Response-Based Segmentation in PLS Path Modeling: Application Of Fimix-Pls to American Customer Satisfaction Index Data

Edward E. Rigdon*, Siegfried P. Gudergan, Christian M. Ringle, Marko Sarstedt

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

The role of customer satisfaction indices is important in understanding business success and is evidenced through established effects on customer retention and profitability. Today, Fornell et al.’s (1996) American Customer Satisfaction Index (ACSI) ranks among the most salient models in marketing. Several studies have provided strong evidence for the measure’s reliability and validity and provide a basis for comparing the effects of antecedent constructs on overall satisfaction and loyalty. However, by using aggregated data for their validation analyses, these studies assume that the data stem from a single homogenous population—a single model represents all observations. This assumption of homogeneity is unrealistic as customers are likely to differ in their perceptions and evaluations of firms’ characteristics, as well as in their familiarity with a given firm’s offerings (Rigdon et al. 2010). Moreover, observable characteristics are often inadequate in capturing the apparent heterogeneity in the data (Sarstedt and Ringle 2010). Even though prior research has found substantial unobserved customer heterogeneity within a given product or service class (Wu and DeSarbo 2005), it usually remains unaddressed which leads to biased parameter estimates and, consequently, flawed conclusions.

Original languageEnglish
Title of host publicationDevelopments in Marketing Science
Subtitle of host publicationProceedings of the Academy of Marketing Science
PublisherSpringer
Pages145
Number of pages1
DOIs
Publication statusPublished - 2015
MoE publication typeA3 Book section, Chapters in research books

Publication series

NameDevelopments in Marketing Science: Proceedings of the Academy of Marketing Science
ISSN (Print)2363-6165
ISSN (Electronic)2363-6173

Keywords

  • Customer Retention
  • Finite Mixture
  • Loyal Customer
  • Salient Model
  • Service Class

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