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Finite Mixture and Genetic Algorithm Segmentation in Partial Least Squares Path Modeling: Identification of Multiple Segments in Complex Path Models

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Abstract

When applying structural equation modeling methods, such as partial least squares (PLS) path modeling, in empirical studies, the assumption that the data have been collected from a single homogeneous population is often unrealistic. Unobserved heterogeneity in the PLS estimates on the aggregate data level may result in misleading interpretations. Finite mixture partial least squares (FIMIX-PLS) and PLS genetic algorithm segmentation (PLS-GAS) allow the classification of data in variance-based structural equation modeling. This research presents an initial application and comparison of these two methods in a computational experiment in respect of a path model which includes multiple endogenous latent variables. The results of this analysis reveal particular advantages and disadvantages of the approaches. This study further substantiates the effectiveness of FIMIX-PLS and PLS-GAS and provides researchers and practitioners with additional information they need to proficiently evaluate their PLS path modeling results by applying a systematic means of analysis. If significant heterogeneity were to be uncovered by the procedures, the analysis may result in group-specific path modeling outcomes, thus allowing further differentiated and more precise conclusions to be formed.

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Author information

Authors and Affiliations

  1. Institute of Industrial Management, University of Hamburg, Von-Melle-Park 5, 20146, Hamburg, Germany

    Christian M. Ringle

  2. Centre for Management and Organisation Studies (CMOS), University of Technology Sydney (UTS), 1-59 Quay Street, Haymarket, NSW, 2001, Australia

    Christian M. Ringle

Authors
  1. Christian M. Ringle
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  2. Marko Sarstedt
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  3. Rainer Schlittgen
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Corresponding author

Correspondence to Christian M. Ringle .

Editor information

Editors and Affiliations

  1. Universität der Bundeswehr, Fak. Wirtschafts-/Sozialwissenschaften, Helmut-Schmidt-Universität, Holstenhofweg 85, Hamburg, 22043, Germany

    Andreas Fink

  2. Dept. Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, United Kingdom

    Berthold Lausen

  3. Universität der Bundeswehr, Fak. Wirtschafts-/Sozialwissenschaften, Helmut-Schmidt-Universität, Holstenhofweg 85, Hamburg, 22043, Germany

    Wilfried Seidel

  4. FB 12 Mathematik und Informatik, Datenbionik AG, Universität Marburg, Hans-Meerwein-Straße, Marburg, 35032, Germany

    Alfred Ultsch

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© 2009 Springer-Verlag Berlin Heidelberg

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Ringle, C.M., Sarstedt, M., Schlittgen, R. (2009). Finite Mixture and Genetic Algorithm Segmentation in Partial Least Squares Path Modeling: Identification of Multiple Segments in Complex Path Models. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_15

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