TOWARD A BETTER MEASURE OF BUSINESS PROXIMITY: TOPIC MODELING FOR INDUSTRY INTELLIGENCE

• 成果类型: Article
• 署名作者: Shi, Zhan (Michael); Lee, Gene Moo; Whinston, Andrew B.
• 署名单位: Arizona State University; Arizona State University-Tempe; University of Texas System; University of Texas Arlington; University of Texas System; University of Texas Austin
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2016/40.4.11
• 发表日期: 2016
• 页码: 1035-+
• 关键词: technological overlap; alliance formation; systems; INVESTMENT; COMPLEMENTARITY; acquisition; analytics; networks

摘要

In this article, we propose a new data-analytic approach to measure firms' dyadic business proximity. Specifically, our method analyzes the unstructured texts that describe firms' businesses using the statistical learning technique of topic modeling, and constructs a novel business proximity measure based on the output. When compared with existent methods, our approach is scalable for large datasets and provides finer granularity on quantifying firms' positions in the spaces of product, market, and technology. We then validate our business proximity measure in the context of industry intelligence and show the measure's effectiveness in an empirical application of analyzing mergers and acquisitions in the U. S. high technology industry. Based on the research, we also build a cloud-based information system to facilitate competitive intelligence on the high technology industry.