The DOI matches its Crossref record.
REBUS‐PLS: A response‐based procedure for detecting unit segments in PLS path modelling
Abstract
Structural equation models (SEMs) make it possible to estimate the causal relationships, defined according to a theoretical model, linking two or more latent complex concepts, each measured through a number of observable indicators, usually called manifest variables. Traditionally, the component‐based estimation of SEMs by means of partial least squares (PLS path modelling, PLS‐PM) assumes homogeneity over the observed set of units: all units are supposed to be well represented by a unique model estimated on the overall data set. In many cases, however, it is reasonable to expect classes made of units showing heterogeneous behaviours to exist. Two different kinds of heterogeneity could be affecting the data: observed and unobserved heterogeneity. The first refers to the case of a priori existing classes, whereas in unobserved heterogeneity no information is available either on the number of classes or on their composition. If a group structure for the statistical units is given, the aim of the analysis is to search for any differences in the behaviours of the a priori given classes. In PLS‐PM this would mean studying the effect of directly observed moderating variables, i.e. estimating as many (local) models as there are classes. Unobserved heterogeneity, instead, implies identifying classes of units (a priori unknown) having similar behaviours. Such heterogeneity is captured by an unobserved (latent) discrete moderating variable defining both the number of classes and the class membership. A new method for unobserved heterogeneity detection in PLS‐PM is proposed in this paper: response‐based procedure for detecting unit segments in PLS‐PM (REBUS‐PLS). REBUS‐PLS, according to PLS‐PM features, does not require distributional hypotheses and may lead to local models that are different in terms of both structural and measurement models. An application of REBUS‐PLS on real data will be shown. Copyright © 2008 John Wiley & Sons, Ltd.
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
Cited by
Pls-Sem: indeed a silver bullet
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679190202 · Cited by 21,631
Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research
F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014) · European Business Review · doi:10.1108/ebr-10-2013-0128 · Cited by 11,728
Testing measurement invariance of composites using partial least squares
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016) · International Marketing Review · doi:10.1108/imr-09-2014-0304 · Cited by 2,964
Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014) · Journal of Family Business Strategy · doi:10.1016/j.jfbs.2014.01.002 · Cited by 2,212
Sarstedt, M., Henseler, J., & Ringle, C. M. (2011) · Advances in International Marketing · doi:10.1108/s1474-7979(2011)0000022012 · Cited by 1,326
Metadata
- Authors
- Esposito Vinzi, V., Trinchera, L., Squillacciotti, S., & Tenenhaus, M.
- Venue
- Applied Stochastic Models in Business and Industry
- Publisher
- Wiley
- Published
- 2008-08-28
- Volume
- 24
- Issue
- 5
- Pages
- 439-458
- ISSN
- 1524-1904 (ISSN-L)
- 1526-4025
- OpenAlex
- W4236684332
- Cited by
- 179
- References
- 0
- FWCI
- 8.71
- Type
- journal article
- Language
- en
- Open access
- closed
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: REBUS‐PLS: A response‐based procedure for detecting unit segments in PLS path modelling. https://app.cochat.ai/papers/10.1002/asmb.728
- The paper
- Esposito Vinzi, V., Trinchera, L., Squillacciotti, S., & Tenenhaus, M. (2008). REBUS‐PLS: A response‐based procedure for detecting unit segments in PLS path modelling. Applied Stochastic Models in Business and Industry. https://doi.org/10.1002/asmb.728
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.
