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dc.contributor.authorKulkarni, Shailesh S.
dc.contributor.authorApte, Uday
dc.contributor.authorEvangelopoulos, Nicholas E.
dc.date.accessioned2018-08-02T20:04:45Z
dc.date.available2018-08-02T20:04:45Z
dc.date.issued2014-10
dc.identifier.citationKulkarni, 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.
dc.identifier.urihttp://hdl.handle.net/10945/59361
dc.description.abstractIn 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.
dc.publisherDecision Sciences Institute
dc.rightsThis 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.en_US
dc.titleThe Use of Latent Semantic Analysis in Operations Management Researchen_US
dc.typeArticleen_US
dc.contributor.departmentBusiness & Public Policy (GSBPP)
dc.subject.authorBig Data Analytics
dc.subject.authorLatent Semantic Analysis
dc.subject.authorOperations Management Research
dc.subject.authorand and Unstructured Text


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