PLS-MGA: A Non-Parametric Approach to Partial Least Squares-based Multi-Group Analysis

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68 Citations (Scopus)

Abstract

This paper adds to an often applied extension of Partial Least Squares (PLS) path modeling, namely the comparison of PLS estimates across subpopulations, also known as multi-group analysis. Existing PLS-based approaches to multi-group analysis have the shortcoming that they rely on distributional assumptions. This paper develops a non-parametric PLS-based approach to multi-group analysis: PLS-MGA. Both the existing approaches and the new approach are applied to a marketing example of customer switching behavior in a liberalized electricity market. This example provides first evidence of favorable operation characteristics of PLS-MGA.
Original languageEnglish
Title of host publicationChallenges at the Interface of Data Analysis, Computer Science, and Optimization
Subtitle of host publicationProceedings of the 34th Annual Conference of the Gesellschaft für Klassifikation e. V., Karlsruhe, July 21 - 23, 2010
EditorsWolfgang A. Gaul, Andreas Geyer-Schulz, Lars Schmidt-Thieme, Jonas Kunze
Place of PublicationBerlin, Heidelberg
PublisherSpringer
Pages495-501
ISBN (Electronic)978-3-642-24466-7
ISBN (Print)9783642244650
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event34th Annual Conference of the Gesellschaft für Klassifikation e.V. 2010 - Karlsruhe, Germany
Duration: 21 Jul 201423 Jul 2014
Conference number: 34

Publication series

NameStudies in Classification, Data Analysis, and Knowledge Organization
PublisherSpringer

Conference

Conference34th Annual Conference of the Gesellschaft für Klassifikation e.V. 2010
CountryGermany
CityKarlsruhe
Period21/07/1423/07/14

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Keywords

  • Partial least squares (PLS)
  • Customer satisfaction
  • Switching cost
  • Bootstrap estimate
  • Partial least squares path modeling

Cite this

Henseler, J. (2012). PLS-MGA: A Non-Parametric Approach to Partial Least Squares-based Multi-Group Analysis. In W. A. Gaul, A. Geyer-Schulz, L. Schmidt-Thieme, & J. Kunze (Eds.), Challenges at the Interface of Data Analysis, Computer Science, and Optimization: Proceedings of the 34th Annual Conference of the Gesellschaft für Klassifikation e. V., Karlsruhe, July 21 - 23, 2010 (pp. 495-501). (Studies in Classification, Data Analysis, and Knowledge Organization). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-24466-7_50