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Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments

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Segmentation in PLS path modeling framework results is a critical issue in social sciences. The assumption that data is collected from a single homogeneous population is often unrealistic. Sequential clustering techniques on the manifest variables level are ineffective to account for heterogeneity in path model estimates. Three PLS path model related statistical approaches have been developed as solutions for this problem. The purpose of this paper is to present a study on sets of simulated data with different characteristics that allows a primary assessment of these methodologies.

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  • Esposito Vinzi, Vincenzo & Ringle, Christian M. & Squillacciotti, Silvia & Trinchera, Laura, 2007. "Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments," ESSEC Working Papers DR 07019, ESSEC Research Center, ESSEC Business School.
    • Handle: RePEc:ebg:essewp:dr-07019
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    1. Josef A. Mazanec & Amata Ring, 2011. "Tourism Destination Competitiveness: Second Thoughts on the World Economic Forum Reports," Tourism Economics, , vol. 17(4), pages 725-751, August.
    2. 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.
    3. Stéphanie Bougeard & Hervé Abdi & Gilbert Saporta & Ndèye Niang, 2018. "Clusterwise analysis for multiblock component methods," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 285-313, June.
    4. 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.

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    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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