Response-based segmentation using Finite Mixture Partial Least Squares: theoretical foundations and an application to American Customer Satisfaction Index Data

Christian M. Ringle, Marko Sarstedt, Erik A. Mooi

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish
Title of host publicationData mining: special issue in annals of information systems
EditorsRobert Stahlbock, Sven F. Crone, Stefan Lessmann
Place of PublicationLondon (UK)
PublisherSpringer
Pages19-49
Number of pages31
Volume8
ISBN (Print)978-1-44191279-4
DOIs
Publication statusPublished - 2010

Publication series

NameAnnals of Information Systems
PublisherSpringer

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Keywords

  • multivariate analysis techniques
  • information systems
  • social science disciplines
  • management information systems
  • MIS
  • marketing
  • empirical data
  • causal modeling approach
  • partial least squares
  • PLS
  • path modeling
  • segmentation
  • heterogeneity

Cite this

Ringle, C. M., Sarstedt, M., & Mooi, E. A. (2010). Response-based segmentation using Finite Mixture Partial Least Squares: theoretical foundations and an application to American Customer Satisfaction Index Data. In R. Stahlbock, S. F. Crone, & S. Lessmann (Eds.), Data mining: special issue in annals of information systems (Vol. 8, pp. 19-49). (Annals of Information Systems). Springer. https://doi.org/10.1007/978-1-4419-1280-0_2