Neural networks to analyse the incidence of customer satisfaction in their loyalty in a tourist destination

Abstract

The relationship between levels of satisfaction experienced and its expression in future projected behaviour is widely addressed in the literature, however in the context of tourism in general and tourist destinations in particular acquires distinctive features that demand further research and deepening in this relationship. This research seeks to deepen this relationship, using techniques such as the analysis of neural networks since it allows identifying hidden relationships among multiple variables when working with large databases. The study was carried out in a tourist destination of sun and beach, allowing first verify the latent variables present in the construct of customer satisfaction and then, through the analysis of neural networks, corroborate that there is a high relationship between levels of satisfaction experienced by tourists and their willingness to return and recommend the destination or willingness to discredit the destination whenever dissatisfactions are generated, noting that this last relationship was more significant. Likewise, it is identified which attributes of the service have the most influence on each of these decisions, observing that in each of the previous decisions, the attributes influence differently.