Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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https://doi.org/10.1007/s40558-022-00222-z |
Metadata
Title
Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality serviceDate
2022-02-15Publisher
SpringerISSN
1098-3058; 1943-4294Bibliographic citation
Oh, S., Ji, H., Kim, J. et al. Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service. Inf Technol Tourism 24, 109–126 (2022). https://doi.org/10.1007/s40558-022-00222-zType
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/publishedVersionAbstract
Customer satisfaction is one of the most important measures in the hospitality
industry. Therefore, several psychological and cognitive theories have been utilized
to provide appropriate explanations of customer ... [+]
Customer satisfaction is one of the most important measures in the hospitality
industry. Therefore, several psychological and cognitive theories have been utilized
to provide appropriate explanations of customer perception. Owing to recent rapid
developments in artifcial intelligence and big data, novel methodologies have presented to examine several psychological theories applied in the hospitality industry. Within this framework, this study combines deep learning techniques with the
expectation-confrmation theory to elucidate customer satisfaction in hospitality
services. Customer hotel review comments, hotel information, and images were
employed to predict customer satisfaction with hotel service. The results show that
the proposed fused model achieved an accuracy of 83.54%. In addition, the recall
value that predicts dissatisfaction improved from 16.46–33.41%. Based on the fndings of this study, both academic and managerial implications for the hospitality
industry are presented. [-]
Is part of
Information Technology & Tourism (2022) 24:109–126Funder Name
Korean Government | Institute of Information and communications Technology Planning and Evaluation (IITP)
Project code
NRF-2020R1C1C1004324 | 2021-0-00358
Project title or grant
Big data based Cyber Security Orchestration and Automated Response Technology Development
Rights
Copyright © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature
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- ICC_Articles [425]