The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
An Analysis of One-Star Online Reviews and Responses in the Washington, D.C., Lodging Market
Abstract
The hotel industry continues to develop strategies for addressing consumer-generated online reviews, and particularly responding to poor reviews, which can have a damaging effect on a hotel’s reputation. To gain a greater understanding of the dynamics of poor reviews, this study analyzed 1,946 one-star reviews from ten popular online review websites, as well as 225 management responses from eighty-six Washington, D.C., hotels. A comprehensive complaint framework found that the most common complaints related to front desk staff, bathroom issues, room cleanliness, and guestroom noise issues. Complaints were also analyzed by hotel characteristics, including chain-scale segments, and reviewer characteristics, including purpose of travel and geographic location. Examining the reviews, highly rated hotels often respond to online complaints with appreciation, apologies, and explanations for what had gone wrong. Compensation adjustments are rarely mentioned by any hotel. The increasingly prominent role of social media necessitates that hotels use online reviews for market research and service recovery opportunities, regardless of whether they respond publicly.
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Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Electronic word-of-mouth in hospitality and tourism management
Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008) · Tourism Management · doi:10.1016/j.tourman.2007.05.011 · Cited by 3,267
The impact of online reviews on hotel booking intentions and perception of trust
Sparks, B. A., & Browning, V. (2011) · Tourism Management · doi:10.1016/j.tourman.2010.12.011 · Cited by 1,688
Tried and tested: The impact of online hotel reviews on consumer consideration
Vermeulen, I. E., & Seegers, D. (2009) · Tourism Management · doi:10.1016/j.tourman.2008.04.008 · Cited by 1,311
Mattila, A. S., & Mount, D. J. (2003) · International Journal of Hospitality Management · doi:10.1016/s0278-4319(03)00014-8 · Cited by 151
Cited by
Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017) · Tourism Management · doi:10.1016/j.tourman.2016.10.001 · Cited by 1,051
Airbnb – an exploration of value co-creation experiences in Jamaica
Johnson, A.-G., & Neuhofer, B. (2017) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-08-2016-0482 · Cited by 184
Twenty‐Five Years of Word‐of‐Mouth Studies: A Critical Review of Tourism Research
Confente, I. (2015) · International Journal of Tourism Research · doi:10.1002/jtr.2029 · Cited by 111
Correlating Languages and Sentiment Analysis on the Basis of Text-based Reviews
García-Pablos, A., Duca, A. L., Cuadros, M., Linaza, M. T., & Marchetti, A. (2016) · Information and Communication Technologies in Tourism 2016 · doi:10.1007/978-3-319-28231-2_41 · Cited by 14
Metadata
- Authors
- Levy, S. E., Duan, W., & Boo, S.
- Venue
- Cornell Hospitality Quarterly
- Publisher
- SAGE Publications
- Published
- 2012-12-18
- Volume
- 54
- Issue
- 1
- Pages
- 49-63
- ISSN
- 1938-9655 (ISSN-L)
- 1938-9663
- OpenAlex
- W2171689662
- Cited by
- 352
- References
- 5
- FWCI
- 37.01
- Type
- journal article
- Language
- en
- Open access
- closed
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