The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
PLS-SEM STATISTICAL PROGRAMS: A REVIEW
Open accessAbstract
Partial least squares structural equation modeling (PLS-SEM) is one of the most widely used methods of multivariate data analysis. Although previous research has discussed different aspects of PLS-SEM, little has been done to explain the attributes of the various PLS-SEM statistical applications. The objective of this editorial is to discuss the multiple PLS-SEM applications, including SmartPLS, WarpPLS, and ADANCO. It is written based on information received from the developers via emails as well as our ongoing understanding and experience of using these applications. We hope this editorial will serve as a manual for users to understand the unique characteristics of each PLS-SEM application and make informed decisions on the most appropriate application for their research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(2013) · Cited by 44,563
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
When to Use and How to Report the Results of PLS-SEM
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) · European Business Review · doi:10.1108/ebr-11-2018-0203 · Cited by 27,541
A primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921
Using pls path modeling in new technology research: updated guidelines
Henseler, J., Hubona, G., & Ray, P. A. (2016) · Industrial Management & Data Systems · doi:10.1108/imds-09-2015-0382 · Cited by 7,173
Assessing measurement model quality in PLS-SEM using confirmatory composite analysis
Hair, J. F., Howard, M. C., & Nitzl, C. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2019.11.069 · Cited by 5,446
Wetzels, M., Odekerken-Schröder, G., & van Oppen, C. (2009) · MIS Quarterly · doi:10.2307/20650284 · Cited by 4,944
Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming, Third Edition
Byrne, B. M. (2016) · doi:10.4324/9781315757421 · Cited by 4,653
Predictive model assessment in PLS-SEM: guidelines for using PLSpredict
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019) · European Journal of Marketing · doi:10.1108/ejm-02-2019-0189 · Cited by 4,618
Self-citation(2018) · Wiley Series in Probability and Statistics · doi:10.1002/9781119409137.ch4 · Cited by 3,515
Cited by
van Bussel, M. J. P., Odekerken–Schröder, G. J., Ou, C., Swart, R. R., & Jacobs, M. J. G. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08189-7 · Cited by 60
Salunkhe, H. A., Jain, D., Hinge, P., & Boralkar, M. (2024) · SA Journal of Human Resource Management · doi:10.4102/sajhrm.v22i0.2723 · Cited by 29
Antasari, I. W. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2024.101255 · Cited by 4
Metadata
- Authors
- Memon, M. A., T., R., Cheah, J.-H., Ting, H., Chuah, F., & Cham, T. H.
- Venue
- Journal of Applied Structural Equation Modeling
- Publisher
- Sarawak Research Society
- Published
- 2021-03-10
- Volume
- 5
- Issue
- 1
- Pages
- i-xiv
- ISSN
- 2590-4221 (ISSN-L)
- OpenAlex
- W3153200187
- Cited by
- 422
- References
- 60
- Type
- journal article
- Language
- en
- Open access
- diamond
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: PLS-SEM STATISTICAL PROGRAMS: A REVIEW. https://app.cochat.ai/papers/10.47263/jasem.5(1)06
- The paper
- Memon, M. A., T., R., Cheah, J.-H., Ting, H., Chuah, F., & Cham, T. H. (2021). PLS-SEM STATISTICAL PROGRAMS: A REVIEW. Journal of Applied Structural Equation Modeling. https://doi.org/10.47263/jasem.5(1)06
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
