Interpretable machine learning for customer satisfaction optimization
Xin Li, Qi Shao, Gang Li, Rob LawPurpose
Understanding how dining experiences are transformed into customer satisfaction judgments is central to hospitality competitiveness. Using high-dimensional user-generated content on tourism dining experiences, this study aims to examine whether shared three-factor classifications coexist with segment-specific differences in satisfaction relationships.
Design/methodology/approach
The authors analyze 82,105 Yelp restaurant reviews from nine market segments based on price level, cuisine type and restaurant category. Bidirectional encoder representations from transformers-based aspect mining and clustering extract attribute–sentiment pairs. Ensemble models estimate nonlinear predictive relationships, and SHAP decomposes their predictions to support attribute classification, segment-specific satisfaction functions and diagnostic prioritization.
Findings
Empirical results reveal a basic-dominant pattern together with segment-level variation in estimated penalty–reward magnitudes, satisfaction-function shapes and diagnostic priorities. This pattern remains largely stable under alternative impact asymmetry thresholds and the procedure based on penalty-reward contrast analysis. Categorical stability can therefore coexist with segment-contingent differences in evaluative weight and relationship form.
Practical implications
Theoretically, the findings highlight the value of extending attribute classification with segment-specific satisfaction-function analysis, because segment heterogeneity is expressed within, and not only between, three-factor categories. Practically, managers can use the framework to diagnose service attributes with higher marginal predictive utility and develop more targeted, segment-specific improvement priorities.
Originality/value
This study refines the categorical interpretation of three-factor theory by showing that a shared attribute classification does not imply an invariant satisfaction relationship. Segment heterogeneity can occur within a shared category through differences in penalty–reward magnitude and satisfaction-function shape, not only through category shifts.