Between comments and repeat visit: capturing repeat visitors with a hybrid approach
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
comunitat-uji-handle4:
INVESTIGACIONMetadata
Title
Between comments and repeat visit: capturing repeat visitors with a hybrid approachAuthor (s)
Date
2021-08-05Publisher
EmeraldBibliographic citation
Kim, J., Jang, Y., Bae, K., Oh, S., Jeong, N.J., Park, E., Han, J. and del Pobil, A.P. (2021), "Between comments and repeat visit: capturing repeat visitors with a hybrid approach", Data Technologies and Applications, Vol. 55 No. 4, pp. 542-557. https://doi.org/10.1108/DTA-06-2020-0123Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/submittedVersionSubject
Abstract
Purpose
Understanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of ... [+]
Purpose
Understanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of this study is to investigate customers' reviews on an online hotel reservation platform, and explores their postbehaviors from their reviews.
Design/methodology/approach
The authors employ two different approaches and compare the accuracy of predicting customers' post behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit the hotel again (referred to as Prediction), while the second subsection examines whether participants want to visit one of the particular hotels when they read other customers' reviews (dubbed as Decision).
Findings
The accuracy of the machine learning approaches (73.23%) is higher than that of the experimental approach (Prediction: 58.96% and Decision: 64.79%). The key reasons of users' predictions and decisions are identified through qualitative analyses.
Originality/value
The findings reveal that using machine learning approaches show the higher accuracy of predicting customers' repeat visits only based on employed sentimental features. With the novel approach of integrating customers' decision processes and machine learning classifiers, the authors provide valuable insights for researchers and providers of hospitality services. [-]
Is part of
Data Technologies and Applications, Vol. 55, No. 4, 2021Funder Name
Ministry of Science and ICT (MSIT), Korea, under the ICT Creative Consilience program | National Research Foundation of Korea (NRF)
Project code
IITP-2020-0-01821 | 2020R1F1A1048225 | NRF-2020R1C1C1004324
Rights
Copyright © 2021, Emerald Publishing Limited
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