Modeling by explainable artificial intelligence: Evaluation of accommodation facilities and the impact of sustainability on customer satisfaction.

The hospitality sector represents an important opportunity for the development of niche tourism, offering authentic and sustainable experiences. In this study, we apply the K-means algorithm to segment accommodation facilities based on common characteristics to improve customer satisfaction, considering variables such as sustainability, comfort, cleanliness, value for money, and services. The analysis of regression models and the use of the SHAP method allowed us to identify the importance of features that influence the overall scores of accommodation facilities, revealing that sustainability has a limited impact on customer satisfaction. The approach followed falls within the scope of Explainable Artificial Intelligence (XAI) and its functional modelling for the analysed case. This information can help optimize business strategies and improve overall satisfaction. However, the analysis revealed that sustainability is seen as a secondary factor in the decisions of accommodation facilities, suggesting the need for strategies that promote sustainable practices to raise awareness of these aspects among visitors. In this study, we present a case study related to the Lower Aosta Valley, a territory in north-western Italy characterized by significant tourism accommodation