Probing the intrinsic mechanism and evolution characteristics of online shopping customer satisfaction via text mining of online reviews

Abstract

Prior research has tended to disregard the dynamic nature of customer satisfaction in online shopping and how it influences corporate marketing decisions. This study originally introduces a dynamic online shopping customer satisfaction index model and devises a new text mining algorithm to quantify online reviews, testing and analyzing the model to reveal the intrinsic mechanism and evolutionary characteristics of online shopping customer satisfaction. Findings reveal disparities between the online shopping customer satisfaction index model and the American customer satisfaction index model. Specifically, customer expectations significantly impact customer loyalty, while customer loyalty influences complaint rates. The study also highlights the impact of COVID-19, which has intensified competition and underscored the importance of perceived quality and brand image. Our findings provides a reference for e-commerce enterprises to realize data-driven marketing decisions.