Text Mining in Organizational Research

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically in...

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Bibliographic Details
Published in:Organizational research methods 2018-07, Vol.21 (3), p.733-765
Main Authors: Kobayashi, Vladimer B., Mol, Stefan T., Berkers, Hannah A., Kismihók, Gábor, Den Hartog, Deanne N.
Format: Article
Language:English
Subjects:
Clustering
Data mining
End users
Environmental scanning
Job openings
Organization theory
Research management
Researchers
Texts
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Summary:Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.
ISSN:1094-4281
1552-7425
DOI:10.1177/1094428117722619