TOward a better measure of business proximity: Topic modeling for industry intelligence

Zhan Michael Shi, Gene Moo Lee, Andrew B. Whinston

Research output: Contribution to journalArticlepeer-review

96 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1035-1056
Number of pages22
JournalMIS Quarterly: Management Information Systems
Issue number4
StatePublished - 2020


  • Big data analytics
  • Business proximity
  • Industry intelligence
  • Information system
  • Topic modeling

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Computer Science Applications
  • Information Systems and Management


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