[2] Fordham Univ, Bronx, NY 10458 USA
来源
关键词
Competitive Intelligence; Text Mining; Business Intelligence; Natural Language Processing;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Competitive intelligence (CI) is the practice of studying competitors and competitive environment in support of firm's strategic decision-making process. Currently, competitors are usually studied from business profile information and reports edited by CI professionals. While being inefficient and expensive in labor and resources, their results are often incomplete and lack objectivity. Some existing literatures introduced text mining to leverage Web information for CI usage. Despite improving on coverage, most of these analyses identify competitors using name co-occurrences from a single data source. The validity and reliability of these studies remain questionable. Our experiment demonstrates that syntactic level text mining can lead to improvements on CI performance. It also shows that the selection of different online data sources and competitor name extraction methods have different implications on CI outcome.
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页数:13
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