The Use of Latent Semantic Analysis in Operations Management Research

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.

Finding academic concerns of the Three Gorges Project based on a topic modeling approach

“…Using abstract texts as input data for topic modeling is a common strategy in previous studies which aim at finding topics in scientific articles (Griffiths and Steyvers, 2004;Kim and Yoon, 2014;Kulkarni et al, 2014;Zheng et al, 2006). Following this strategy, abstracts of the collected articles were used as the input data for topic modeling in this study.…”

“…A characteristic development in today's world is the volume and ubiquitous availability of structured as well as unstructured data and their consideration as a resource for mining valuable knowledge (Kulkarni et al, 2014). The tremendous growth of unstructured data sources, especially textual data sources, has inspired the development of new approaches to understand latent intelligence in a variety of events or disciplines (George et al, 2014;Wood et al, 2013).…”

“…Previous studies have used thematic reviews, manual content analysis and citation-cocitation analysis to perform literature review work. However, these approaches are considered to be time-consuming, subjective and difficult to process vast amounts of data (Kulkarni et al, 2014). In order to improve the efficiency of processing scientific documents, recently, text mining approaches, such as topic modeling, have been introduced into scientometric and bibliometric studies (Nichols, 2014;Yau et al, 2014) to help find the intellectual structure of a discipline.…”

Innovativeness as an Unintended Outcome of Franchising: Insights from Restaurant Chains

“…As a final alternative, we also tested the robustness of our findings by using a different measure of innovativeness developed using a software‐based text analysis of the news announcements (e.g., Sabherwal & Sabherwal, ; Kulkarni, Apte, & Evangelopoulos, ). To this end, we first developed a comprehensive list of words that indicated improvements in either product or process innovativeness.…”

The Role of Industry Studies and Public Policies in Production and Operations Management

“…We sampled a large body of papers from representative publications and identified relevant themes in the field of POM (Kulkarni et al. ). Admittedly, the sample and thus the findings will change in the future if the basic IS&PP themes change.…”