Complex models have been traditionally and increasingly used by both marketing academics and practitioners to represent and understand consumer behaviour. Thus, we firstly pose that models of consumer behaviour firms use to make their decisions must be close to what a real Marketing Management Support System should offer, in order to be of use.
In this sense, rather than focusing on analysing the quality of the consumer-related marketing phenomena represented by such models - i.e.: those theoretical issues which support their validity -, we take an in-depth look at the utility of the statistical techniques used to estimate those models theoretically proposed.
Thus, we pose that the use of fuzzy systems as a knowledge discovery tool is of great interest for improving the interpretation and understanding of such consumer models. Moreover, we put forward a new application for consumer behaviour modelling, based on fuzzy association rules (FAS) for adjusting the data, as a complementary alternative to the results obtained by using the classic technique of model estimation based on Structural Equation Modelling (SEM).
With this aim, a behavioural model centred on explaining consumer attitude towards Internet and trust in Internet shopping is presented, being later tested by making use of both FAS and SEM. Finally, a comparative analysis of the results is done, focusing particularly on our proposal of methodological application.