Document Type
Article
Department/Program
Business
Pub Date
6-2017
Journal Title
Journal of Corporate Citizenship
Volume
66
Abstract
This study demonstrates the use of Text Data Mining (TDM) for exploring the content of a collection of Corporate Citizenship(CC) reports. The collection analyzed comprises CC reports produced by seven Dow Jones companies (Citi, Coca-Cola, ExxonMobil, General Motors, Intel, McDonalds and Microsoft) in2004, 2008 and 2012.Exploratory con-tent analysis using TDM enables insights for CC professionals and analysts, in less time using fewer resources, which in turn could help them explore collaboration opportunities around supply chains, re-training programs, and alternative risk mitigation strategies in terms of governance and compliance. In addition, TDM, using supervised machine learning on the whole collection (or corpus) as well as unsupervised machine learning on document collections by year, suggests the integration of CC considerations related to environmental sustain-ability in CC report components discussing the core business of some firms. This method has been used in many contexts in which a collection of documents needs to be categorized and/or analyzed to uncover new patterns and relationships.
DOI
10.9774/T&F.4700.2017.ju.00007
Journal Article URL
https://proxy.wm.edu/login?url=https://www.jstor.org/stable/26629170
Recommended Citation
Parra, Carlos M.; Tremblay, Monica; Paul, Karen; and Castellanos, Arturo, Exploratory Content Analysis Using Text Data Mining: Corporate Citizenship Reports of Seven US Companes from 2004 to 2012 (2017). Journal of Corporate Citizenship, 66.
10.9774/T&F.4700.2017.ju.00007
Publisher Statement
This article has been accepted for publication in Journal of Corporate Citizenship, published by Routledge (Greenleaf Press).
Included in
Business Administration, Management, and Operations Commons, Business Law, Public Responsibility, and Ethics Commons, Organizational Behavior and Theory Commons, Technology and Innovation Commons
