Topic Modeling in Management Research: Rendering New Theory from Textual Data

Co-author(s): Tim Hannigan (UAlberta), Richard Haans (Rotterdam School of Management), Hovig Tchalian (Claremont), Vern Glaser (UAlberta), Milo Wang (UAlberta), Sarah Kaplan (Rotman), Dev Jennings (Ualberta)

Published in Academy of Management Annals, 2019

Recommended citation: Hannigan, T., Haans, R.F.J., Vakili, K., Tchalian, H., Glaser, V., Wang, M., Kaplan, S., Jennings, P.D. 2019. "Topic Modeling in Management Research: Rendering New Theory from Textual Data.." Academy of Management Annals, 13(2): 586-632.

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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.

Keywords: topic modeling, management theory, rendering, text analysis, big data, theory building, qualitative analysis, mixed methods