The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Understanding the Impact of Online Reviews on Hotel Performance: An Empirical Analysis
Open accessAbstract
Understanding consumers’ needs and wants has been a major source of success for hotel organizations. Notwithstanding, investigating the valence of online reviews and modeling hotel attributes and performance is still a rather novel approach. Using partial least squares path modeling, Swiss country-level data for online reviews from 68 online platforms, together with data from 442 hotels, we test 11 hypotheses. Our research model includes three distinctive areas of the hotel: physical aspects, quality of food and drink, and human aspects of service provision. RevPAR and occupancy are employed as performance metrics. We also test for mediation effects. Results indicate that hotel attributes, including the quality of rooms, Internet provision and building show the highest impact on hotel performance, and that positive comments have the highest impact on customer demand. This study contributes to theories of valence on hotel performance and presents salient implications for practitioners to enhance performance.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007) · Behavior Research Methods · doi:10.3758/bf03193146 · Cited by 65,971
A primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921
Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models
Sobel, M. E. (1982) · Sociological Methodology · doi:10.2307/270723 · Cited by 12,810
(2014) · DigitalCommons - Kennesaw State University (Kennesaw State University) · Cited by 12,785
(1998) · Cited by 12,725
Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001) · Review of General Psychology · doi:10.1037/1089-2680.5.4.323 · Cited by 7,678
Modern Methods for Business Research
(1998) · doi:10.4324/9781410604385 · Cited by 6,534
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004) · Journal of Interactive Marketing · doi:10.1002/dir.10073 · Cited by 5,916 · cited 15×
“Hypothesis 11: The effect of negative sentiment about hotel (a), grounds (b), building (c), ambiance (d) rooms, and (e) Internet on demand is mediated by negative sentiment about physical hotel attributes.”
BackgroundInfluential
Research works printed but not linked: 9
- Anderson C., 2012, Cornell Hospitality Report, 12, 1
- 10.1177/0047287511426337 · doi:10.1177/0047287511426337
- 10.1002/jtr.333 · doi:10.1002/jtr.333
- Chen P.-Y., 2004, Proceedings of the International Conference on Information Systems, ICIS 2004, 711
- Dolnicar S., 2003, Proceedings of the 9th Annual Conference of the Asia Pacific Tourism Association (APTA), 176
- 10.1080/14790530600638788 · doi:10.1080/14790530600638788
- 10.1037/0021-9010.82.2.247 · DOI · Paper page
- XLSTAT, 2015, XLSTAT: PLSPM Module
- 10.1300/J396v25n02_01 · doi:10.1300/j396v25n02_01
Cited by
Sentiment Analysis in Tourism: Capitalizing on Big Data
Alaei, A. R., Becken, S., & Stantic, B. (2019) · Journal of Travel Research · doi:10.1177/0047287517747753 · Cited by 622
Applications of big data in emerging management disciplines: A literature review using text mining
Kushwaha, A. K., Kar, A. K., & Dwivedi, Y. K. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2021.100017 · Cited by 255
El-Said, O. A. (2020) · Tourism Management Perspectives · doi:10.1016/j.tmp.2019.100604 · Cited by 176
Metadata
- Authors
- Phillips, P., Barnes, S., Zigan, K., & Schegg, R.
- Venue
- Journal of Travel Research
- Publisher
- SAGE Publications
- Published
- 2016-08-04
- Volume
- 56
- Issue
- 2
- Pages
- 235-249
- ISSN
- 0047-2875 (ISSN-L)
- 1552-6763
- OpenAlex
- W2293447313
- Cited by
- 257
- References
- 95
- FWCI
- 59.07
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
