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

Zhao, Y., Xu, X. and Wang, M. (2019) Predicting overall customer satisfaction: big data evidence from hotel online textual reviews. International Journal of Hospitality Management, 76(Part A), pp. 111-121. (doi: 10.1016/j.ijhm.2018.03.017)

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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, 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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Wang, Dr Mingshu
Authors: Zhao, Y., Xu, X., and Wang, M.
College/School:College of Science and Engineering > School of Geographical and Earth Sciences
Journal Name:International Journal of Hospitality Management
ISSN (Online):1873-4693
Published Online:15 May 2018

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