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dc.contributor.authorKim, Jina
dc.contributor.authorJang, Yeonju
dc.contributor.authorBae, Kunwoo
dc.contributor.authorOh, Soyoung
dc.contributor.authorNam, Jeong Jeong
dc.contributor.authorPark, Eunil
dc.contributor.authorHan, JinYoung
dc.contributor.authordel Pobil, Angel P.
dc.date.accessioned2021-10-19T11:45:42Z
dc.date.available2021-10-19T11:45:42Z
dc.date.issued2021-08-05
dc.identifier.citationKim, 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-06-2020-0123ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/195077
dc.description.abstractPurpose 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.ca_CA
dc.format.extent16 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherEmeraldca_CA
dc.relation.isPartOfData Technologies and Applications, Vol. 55, No. 4, 2021ca_CA
dc.rightsCopyright © 2021, Emerald Publishing Limitedca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/ca_CA
dc.subjectonline commentsca_CA
dc.subjectrepeat visitca_CA
dc.subjectmachine learningca_CA
dc.subjectonline reviewsca_CA
dc.subjectuser experienceca_CA
dc.titleBetween comments and repeat visit: capturing repeat visitors with a hybrid approachca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1108/DTA-06-2020-0123
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.type.versioninfo:eu-repo/semantics/submittedVersionca_CA
project.funder.nameMinistry of Science and ICT (MSIT), Korea, under the ICT Creative Consilience programca_CA
project.funder.nameNational Research Foundation of Korea (NRF)ca_CA
oaire.awardNumberIITP-2020-0-01821ca_CA
oaire.awardNumber2020R1F1A1048225ca_CA
oaire.awardNumberNRF-2020R1C1C1004324ca_CA


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