Shi, Zhan, Gene Moo Lee, Andrew B. Whinston (2016) Toward a Better Measure of Business Proximity: Topic Modeling for Industry Intelligence. MIS Quarterly 40(4), pp. 1035-1056.
- Business proximity demo site: https://misr.sauder.ubc.ca/bizprox/
- Media coverage: [Huffington Post] [ACM TechNews] [UTA Inquiry] [W.P. Carey KnowIT]
- Presented in ACM EC (Stanford, CA 2014), MISQ Workshop (Lueven, Belgium 2015), KOCSEA (Vienna, VA 2016), UBC (2016), UT Arlington (2017), Rutgers Business School (2018)
- Dissertation Paper #1
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.