Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service
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Otros documentos de la autoría: Oh, Soyoung; Ji, honggeun; Kim, Jina; Park, Eunil; del Pobil, Angel P.
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https://doi.org/10.1007/s40558-022-00222-z |
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Título
Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality serviceFecha de publicación
2022-02-15Editor
SpringerISSN
1098-3058; 1943-4294Cita bibliográfica
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-zTipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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. [-]
Publicado en
Information Technology & Tourism (2022) 24:109–126Entidad financiadora
Korean Government | Institute of Information and communications Technology Planning and Evaluation (IITP)
Código del proyecto o subvención
NRF-2020R1C1C1004324 | 2021-0-00358
Título del proyecto o subvención
Big data based Cyber Security Orchestration and Automated Response Technology Development
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Copyright © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature
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