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The Use of Latent Semantic Analysis in Operations Management Research
Author(s) -
Kulkarni Shailesh S.,
Apte Uday M.,
Evangelopoulos Nicholas E.
Publication year - 2014
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12095
Subject(s) - latent semantic analysis , computer science , decipher , field (mathematics) , data science , latent variable , key (lock) , information retrieval , artificial intelligence , genetics , mathematics , computer security , biology , pure mathematics
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.