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Capturing Customer Heterogeneity Using A Finite Mixture Pls Approach


  • Carsten Hahn
  • Michael D. Johnson
  • Andreas Herrmann
  • Frank Huber


An approach for capturing unobserved customer heterogeneity in structural equation modeling is proposed based on partial least squares. The method uses a modified finite-mixture distribution approach. An empirical analysis using quality, customer satisfaction and loyalty data for convenience stores illustrates the advantages of the new method vis-à-vis a traditional market segmentation scheme based on well known grouping variables. The results confirm the assumption of heterogeneity in the individuals’ perception of the antecedents and consequences of satisfaction and their relationships. The results also illustrate how the finite-mixture approach complements and provides insights over and above a traditional segmentation scheme.

Suggested Citation

  • Carsten Hahn & Michael D. Johnson & Andreas Herrmann & Frank Huber, 2002. "Capturing Customer Heterogeneity Using A Finite Mixture Pls Approach," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 54(3), pages 243-269, July.
  • Handle: RePEc:sbr:abstra:v:54:y:2002:i:3:p:243-269

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    Cited by:

    1. Jan-Michael Becker & Christian Ringle & Marko Sarstedt & Franziska Völckner, 2015. "How collinearity affects mixture regression results," Marketing Letters, Springer, vol. 26(4), pages 643-659, December.
    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. Eurico, Sofia & Valle, Patrícia & Silva, João Albino & Marques, Catarina, 2012. "Segmenting Graduate Consumers of Higher Education in Tourism: An Extension of the ECSI Model," Spatial and Organizational Dynamics Discussion Papers 2012-7, CIEO-Research Centre for Spatial and Organizational Dynamics, University of Algarve.
    4. Alexander Himme, 2012. "Critical success factors of strategic cost reduction," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 23(3), pages 183-210, December.
    5. Tom Frans Wilderjans & Eva Gaer & Henk A. L. Kiers & Iven Mechelen & Eva Ceulemans, 2017. "Principal Covariates Clusterwise Regression (PCCR): Accounting for Multicollinearity and Population Heterogeneity in Hierarchically Organized Data," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 86-111, March.
    6. Ringle, Christian M., 2006. "Segmentation for path models and unobserved heterogeneity: The finite mixture partial least squares approach," MPRA Paper 10734, University Library of Munich, Germany.
    7. 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.
    8. Marques, Catarina & Reis, Elizabeth, 2015. "How to deal with heterogeneity among tourism constructs?," Annals of Tourism Research, Elsevier, vol. 52(C), pages 172-174.
    9. 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.
    10. 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.
    11. 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.
    12. Luca Zanin, 2013. "Detecting Unobserved Heterogeneity in the Relationship Between Subjective Well-Being and Satisfaction in Various Domains of Life Using the REBUS-PLS Path Modelling Approach: A Case Study," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(1), pages 281-304, January.
    13. Schubring, Sandra & Lorscheid, Iris & Meyer, Matthias & Ringle, Christian M., 2016. "The PLS agent: Predictive modeling with PLS-SEM and agent-based simulation," Journal of Business Research, Elsevier, vol. 69(10), pages 4604-4612.
    14. 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.
    15. Dominic, Theresia & Theuvsen, Ludwig, 2015. "Agribusiness Firm Resources and Performance: The Mediating Role of Strategic Management Practices," Discussion Papers 200324, Georg-August-Universitaet Goettingen, GlobalFood, Department of Agricultural Economics and Rural Development.
    16. 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.

    More about this item

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General


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