The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
Latent class analysis in PLS-SEM: A review and recommendations for future applications
Abstract
Abstract With the increasing prominence of partial least squares structural equation modeling (PLS-SEM) in business research, the use of latent class analyses for identifying and treating unobserved heterogeneity has also gained momentum. Researchers have introduced various latent class approaches in a PLS-SEM context, of which finite mixture PLS (FIMIX-PLS) plays a central role due to its ability to identify heterogeneity and indicate a suitable number of segments to extract from the data. However, applying FIMIX-PLS requires researchers to make several choices that, if incorrect, could lead to wrong results and false conclusions. Addressing this concern, we present the results of a systematic review of FIMIX-PLS applications published in major business research journals. Our review provides an overview of the interdependencies between researchers’ choices and identifies potential problem areas. Based on our results, we offer concrete guidance on how to prevent common pitfalls when using FIMIX-PLS, and identify future research areas.
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Becker, J.-M., Cheah, J.-H., Gholamzade, R., Ringle, C. M., & Sarstedt, M. (2023) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-04-2022-0474 · Cited by 1,223
Self-citationImproving PLS-SEM Use for Business Marketing Research
Guenther, P., Guenther, M., Ringle, C. M., Zaefarian, G., & Cartwright, S. (2023) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.03.010 · Cited by 800
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Metadata
- Authors
- Sarstedt, M., Radomir, L., Moisescu, O. I., & Ringle, C. M.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 138
- Pages
- 398-407
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W3202481927
- Cited by
- 319
- References
- 94
- FWCI
- 14.11
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sarstedt, M., Radomir, L., Moisescu, O. I., & Ringle, C. M. (2022). Latent class analysis in PLS-SEM: A review and recommendations for future applications. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2021.08.051
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
