Uncovering and Treating Unobserved Heterogeneity with FIMIX-PLS: Which Model Selection Criterion Provides an Appropriate Number of Segments?
AbstractSince its first introduction in the Schmalenbach Business Review, Hahn et al.’s (2002) finite mixture partial least squares (FIMIX-PLS) approach to response-based segmentation in variance-based structural equation modeling has received much attention from the marketing and management disciplines. When applying FIMIX-PLS to uncover unobserved heterogeneity, the actual number of segments is usually unknown. As in any clustering procedure, retaining a suitable number of segments is crucial, since many managerial decisions are based on this result. In empirical research, applications of FIMIX-PLS rely on information and classification criteria to select an appropriate number of segments to retain from the data. However, the performance and robustness of these criteria in determining an adequate number of segments has not yet been investigated scientifically in the context of FIMIX-PLS. By conducting computational experiments, this study provides an evaluation of several model selection criteria’s performance and of different data characteristics’ influence on the robustness of the criteria. The results engender key recommendations and identify appropriate model selection criteria for FIMIX-PLS. The study’s findings enhance the applicability of FIMIX-PLS in both theory and practice.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by LMU Munich School of Management in its journal Schmalenbach Business Review.
Volume (Year): 63 (2011)
Issue (Month): 1 (January)
FIMIX-PLS; Finite Mixture Modeling; Model Selection; Partial Least Squares (PLS); Segmentation; Structural Equation Modeling;
Find related papers by JEL classification:
- C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
- M31 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Marketing
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Ringle, Christian M. & Sarstedt, Marko & Schlittgen, Rainer & Taylor, Charles R., 2013. "PLS path modeling and evolutionary segmentation," Journal of Business Research, Elsevier, vol. 66(9), pages 1318-1324.
- Sarstedt, Marko & Wilczynski, Petra & Melewar, T.C., 2013. "Measuring reputation in global markets—A comparison of reputation measures’ convergent and criterion validities," Journal of World Business, Elsevier, vol. 48(3), pages 329-339.
- Sarstedt, Marko & Ringle, Christian M. & Smith, Donna & Reams, Russell & Hair, Joseph F., 2014. "Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers," Journal of Family Business Strategy, Elsevier, vol. 5(1), pages 105-115.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (sbr) The email address of this maintainer does not seem to be valid anymore. Please ask sbr to update the entry or send us the correct address.
If references are entirely missing, you can add them using this form.