Espacio ocupado en el lineal por las marcas de distribuidor: estimación mediante redes neuronales vs regresión multiple

This paper analyses the influence of some variables in the shelf space occupied by store brands. We propose and test a theoretical model of store brand shelf space. Data were collected for 29 product categories in 55 retail stores. A two-phase procedure was adopted: (1) multiple regression analyses; (2) neural network simulation (ANN). The application of this last method improves the goodness of fit obtained through the regression method. Furthermore, it presents additional advantages since ANN does not need to fulfil the main assumptions needed in regression analyses. The findings corroborate our proposed model, in that all hypothesized relationships and directions are supported. On this basis, we draw theoretical as well as useful managerial implications for both retailers and manufacturers.