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Uncovering and Treating Unobserved Heterogeneity with FIMIX-PLS: Which Model Selection Criterion Provides an Appropriate Number of Segments?


  • Marko Sarstedt
  • Jan-Michael Becker
  • Christian M. Ringle
  • Manfred Schwaiger


Since 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.

Suggested Citation

  • Marko Sarstedt & Jan-Michael Becker & Christian M. Ringle & Manfred Schwaiger, 2011. "Uncovering and Treating Unobserved Heterogeneity with FIMIX-PLS: Which Model Selection Criterion Provides an Appropriate Number of Segments?," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 63(1), pages 34-62, January.
  • Handle: RePEc:sbr:abstra:v:63:y:2011:i:1:p:34-62

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    Cited by:

    1. 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.
    2. Ratzmann, Martin & Gudergan, Siegfried P. & Bouncken, Ricarda, 2016. "Capturing heterogeneity and PLS-SEM prediction ability: Alliance governance and innovation," Journal of Business Research, Elsevier, vol. 69(10), pages 4593-4603.
    3. Schlittgen, Rainer & Ringle, Christian M. & Sarstedt, Marko & Becker, Jan-Michael, 2016. "Segmentation of PLS path models by iterative reweighted regressions," Journal of Business Research, Elsevier, vol. 69(10), pages 4583-4592.
    4. repec:spr:jmgtco:v:28:y:2017:i:2:d:10.1007_s00187-017-0249-6 is not listed on IDEAS
    5. repec:spr:soinre:v:135:y:2018:i:2:d:10.1007_s11205-016-1516-x is not listed on IDEAS
    6. Kathrin Dudenhöffer, 2013. "Why electric vehicles failed," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 24(2), pages 95-124, July.
    7. 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.
    8. 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.
    9. Fiedler, Marina & Sarstedt, Marko, 2014. "Influence of community design on user behaviors in online communities," Journal of Business Research, Elsevier, vol. 67(11), pages 2258-2268.
    10. Segarra-Moliner, Jose Ramón & Moliner-Tena, Miguel Ángel, 2016. "Customer equity and CLV in Spanish telecommunication services," Journal of Business Research, Elsevier, vol. 69(10), pages 4694-4705.

    More about this item


    FIMIX-PLS; Finite Mixture Modeling; Model Selection; Partial Least Squares (PLS); Segmentation; Structural Equation Modeling;

    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing


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