A modified PLS path modeling algorithm handling reflective categorical variables and a new model building strategy

Authors:
Emmanuel Jakobowicz;Christian Derquenne
Affiliations:
EDF R&D, 1 Avenue du Géénéral de Gaulle, 92121 Clamart Cedex, France and CEDRIC-CNAM, 292 rue Saint Martin, 75141 Paris Cedex 03, France;EDF R&D, 1 Avenue du Géénéral de Gaulle, 92121 Clamart Cedex, France
Venue:
Computational Statistics & Data Analysis
Year:
2007

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Abstract

Partial least squares (PLS) path modeling has found increased applications in customer satisfaction analysis thanks to its ability to handle complex models. A modified PLS path modeling algorithm together with a model building strategy are introduced and applied to customer satisfaction analysis at the French energy supplier Electricite de France. The modified PLS algorithm handles all kinds of scales (categorical or nominal variables) and is well suited when nominal or binary variables are involved. PLS path modeling and structural equation modeling are confirmatory approaches and thus need an initial conceptual model. A two-step model building strategy is presented; the first step is based on Bayesian networks structure learning to build the measurement model and the second step is based on partial correlation and hypothesis tests to build the structural model. Applications to customer satisfaction data are presented.