PLS Path Modeling represents a useful tool to model consumer's preferences. Nevertheless, the definition of a unique model for all the consumers may hide differences in their behaviour. Hence, models accounting for such heterogeneity afford more efficient strategies. Unobserved heterogeneity among statistical units requires to look for latent classes as well as to estimate local models. In the present work, a new method is proposed to overcome a few shortcomings of the already existing methods, such as the strong normality assumption on latent variables in the case of finite mixture models, or the lack of an optimizing criterion in the Typological approach. The goal will be achieved combining fuzzy approach to PLS Path Modeling and PLS-TPM. As a matter of fact, the fuzzy approach embeds residual information inside the model coefficients (expressed as intervals) thus permitting to search for latent classes by referring to the fuzziness of coefficients.
ESPOSITO VINZI, V., ROMANO, R. and TRINCHERA, L. (2007). Fuzzy PLS Path Modeling for Latent Class Analysis: Capturing Unobserved Heterogeneity in Consumers' Preference.
Keywords : #Customer-Satisfaction