Topic Modeling as a Strategy of Inquiry in Organizational Research: A Tutorial With an Application Example on Organizational Culture
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Research has emphasized the limitations of qualitative and quantitative approaches to studying organizational phenomena. For example, in-depth interviews are resource-intensive, while questionnaires with closed-ended questions can only measure predefined constructs. With the recent availability of large textual data sets and increased computational power, text mining has become an attractive method that has the potential to mitigate some of these limitations. Thus, we suggest applying topic modeling, a specific text mining technique, as a new and complementary strategy of inquiry to study organizational phenomena. In particular, we outline the potentials of structural topic modeling for organizational research and provide a step-by-step tutorial on how to apply it. Our application example builds on 428,492 reviews of Fortune 500 companies from the online platform Glassdoor, on which employees can evaluate organizations. We demonstrate how structural topic models allow to inductively identify topics that matter to employees and quantify their relationship with employees’ perception of organizational culture. We discuss the advantages and limitations of topic modeling as a research method and outline how future research can apply the technique to study organizational phenomena.
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The structural topic model followed the protocol described in Schmiedel et al. (2019) and was supported by the stm package in R.
“The creation of our structural topic model follows the protocol described in (Schmiedel et al., 2019) and is supported by the stm package in R (Roberts et al., 2019).”
From Ethical use of artificial intelligence based tools in higher education: Are future business leaders ready? (2025) · cites this paper as Schmiedel et al., 2019
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- Authors
- Schmiedel, T., Müller, O., & vom Brocke, J.
- Affiliations
- University of Liechtenstein (LI)
- IT University of Copenhagen (DK)
- University of Liechtenstein, Liechtenstein
- Corresponding author
- Schmiedel, Theresa
- ORCID
- Schmiedel, Theresa
- Venue
- Organizational Research Methods
- Publisher
- SAGE Publications
- Published
- 2018-05-06
- Volume
- 22
- Issue
- 4
- Pages
- 941-968
- ISSN
- 1094-4281 (ISSN-L)
- 1552-7425
- OpenAlex
- W2801182247
- Cited by
- 319
- References
- 51
- Citations by year
- 2026: 34 · 2025: 54 · 2024: 53 · 2023: 61 · 2022: 44 · 2021: 42 · 2020: 23 · 2019: 6 · 2018: 2
- FWCI
- 67.52
- Type
- journal article
- Language
- en
- Open access
- green
- pure.itu.dk
- Locations
- Organizational Research Methods (publishedVersion)
- IT University Of Copenhagen (IT University of Copenhagen) (submittedVersion, other-oa)
- Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- topic modeling, structural topic modeling, text mining, organizational culture, employee perceptions, Glassdoor reviews, Fortune 500 companies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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