{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:19:26Z","timestamp":1776093566536,"version":"3.50.1"},"reference-count":48,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:00:00Z","timestamp":1617321600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2021,8,5]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>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).<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-06-2020-0123","type":"journal-article","created":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T23:38:18Z","timestamp":1617406698000},"page":"542-557","source":"Crossref","is-referenced-by-count":6,"title":["Between comments and repeat visit: capturing repeat visitors with a hybrid approach"],"prefix":"10.1108","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1140-6113","authenticated-orcid":false,"given":"Jina","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1606-874X","authenticated-orcid":false,"given":"Yeonju","family":"Jang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8143-051X","authenticated-orcid":false,"given":"Kunwoo","family":"Bae","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soyoung","family":"Oh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nam Jeong","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3177-3538","authenticated-orcid":false,"given":"Eunil","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyoung","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angel P.","family":"del Pobil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2021,4,2]]},"reference":[{"key":"key2022092811512783100_ref001","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1108\/03090560910946990","article-title":"Analysing the effect of satisfaction and previous visits on tourist intentions to return","volume":"43","year":"2009","journal-title":"European Journal of Marketing"},{"key":"key2022092811512783100_ref002","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1080\/13683500.2019.1594723","article-title":"Analyzing online reviews in hospitality: data-driven opportunities for predicting the sharing of negative emotional content","volume":"22","year":"2019","journal-title":"Current Issues in Tourism"},{"key":"key2022092811512783100_ref003","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1177\/109467059800100102","article-title":"Customer satisfaction and word of mouth","volume":"1","year":"1998","journal-title":"Journal of Service Research"},{"key":"key2022092811512783100_ref004","first-page":"336","article-title":"How online reviews in a year predict online sales in the next on expedia. com+ agoda. com+ hotels. com? 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