FUZZY ASSOCIATION RULES FOR ESTIMATING CONSUMER BEHAVIOUR MODELS AND THEIR ITS APPLICATION TO EXPLAINING TRUST IN INTERNET SHOPPING

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 in which firms are based to take their decisions must be close to what a real Marketing Management Support System should offer, in order to be of use of being usefulness. 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 deeply reflect about 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 it is proposed 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 specially on our proposal of methodological application.