Determining directions of service quality management using online review mining with interpretable machine learning
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Elservier
Abstract
Determining the importance values of service features is necessary to prioritize the points in service quality
management and improvement. Existing studies have used linearly additive relationship models to estimate
service feature importance, such as linear and logistic regression. This traditional approach is interpretable but
often limited in terms of model fitness and prediction performance. Meanwhile, modern advanced machine
learning models provide high fitness and performance but often lack interpretability. Thus, to achieve both
reliable prediction and interpretation, we propose a systematic framework for estimating the importance of
service features using online review mining with interpretable machine learning. An interpretable machine
learning-based method is proposed to estimate the importance values of features by applying the shapley ad
ditive global importance metric to the highest-performance prediction model. We validate the superiority of our
framework over existing methods through a case study on the global importance estimation of hotel service
features in Singapore. To facilitate additional applications, we offer the implementation code of our work at http
s://github.com/JK-SHIN-PG/OnReviewServImprovement.
Description
International Journal of Hospitality Management 118 (2024) 103684
Keywords
Service management,Feature importance,Interpretable machine learning,Explainable artificial intelligence,Customer reviews,Customer needs