The relationship between attribute performance and customer satisfaction: An interpretable machine learning approach

Abstract

Understanding the relationship between attribute performance (AP) and customer satisfaction (CS) is crucial for the hospitality industry. However, accurately modeling this relationship remains challenging. To address this issue, we propose an interpretable machine learning-based dynamic asymmetric analysis (IML-DAA) approach that leverages interpretable machine learning (IML) to improve traditional relationship analysis methods. The IML-DAA employs extreme gradient boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to construct relationships and explain the significance of each attribute. Following this, an improved version of penalty-reward contrast analysis (PRCA) is used to classify attributes, whereas asymmetric impact-performance analysis (AIPA) is employed to determine the attribute improvement priority order. A total of 29,724 user ratings in New York City collected from TripAdvisor were investigated. The results suggest that IML-DAA can effectively capture non-linear relationships and that there is a dynamic asymmetric effect between AP and CS, as identified by the dynamic AIPA (DAIPA) model. This study enhances our understanding of the relationship between AP and CS and contributes to the literature on the hotel service industry.