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Topic Modeling as a Strategy of Inquiry in Organizational Research: A Tutorial With an Application Example on Organizational Culture
Open accessAbstract
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 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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Cites
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Seeking Qualitative Rigor in Inductive Research: Notes on the Gioia Methodology
Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013) · Organizational Research Methods · doi:10.1177/1094428112452151 · Cited by 13,227
The Handbook of Social Psychology
Gravatt, A. E., Lindzey, G. E., & Aronson, F. (1969) · The Family Coordinator · doi:10.2307/582242 · Cited by 12,518
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150
Weber, R. (1990) · doi:10.4135/9781412983488 · Cited by 7,750
Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516
Culture, leadership, and organizations: the GLOBE study of 62 societies
(2005) · Choice Reviews Online · doi:10.5860/choice.42-4132 · Cited by 5,058
Harris, Z. S. (1954) · WORD · doi:10.1080/00437956.1954.11659520 · Cited by 4,807
Journal of Statistical Software
de Leeuw, J. (2009) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.10 · Cited by 3,651
Quantitative Analysis of Culture Using Millions of Digitized Books
Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., The Google Books Team, Pickett, J. P., Hoiberg, D., Clancy, D., Norvig, P., et al. (2010) · Science · doi:10.1126/science.1199644 · Cited by 3,235
From Frequency to Meaning: Vector Space Models of Semantics
Turney, P. D., & Pantel, P. (2010) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2934 · Cited by 2,949
(2009) · Philosophical Investigations · doi:10.1111/(issn)1467-9205 · Cited by 2,366
Schein, E. H. (1990) · American Psychologist · doi:10.1037/0003-066x.45.2.109 · Cited by 2,244
Understanding Power and Rules of Thumb for Determining Sample Sizes
VanVoorhis, C. R. W., & Morgan, B. L. (2007) · Tutorials in Quantitative Methods for Psychology · doi:10.20982/tqmp.03.2.p043 · Cited by 1,946
Duriau, V. J., Reger, R. K., & Pfarrer, M. D. (2007) · Organizational Research Methods · doi:10.1177/1094428106289252 · Cited by 1,651
Jindal, N., & Liu, B. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341560 · Cited by 1,542
A Survey of Opinion Mining and Sentiment Analysis
Liu, B., & Zhang, L. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3223-4_13 · Cited by 1,356
Reclaiming Qualitative Methods for Organizational Research: A Preface
Maanen, J. V. (1979) · Administrative Science Quarterly · doi:10.2307/2392358 · Cited by 1,276
DiMaggio, P., Nag, M., & Blei, D. M. (2013) · Poetics · doi:10.1016/j.poetic.2013.08.004 · Cited by 1,233
Grounded Theory for Qualitative Research: A Practical Guide
Urquhart, C. (2013) · doi:10.4135/9781526402196 · Cited by 1,041
Chatman, J. A., & Jehn, K. A. (1994) · Academy of Management Journal · doi:10.2307/256699 · Cited by 805
A Model of Text for Experimentation in the Social Sciences
Roberts, M. E., Stewart, B., & Airoldi, E. M. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1141684 · Cited by 791
Measures of response agreement for qualitative data: Some generalizations and alternatives.
Light, R. J. (1971) · Psychological Bulletin · doi:10.1037/h0031643 · Cited by 747 · cited 1×
“Consolidating the individual coding results, the topic labeling exercise revealed high intercoder reliability with an intercoder agreement of 86% and an average kappa value of 0.86 (Light, 1971; Moore & Benbasat, 1991).”
BackgroundInfluentialScherbaum, C. A., & Ferreter, J. M. (2009) · Organizational Research Methods · doi:10.1177/1094428107308906 · Cited by 702
Overcoming the J-shaped distribution of product reviews
Hu, N., Zhang, J., & Pavlou, P. A. (2009) · Communications of the ACM · doi:10.1145/1562764.1562800 · Cited by 702
Studies in linguistic analysis
Firth, J. R. (1957) · Medical Entomology and Zoology · Cited by 694
How to analyze political attention with minimal assumptions and costs
Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2009.00427.x · Cited by 671
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
Miner, G. D. (2012) · Elsevier eBooks · doi:10.1016/c2010-0-66188-8 · Cited by 566
Taking the Measure of Work: A Guide to Validated Scales for Organizational Research and Diagnosis
Fields, D. (2002) · Sage eBooks · doi:10.4135/9781452231143 · Cited by 563
Manipulation of online reviews: An analysis of ratings, readability, and sentiments
Hu, N., Bose, I., Koh, N. S., & Liu, L. (2011) · Decision Support Systems · doi:10.1016/j.dss.2011.11.002 · Cited by 531
Taras, V., Rowney, J. I. A., & Steel, P. (2009) · Journal of International Management · doi:10.1016/j.intman.2008.08.005 · Cited by 505
Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures
Bao, Y., & Datta, A. (2014) · Management Science · doi:10.1287/mnsc.2014.1930 · Cited by 490
Industry Determinants of Organizational Culture
Gordon, G. (1991) · Academy of Management Review · doi:10.5465/amr.1991.4278959 · Cited by 471
Tapping the power of text mining
Fan, W., Wallace, L., Rich, S., & Zhang, Z. (2006) · Communications of the ACM · doi:10.1145/1151030.1151032 · Cited by 433
Instruments for Exploring Organizational Culture: A Review of the Literature
Jung, T., Scott, T., Davies, H. T. O., Bower, P., Whalley, D., McNally, R., & Mannion, R. (2009) · Public Administration Review · doi:10.1111/j.1540-6210.2009.02066.x · Cited by 320
Kelley, K., & Maxwell, S. E. (2003) · Psychological Methods · doi:10.1037/1082-989x.8.3.305 · Cited by 310
Text mining and ontologies in biomedicine: Making sense of raw text
Spasić, ., Ananiadou, S., McNaught, J., & Kumar, A. (2005) · Briefings in Bioinformatics · doi:10.1093/bib/6.3.239 · Cited by 299
Utilizing big data analytics for information systems research: challenges, promises and guidelines
Müller, O., Junglas, I. A., Brocke, J. V., & Debortoli, S. (2016) · European Journal of Information Systems · doi:10.1057/ejis.2016.2 · Cited by 282
Self-citationComplementary Use of Qualitative and Quantitative Cultural Assessment Methods
Yauch, C. A., & Steudel, H. J. (2003) · Organizational Research Methods · doi:10.1177/1094428103257362 · Cited by 280
The International Handbook of Organizational Culture and Climate
Cooper, C. L., Cartwright, S., Cartright, S., & Earley, P. C. (2001) · eCite Digital Repository (University of Tasmania) · Cited by 250
Tonidandel, S., King, E. B., & Cortina, J. M. (2016) · Organizational Research Methods · doi:10.1177/1094428116677299 · Cited by 221
Text mining for information systems researchers: An annotated topic modeling tutorial
University of Liechtenstein, Debortoli, S., Müller, O., IT University of Copenhagen, Junglas, I., Florida State University, vom Brocke, J., & University of Liechtenstein (2016) · Communications of the Association for Information Systems · doi:10.17705/1cais.03907 · Cited by 199 · cited 4×
“, clustering, principal components analysis, regression) (Debortoli et al., 2016).”
BackgroundInfluentialSelf-citationIndustry Mindsets: Exploring the Cultures of Two Macro-Organizational Settings
Phillips, M. E. (1994) · Organization Science · doi:10.1287/orsc.5.3.384 · Cited by 178
Industry Determinants of Organizational Culture
Gordon, G. G. (1991) · Academy of Management Review · doi:10.2307/258868 · Cited by 138
Müller, O., Schmiedel, T., Gorbacheva, E., & Brocke, J. V. (2014) · Enterprise Information Systems · doi:10.1080/17517575.2014.923514 · Cited by 96
Self-citationText Mining in Qualitative Research
Janasik, N., Honkela, T., & Bruun, H. (2008) · Organizational Research Methods · doi:10.1177/1094428108317202 · Cited by 93
Currall, S. C., Hammer, T. H., Baggett, L. S., & Doniger, G. M. (1999) · Organizational Research Methods · doi:10.1177/109442819921002 · Cited by 87
A computational model for mining consumer perceptions in social media
Pournarakis, D. E., Sotiropoulos, D. N., & Giaglis, G. M. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.09.018 · Cited by 84
Hardy, B., & Ford, L. R. (2014) · Organizational Research Methods · doi:10.1177/1094428113520185 · Cited by 69
LeBreton, J. M., Binning, J. F., Adorno, A. J., & Melcher, K. M. (2004) · Organizational Research Methods · doi:10.1177/1094428104266015 · Cited by 65
The Role of Gender in Business Process Management Competence Supply
Gorbacheva, E., Stein, A., Schmiedel, T., & Müller, O. (2016) · Business & Information Systems Engineering · doi:10.1007/s12599-016-0428-2 · Cited by 35
Self-citationOrganizational Culture: The Management Approach
Morey, N. C., & Morey, R. V. (1994) · NAPA Bulletin · doi:10.1525/napa.1994.14.1.17 · Cited by 6
Research works printed but not linked: 6
- Berente N., 2014, Paper presented at the 20th Americas Conference on Information Systems (AMCIS)
- Blei D., 2003, Journal of Machine Learning Research, 3, 993
- Frawley W., 1992, Al Magazine, 13, 57
- Jagarlamudi J., 2012, Paper presented at the 13th Conference of the European Chapter of the Association for Computational Linguistics
- Mimno D., 2011, Paper presented at the Conference on empirical methods in natural language processing
- Nguyen L., 2015, Paper presented at the Pacific-Asia Knowledge Discovery and Data Mining
Cited by
Sorted by citations
Marzi, G., Balzano, M., Caputo, A., & Pellegrini, M. M. (2024) · International Journal of Management Reviews · doi:10.1111/ijmr.12381 · Cited by 831
Goh, K. H., Wang, L., Yeow, A. Y. K., Poh, H., Li, K., Yeow, J. J. L., & Tan, G. Y. H. (2021) · Nature Communications · doi:10.1038/s41467-021-20910-4 · Cited by 415
Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations
Hickman, L., Thapa, S., Tay, L., Cao, M., & Srinivasan, P. (2020) · Organizational Research Methods · doi:10.1177/1094428120971683 · Cited by 445
Artificial intelligence and the conduct of literature reviews
Wagner, G., Lukyanenko, R., & Paré, G. (2021) · Journal of Information Technology · doi:10.1177/02683962211048201 · Cited by 364
Hacker, J. V., Brocke, J. V., Handali, J. P., Otto, M., & Schneider, J. (2020) · European Journal of Information Systems · doi:10.1080/0960085x.2020.1814680 · Cited by 315
Metadata
- 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
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