The DOI matches its Crossref record; 99 of the 99 references it cites were found in a registry or an index.
Motivation and satisfaction of Chinese and U.S. tourists in restaurants: A cross-cultural text mining of online reviews
Abstract
Abstract Tourists with dissimilar cultural backgrounds think and behave differently. Precisely capturing and correctly understanding the cultural difference will help tourism managers generate greater customer satisfaction and increased business revenue. To this end, this paper uncovers and compares the motivation and satisfaction of restaurant tourist customers coming from China and U.S. by investigating their online ratings and reviews. From two major online review communities, customer ratings and reviews have been retrieved, quantified, text-mined, compared, and interpreted using statistics, latent Dirichlet allocation, and frequency analysis. Results suggest that Chinese tourists are less inclined to assign lower ratings to restaurants, and are more strongly fascinated by the food offered, whereas U.S. tourists are more apt to be fun-seeking, and are less uncomfortable with crowdedness.
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
SERVQUAL: A multiple-Item Scale for measuring consumer perceptions of service quality
Parasuraman, A., Berry, L. L., & Zeithaml, V. A. (1988) · Zenodo · doi:10.5281/zenodo.14579729 · Cited by 21,118
Cultures and Organizations: Software of the Mind.
Triandis, H. C., & Hofstede, G. (1993) · Administrative Science Quarterly · doi:10.2307/2393257 · Cited by 17,696
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516
Cultures and organizations: software of the mind
(1992) · Choice Reviews Online · doi:10.5860/choice.29-4203 · Cited by 3,673
Netnography: Doing Ethnographic Research Online
O’Donohoe, S. (2010) · International Journal of Advertising · doi:10.2501/s026504871020118x · Cited by 1,983
Servqual : a multiple-item scale for measuring customer perceptions of service quality
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1986) · Marketing Science Institute eBooks · Cited by 1,850
Quan, S., & Wang, N. (2004) · Tourism Management · doi:10.1016/s0261-5177(03)00130-4 · Cited by 1,721
Netnography: Doing Ethnographic Research Online
Kozinets, R. V. (2009) · Cited by 1,564
Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud
Luca, M., & Zervas, G. (2016) · Management Science · doi:10.1287/mnsc.2015.2304 · Cited by 1,263
Metadata
- Authors
- Jia, S. (.
- Venue
- Tourism Management
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 78
- Pages
- 104071
- ISSN
- 0261-5177 (ISSN-L)
- OpenAlex
- W2999582513
- Cited by
- 253
- References
- 99
- FWCI
- 22.03
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Motivation and satisfaction of Chinese and U.S. tourists in restaurants: A cross-cultural text mining of online reviews. https://app.cochat.ai/papers/10.1016/j.tourman.2019.104071
- The paper
- Jia, S. (. (2020). Motivation and satisfaction of Chinese and U.S. tourists in restaurants: A cross-cultural text mining of online reviews. Tourism Management. https://doi.org/10.1016/j.tourman.2019.104071
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.
