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Response-Based Segmentation Using Finite Mixture Partial Least Squares

Theoretical Foundations and an Application to American Customer Satisfaction Index Data

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Book cover Data Mining

Part of the book series: Annals of Information Systems ((AOIS,volume 8))

Abstract

When applying multivariate analysis techniques in information systems and social science disciplines, such as management information systems (MIS) and marketing, the assumption that the empirical data originate from a single homogeneous population is often unrealistic. When applying a causal modeling approach, such as partial least squares (PLS) path modeling, segmentation is a key issue in coping with the problem of heterogeneity in estimated cause-and-effect relationships. This chapter presents a new PLS path modeling approach which classifies units on the basis of the heterogeneity of the estimates in the inner model. If unobserved heterogeneity significantly affects the estimated path model relationships on the aggregate data level, the methodology will allow homogenous groups of observations to be created that exhibit distinctive path model estimates. The approach will, thus, provide differentiated analytical outcomes that permit more precise interpretations of each segment formed. An application on a large data set in an example of the American customer satisfaction index (ACSI) substantiates the methodology’s effectiveness in evaluating PLS path modeling results.

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Authors and Affiliations

  1. Institute for Industrial Management and Organizations, 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

  3. Institute for Market-based Management, University of Munich, Kaulbachstr. 45, 80539, Munich, Germany

    Marko Sarstedt

  4. Aston Business School, Aston University, Room NB233 Aston Triangle, Birmingham, B47ET, UK

    Erik A. Mooi

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  1. Christian M. Ringle
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  2. Marko Sarstedt
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  3. Erik A. Mooi
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Correspondence to Christian M. Ringle , Marko Sarstedt or Erik A. Mooi .

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Editors and Affiliations

  1. Inst. Wirtschaftsinformatik, Universität Hamburg, Von-Melle-Park 5, Hamburg, 20146, Germany

    Robert Stahlbock

  2. Management School, Dept. Management Science, Lancaster University, Lancaster, LA1 4YX, United Kingdom

    Sven F. Crone

  3. Inst. Wirtschaftsinformatik, Universität Hamburg, Von-Melle-Park 5, Hamburg, 20146, Germany

    Stefan Lessmann

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Ringle, C.M., Sarstedt, M., Mooi, E.A. (2010). Response-Based Segmentation Using Finite Mixture Partial Least Squares. In: Stahlbock, R., Crone, S., Lessmann, S. (eds) Data Mining. Annals of Information Systems, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1280-0_2

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