Topic modeling in management research: Rendering new theory from textual data

Timothy R. Hannigan, Richard F.J. Haan, Keyvan Vakili, Hovig Tchalian, Vern L. Glaser, Milo Shaoqing Wang, Sarah Kaplan, P. Devereaux Jennings

Research output: Contribution to journalArticlepeer-review

80 Scopus citations

Abstract

Increasingly, management researchers are using topic modeling, a new method borrowed from computer science, to reveal phenomenon-based constructs and grounded conceptual relationships in textual data. By conceptualizing topic modeling as the process of rendering constructs and conceptual relationships from textual data, we demonstrate how this new method can advance management scholarship without turning topic modeling into a black box of complex computer-driven algorithms. We begin by comparing features of topic modeling to related techniques (content analysis, grounded theorizing, and natural language processing). We then walk through the steps of rendering with topic modeling and apply rendering to management articles that draw on topic modeling. Doing so enables us to identify and discuss how topic modeling has advanced management theory in five areas: detecting novelty and emergence, developing inductive classification systems, understanding online audiences and products, analyzing frames and social movements, and understanding cultural dynamics. We conclude with a review of new topic modeling trends and revisit the role of researcher interpretation in a world of computer-driven textual analysis.

Original languageEnglish (US)
Pages (from-to)586-632
Number of pages47
JournalAcademy of Management Annals
Volume13
Issue number2
DOIs
StatePublished - Jul 2019
Externally publishedYes

ASJC Scopus subject areas

  • Business and International Management
  • Organizational Behavior and Human Resource Management

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