Topic Modeling: How and Why to Use in Management Research

José Eduardo Storopoli

doi:10.5585/ijsm.v18i3.14561

Authors

José Eduardo Storopoli Universidade Nove de Julho - UNINOVE https://orcid.org/0000-0002-0559-5176

DOI:

https://doi.org/10.5585/ijsm.v18i3.14561

Keywords:

Topic modeling, Latent Dirichlet allocation, Computer-aided text analysis, Machine learning, Big data

Abstract

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.

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Author Biography

José Eduardo Storopoli, Universidade Nove de Julho - UNINOVE

Doutor em Administração pela Universidade Nove de Julho - UNINOVE, São Paulo, (Brasil). Professor do Doutorado em Administração e do Mestrado em Cidades Inteligentes e Sustentáveis na Universidade Nove de Julho. Líder da Linha de Pesquisa Inovação Aplicada ao Planejamento Urbano do Mestrado em Cidades Inteligentes e Sustentáveis.

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APPENDIX I - Topic terms weights

First Topic - 0.212*"estratég" + 0.049*"organiz" + 0.042*"competi" + 0.040*"prát" + 0.036*"merc" + 0.036*"conceit" + 0.035*"empr" + 0.022*"vantag" + 0.018*"gerenc" + 0.016*"comport"

Second Topic - 0.055*"gest" + 0.047*"teor" + 0.042*"negóci" + 0.027*"internac" + 0.024*"caracterís" + 0.022*"futur" + 0.021*"decis" + 0.020*"abord" + 0.018*"país" + 0.017*"relacion"

Third Topic - 0.080*"desenvolv" + 0.043*"ambi" + 0.032*"inform" + 0.030*"empreend" + 0.029*"públic" + 0.026*"sustent" + 0.025*"instituc" + 0.023*"institu" + 0.022*"internacion" + 0.022*"empreendedor"

Fourth Topic - 0.090*"process" + 0.037*"conhec" + 0.030*"entrev" + 0.030*"context" + 0.028*"qualit" + 0.023*"perspec" + 0.023*"form" + 0.023*"particip" + 0.020*"envolv" + 0.018*"mudanç"

Fifth Topic - 0.125*"empr" + 0.043*"fat" + 0.042*"recurs" + 0.040*"brasil" + 0.032*"ativ" + 0.026*"estrut" + 0.023*"corpor" + 0.022*"financ" + 0.019*"efici" + 0.019*"capit"

Sixth Topic - 0.084*"inov" + 0.082*"desempenh" + 0.067*"capac" + 0.062*"organizac" + 0.059*"model" + 0.027*"dimens" + 0.024*"dinâm" + 0.024*"produt" + 0.022*"ges" + 0.018*"pequen"