Online Consumer Review Factors Affecting Offline Hotel Popularity: Evidence from Tripadvisor

ABSTRACT

The business value of online consumer reviews has emerged in recent year as one of utmost importance for hotel marketers. This study examines how online consumer reviews affect offline hotel popularity. Using time-series data of 56,284 hotel reviews posted for more than 1000 hotels listed on TripAdvisor, this paper estimates the effect of factors of online consumer review, including quality, quantity, consistency, and recency, on the offline hotel occupancy (i.e. how popular the hotel is among consumers). The empirical evidence shows the relative effect of online consumer review factors on offline hotel popularity when controlling for other hotel characteristics. In particular, the effect of review quality lasts for at least a couple of quarters, whereas that of other online consumer review factors remains short-term. The findings provide a managerial basis to improve the online presence of hotels on social media platforms by strategically utilizing important review factors.

KEYWORDS:

• Online consumer review
• offline hotel popularity
• hotel occupancy
• social media marketing

Notes

1. “Fully automated parsing” refers to the approach used to collect information from a website. We developed two crawlers using Ruby (1) to download automatically the web pages of hotel reviews and other hotel information from TripAdvisor, and (2) to remove the HTML formatting from the text and then convert it into an XML file that separated the data into records (the review) and fields (the data in each review) in an automated fashion using a precoded computer program on the local machine. We used the crawlers to retrieve all available user-generated content information for the designated hotels. For each hotel, we obtained all of the posted reviews. Each consumer review was analyzed and selected review features were recorded.

2. In order to account for the nonlinearities and to smooth skewed distributions (Greene, 2003), we took the logarithm of the hotel occupancy. Similar variable transformation has also been applied on the average daily rate. For purposes of brevity, the nonlinearity and skewness of each variable is not shown but is available upon request.

Additional information

Notes on contributors

Karen L. Xie

Karen L. Xie, PhD is Assistant Professor, Hospitality Management, Daniels College of Business, University of Denver, Denver, CO, USA, USA (E-mail: [email protected]).

Chihchien Chen

Chihchien Chen, PhD, is Assistant Professor, College of Hotel Administration, University of Nevada, Las Vegas, NV, USA (E-mail: [email protected]).

Shinyi Wu

Shinyi Wu, is Associate Professor, Information Systems, W. P. Carey School of Business, Arizona State University, Tempe, AZ, USA (E-mail: [email protected]).