Modeling customer satisfaction and revisit intention from online restaurant reviews: an attribute-level analysis
Industrial Management & Data Systems
ISSN: 0263-5577
Article publication date: 28 March 2023
Issue publication date: 27 April 2023
Abstract
Purpose
The purpose of this paper is to detect predefined service attributes and their sentiments from online restaurant reviews, and then to measure the effects of customer sentiments toward service attributes on customer satisfaction (CS) and revisit intention (RVI) simultaneously.
Design/methodology/approach
This study proposed a supervised framework to model CS and RVI simultaneously from restaurant reviews. Specifically, the authors detected the predefined service dimensions from online reviews based on random forest. Then, the sentiment polarities of the reviews toward each predefined dimension were identified using light-gradient boosting machine (LightGBM). Finally, the effects of attribute-specific sentiments on CS and RVI were evaluated by a bagged neural network-based model. The proposed framework was evaluated by 305,000 restaurant comments collected from DianPing.com, a Yelp-like website in China.
Findings
The authors obtained a hierarchal importance order of the investigated service themes (i.e. location, service, environment, price and food). The authors found that food played the most important role in affecting both CS and RVI. The most salient attribute with respect to each service theme was also identified.
Originality/value
Unlike prior work relying on the data collected from surveys, this study is among the first to model the relationship among service attributes, CS and RVI simultaneously from real-world data. The authors established a hierarchal structure of eighteen attributes within five service themes and estimated their effects on both CS and RVI, which will broaden our understanding of customer perception and behavioral intention during service consumption.
Keywords
Acknowledgements
This work has been supported by the National Natural Science Foundation of China (Grant no. 72274032), the Fundamental Research Funds for the Central Universities (Grant no. N2223033), the Humanities and Social Science Research Project of Hebei Education Department (Grant no. BJ2021104), the Natural Science Foundation of Hebei Province (Grant no. G2021501012), and the Social Science Foundation of Liaoning Province (Grant no. L20BXW004).
Citation
Zhao, F. and Liu, H. (2023), "Modeling customer satisfaction and revisit intention from online restaurant reviews: an attribute-level analysis", Industrial Management & Data Systems, Vol. 123 No. 5, pp. 1548-1568. https://doi.org/10.1108/IMDS-09-2022-0570
Publisher
:Emerald Publishing Limited
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