The neural network is a very useful tool for approximation of a function, time series prediction, classification, and pattern recognition. If there is found to be a non-linear relationship between input data and output data, it is difficult to analyse the system. A neural network is very effective to solve this problem. This chapter studies the applied neural network model in relation to clearance sales outshopping behaviour. Since neural network theory can be applied effectively to this case, the authors have used neural network theory to recognise the retail area satisfaction and loyalty. To measure the impact among the retail area attributes, retail area satisfaction, and retail area loyalty, the authors have used the neural network model. In this chapter, they have treated twenty seven factors as the input signals into the input layer. Therefore, they find the weights between nodes in the relationship between the value of all twenty seven factors and the retail area satisfaction and loyalty. The development of the model by retail area attributes, and their interpretation, was facilitated by a collection of data across three trading areas. This neural network modeling approach to understand clearance sales outshopping behaviour provides retail managers with information to support retail strategy development.
An Expansion of the Neural Network Theory by Introducing Hebb Postulate
