Quality in the hotel service: proposal of experimental methodology

Abstract

Service quality is a variable that has an underlying purpose, which is customer satisfaction. Service quality indicators in the hotel sector have been analyzed in multiple investigations. The article aims to identify the most significant indicators of service quality, a disruptive quantitative experimental methodology uses machine learning, particularly K-means as an unsupervised learning technique to group the dataset that describes the quality in the service. The results of the statistical analysis show that the most important indicators were Assertiveness and Clarifications of human capital. The use of new methodologies contributes to analyze those indicators of quality in the hotel service and multidisciplinary information to promote scenarios to make managerial decisions in the hotel sector.