Topic Modeling: How and Why to Use in Management Research

• Autor(a) principal: Storopoli, José Eduardo
• Data de Publicação: 2019
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Ibero Americana de Estratégia - RIAE
• Texto Completo: https://periodicos.uninove.br/riae/article/view/14561
• topic: management; business; machine learning; Topic modeling; Latent Dirichlet allocation; Computer-aided text analysis; Machine learning; Big data

Objective: To exemplify how topic modeling can be used in management research, my objectives are two-fold. First, I introduce topic modeling as a social sciences research tool and map critical published studies in management and other social sciences that employed topic modeling in a proper manner. Second, I illustrate how to do topic modeling by applying topic modeling in an analysis of the last five years of published research in this journal: the Iberoamerican Journal of Strategic Management (IJSM). Methodology: I analyze the last five years (2014 to 2018) of published articles in the IJSM. The sample is 164 articles. The abstracts were subjected to a standard topic modeling text pre-processing routine, generating 1,252 unique tokens. Originality/Relevance: By proposing topic modeling as a valid and opportunistic methodology for analyzing textual data, it can shift the old paradigm that textual data belongs only to the qualitative realm. Furthermore, allowing textual data to be labeled and quantified in a reproducible manner that mitigates (or closely fully eliminates) researcher bias. Main Results:  Six topics were generated through Latent Dirichlet Allocation (LDA): Topic 1 – Strategy and Competitive Advantage; Topic 2 – International Business and Top Management Team; Topic 3 – Entrepreneurship; Topic 4 – Learning and Cooperation; Topic 5 – Finance and Strategy; and Topic 6 – Dynamic Capabilities. Theoretical/methodological Contributions: I present the state of the art of the literature published in IJSM and also show how the reader can perform their own topic modeling. The full data and code that was used are available in free open science repositories in Open Science Framework (OSF) and GitHub.