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    請使用永久網址來引用或連結此文件: https://ir.lib.ncu.edu.tw/handle/987654321/106947


    題名: Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings
    作者: 胡雅涵;Hu, Ya-Han;Chen, Kuanchin
    貢獻者: 管理學院資訊管理學系
    關鍵詞: Categories;Classroom communication;Constants;Customer feedback;Electronics;eWOM;Helping behavior;Hotels;Hotels & motels;Mathematical models;Measurement;Online hotel reviews;Ratings;Ratings & rankings;Regression;Regression analysis;Review helpfulness;Review visibility;Sentiment analysis;Social networks;Studies;Subsets;Support vector machines;Tourism;Variables;Visibility;Web site reviews;Word of mouth advertising
    日期: 2016-12-01
    上傳時間: 2026-04-23 13:50:13 (UTC+8)
    出版者: Elsevier Ltd.;Kidlington: Elsevier Ltd
    摘要: 摘要: •The interaction effect of Hotel Star Class (HSC) and Review Rating (RR) is confirmed to affect review helpfulness.•The concept of review helpfulness is posed to include three variables. All had a varying and significant effect on review helpfulness.•Model tree (M5P) outperformed linear regression and support vector regression.•Certain predictors (such as badges earned) are back-traced to the time when a review was written in order to improve prediction accuracy of existing studies.•Prediction accuracy improves after review visibility, interaction effect and improved data collection are taken into account. The tourism industry has been strongly influenced by electronic word-of-mouth (eWOM) in recent years. Currently, there are only limited studies available that look into hotel review helpfulness. This present study addresses three hidden assumptions prevalent in online review studies: (1) all reviews are visible equally to online users, (2) review rating (RR) and hotel star class (HSC) affect review helpfulness individually with no interaction, and (3) characteristics of reviews and reviewer status stay constant. Four categories of input variables were considered in the present study: review content, sentiment, author, and visibility. Our findings confirmed the interaction effect between HSC and RR. The data set was sub-divided into eight subsets as a result. Three review visibility indicators (including days since a review was posted, days since a review has remained on the home page, and number of reviews with the same rating at the time a review was written) had a varying and strong effect on review helpfulness. The model performance was greatly improved after taking account of review visibility features, the interaction effect of HSC and RR, and a more accurate measurement of variables. Model tree (M5P) outperformed linear regression and support vector regression as it better modeled the interaction effect.
    出版者: Kidlington: Elsevier Ltd
    出版日期: 2016-12
    出處: International Journal of Information Management, 2016-12, Vol.36 (6), p.929-944
    資源來源: Elsevier ScienceDirect Journals Complete
    版權: 2016 Elsevier Ltd
    版權: Copyright Elsevier Science Ltd. Dec 2016
    識別號: ISSN: 0268-4012
    識別號: EISSN: 1873-4707
    識別號: DOI: 10.1016/j.ijinfomgt.2016.06.003
    顯示於類別:[資訊管理學系] 期刊論文

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