Toward a Better Measure of Business Proximity: Topic Modeling for Industry Intelligence

MIS Quarterly, 40(4), 1035-1056

Posted: 25 Oct 2015 Last revised: 8 Jul 2017

See all articles by Zhan Shi

Zhan Shi

Arizona State University (ASU) - W.P. Carey School of Business

Gene Moo Lee

University of British Columbia (UBC) - Sauder School of Business

Andrew B. Whinston

University of Texas at Austin - Department of Information, Risk and Operations Management

Date Written: October 23, 2015

Abstract

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.

Keywords: Big Data analytics, business proximity, topic modeling, industry intelligence, information system

Suggested Citation

Shi, Zhan and Lee, Gene Moo and Whinston, Andrew B., Toward a Better Measure of Business Proximity: Topic Modeling for Industry Intelligence (October 23, 2015). MIS Quarterly, 40(4), 1035-1056. Available at SSRN: https://ssrn.com/abstract=2676630

Zhan Shi (Contact Author)

Arizona State University (ASU) - W.P. Carey School of Business ( email )

Tempe, AZ 85287-3706
United States

Gene Moo Lee

University of British Columbia (UBC) - Sauder School of Business ( email )

2053 Main Mall
Vancouver, BC V6T 1Z2
Canada

Andrew B. Whinston

University of Texas at Austin - Department of Information, Risk and Operations Management ( email )

CBA 5.202
Austin, TX 78712
United States
512-471-8879 (Phone)

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