Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews

被引：329
作者：
Zhao, Yabing [1 ]
Xu, Xun [2 ]
Wang, Mingshu [3 ]
机构：
[1] San Francisco State Univ, Coll Business, Dept Decis Sci, 1600 Holloway Ave, San Francisco, CA 94132 USA
[2] Calif State Univ Stanislaus, Coll Business Adm, Dept Management Operat & Mkt, One Univ Circle, Turlock, CA 95382 USA
[3] Univ Georgia, Dept Geog, 210 Field St, Athens, GA 30602 USA
来源：
INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT | 2019年 / 76卷
关键词：
Online textual reviews; Technical attributes; Overall customer satisfaction; Hotel industry; Big data; WORD-OF-MOUTH; CONSUMER-GENERATED MEDIA; SOCIAL MEDIA; SENTIMENT ANALYSIS; IMPACT; MODEL; ANTECEDENTS; HELPFULNESS; EXPERIENCE; LANGUAGE;
D O I：
10.1016/j.ijhm.2018.03.017
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
Customer online reviews of hotels have significant business value in the e-commerce and big data era. Online textual reviews have an open-structured form, and the technical side, namely the linguistic attributes of online textual reviews, is still largely under-explored. Using a sample of 127,629 reviews from tripadvisor.com, this study predicts overall customer satisfaction using the technical attributes of online textual reviews and customers' involvement in the review community. We find that a higher level of subjectivity and readability and a longer length of textual review lead to lower overall customer satisfaction, and a higher level of diversity and sentiment polarity of textual review leads to higher overall customer satisfaction. We also find that customers' review involvement positively influences their overall satisfaction. We provide implications for hoteliers to better understand customer online review behavior and implement efficient online review management actions to use electronic word of mouth and enhance hotels' performance.
引用
收藏
页码：111 / 121
页数：11
相关论文
共 85 条
[51]   ASSESSING THE DIMENSIONALITY AND STRUCTURE OF THE CONSUMPTION EXPERIENCE - EVALUATION, FEELING, AND SATISFACTION [J].
MANO, H ;
OLIVER, RL .
JOURNAL OF CONSUMER RESEARCH, 1993, 20 (03) :451-466
[52]  
Mccoll-Kennedy J.R., 2003, Journal of Service Research, V5, P251, DOI [10.1177/1094670502238918, DOI 10.1177/1094670502238918]
[53]   Analyzing user sentiment in social media: Implications for online marketing strategy [J].
Micu, Adrian ;
Micu, Angela Eliza ;
Geru, Marius ;
Lixandroiu, Radu Constantin .
PSYCHOLOGY & MARKETING, 2017, 34 (12) :1094-1100
[54]   A COGNITIVE MODEL OF THE ANTECEDENTS AND CONSEQUENCES OF SATISFACTION DECISIONS [J].
OLIVER, RL .
JOURNAL OF MARKETING RESEARCH, 1980, 17 (04) :460-469
[55]   Forecasting Destination Weekly Hotel Occupancy with Big Data [J].
Pan, Bing ;
Yang, Yang .
JOURNAL OF TRAVEL RESEARCH, 2017, 56 (07) :957-970
[56]   Determinants of online merchant rating: Content analysis of consumer comments about Yahoo merchants [J].
Qu, Zhe ;
Zhang, Han ;
Li, Haizheng .
DECISION SUPPORT SYSTEMS, 2008, 46 (01) :440-449
[57]   Online Customer Experience: A Review of the Business-to-Consumer Online Purchase Context [J].
Rose, Susan ;
Hair, Neil ;
Clark, Moira .
INTERNATIONAL JOURNAL OF MANAGEMENT REVIEWS, 2011, 13 (01) :24-39
[58]   Contextual semantics for sentiment analysis of Twitter [J].
Saif, Hassan ;
He, Yulan ;
Fernandez, Miriam ;
Alani, Harith .
INFORMATION PROCESSING & MANAGEMENT, 2016, 52 (01) :5-19
[59]   Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics [J].
Salehan, Mohammad ;
Kim, Dan J. .
DECISION SUPPORT SYSTEMS, 2016, 81 :30-40
[60]  
Schoefer K., 2005, J SERV MARK, V19, P261, DOI DOI 10.1108/08876040510609880
123456789