European Business Schools Librarian's Group
ESSEC Working Papers,
ESSEC Research Center, ESSEC Business School
No DR 07019: Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments
Vincenzo Esposito Vinzi (), Christian M. Ringle, Silvia Squillacciotti () and Laura Trinchera
Additional contact information
Vincenzo Esposito Vinzi: ESSEC Business school, Postal: Avenue Bernard Hirsch - B.P. 50105, 95021 CERGY-PONTOISE Cedex , FRANCE, ,
Christian M. Ringle: University of Hamburg, Faculty of Business, Economics and social Sciences, Postal: Von-Melle-Park 5, 20146 HAMBURG, GERMANY
Silvia Squillacciotti: Electricité de France, Research & Development, Postal: 1 Avenue du Général de Gaulle, 92141 CLAMART, FRANCE
Laura Trinchera: University of Naples, Federico II, Department of Mathematics and Statistics, Postal: Via Cintia 27 , Complesso Monte Sant'Angelo, 80126 NAPLES, ITALY
Abstract: Segmentation in PLS path modeling framework results is a critical issue in social sciences. The assumption that data is collected from a single homogeneous population is often unrealistic. Sequential clustering techniques on the manifest variables level are ineffective to account for heterogeneity in path model estimates. Three PLS path model related statistical approaches have been developed as solutions for this problem. The purpose of this paper is to present a study on sets of simulated data with different characteristics that allows a primary assessment of these methodologies.
Keywords: Partial Least Squares; Path Modeling; Unobserved Heterogeneity
24 pages, July 2007
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