Modeling factors influencing the confusion of female customers in choosing retail stores

Today, the shopping center is no longer a place for transaction purposes where customers go to if they need a product or service, but it is a place for social purposes. Shoppers may become confused if they perceive a social environment as too stimulating or inappropriate. The aim of the current research is to combine interpretive structural modeling and machine learning to stratify factors affecting customer confusion in choosing a retail store. In order to select the features that are most related to the target variables and can provide the best performance in predicting and interpreting the model, feature selection using a neural network has been used. The proposed approach initially reduces the computational complexity in solving the design problem by reducing the dimensions of the problem space through the training of a type of multilayer neural networks. 7 factors affecting customer confusion were extracted based on this neural network model, classified in 5 levels using interpretative structural modeling, and the related model was drawn. In this category, the variables of a wide variety of store brands, loyalty programs and the concentration of stores in one location, the greatest power of influence (influence) and perceived risk, individual and demographic factors and the volume of information show the most dependence (influence) and the variable of the commodity factor is the only It is a variable that has a two-way relationship with other variables.