Analyzing changes in hotel customers' expectations by trip mode

Shaowu Liu, Chun Hung Roberts Law, Jia Rong, Gang Li, John Hall

Research output: Journal article publicationJournal articleAcademic researchpeer-review

73 Citations (Scopus)

Abstract

With the emergence of Web 2.0, electronic word-of-mouth (eWOM) shared through social networking sites has become the primary information source for many travelers. An enormous quantity of reviews has been posted by customers, and has become a valuable means by which hoteliers can better understand customer satisfaction and expectations. Efforts have been made to analyze these parameters in terms of customers' backgrounds. Although customer expectations vary according to background, whether or not this is still the case across different trip modes remains unknown. In this study, an eWOM dataset was obtained from an online source and sentiment mining used to improve its quality by imputing missing values. A complete analysis of customer profiles and their contrast by trip mode was then conducted using association rule mining. The empirical results demonstrate differences in both customer expectation and satisfaction when the same traveler engages in different trip modes.
Original languageEnglish
Pages (from-to)359-371
Number of pages13
JournalInternational Journal of Hospitality Management
Volume34
Issue number1
DOIs
Publication statusPublished - 1 Sep 2013

Keywords

  • Association rule
  • Contrast analysis
  • Customer expectation, Sentiment mining
  • Data mining
  • EWOM

ASJC Scopus subject areas

  • Tourism, Leisure and Hospitality Management
  • Strategy and Management

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