The Use of Latent Semantic Analysis in Operations Management Research
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Authors
Kulkarni, Shailesh S.
Apte, Uday M.
Evangelopoulos, Nicholas E.
Subjects
Big Data Analytics
Latent Semantic Analysis
Operations Management Research
Unstructured Text
Latent Semantic Analysis
Operations Management Research
Unstructured Text
Advisors
Date of Issue
2014-10
Date
October 2014
Publisher
Wiley
Language
Abstract
In this article, we introduce the use of Latent Semantic Analysis (LSA) as a technique
for uncovering the intellectual structure of a discipline. LSA is an emerging quantitative
method for content analysis that combines rigorous statistical techniques and scholarly
judgment as it proceeds to extract and decipher key latent factors. We provide a stepwise
explanation and illustration for implementing LSA. To demonstrate LSA’s ability to
uncover the intellectual structure of a discipline, we present a study of the field of
Operations Management. We also discuss a number of potential applications of LSA
to show how it can be used in empirical Operations Management research, specifically
in areas that can benefit from analyzing large volumes of unstructured textual data.
Type
Article
Description
Series/Report No
Department
Graduate School of Business & Public Policy (GSBPP)
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
Format
24 p.
Citation
Kulkarni, Shailesh S., Uday M. Apte, and Nicholas E. Evangelopoulos. "The use of latent semantic analysis in operations management research." Decision Sciences 45.5 (2014): 971-994.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.