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Response-based Segmentation Using Finite Mixture Partial Least Squares: Theoretical Foundations and an Application to American Customer Satisfaction Index Data
Publikationstyp
Book Part
Date Issued
2010
Sprache
English
Author(s)
Herausgeber*innen
TORE-URI
Start Page
19
End Page
49
Citation
Annals of Information Systems: 19-49
Publisher DOI
Publisher
Springer
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.
Subjects
Partial Little Square
Customer Satisfaction
Unobserved Heterogeneity
Finite Mixture
Partial Little Square Path Modeling
DDC Class
330: Wirtschaft