The DOI matches its Crossref record; 61 of the 64 references it cites were found in a registry or an index.
What can big data and text analytics tell us about hotel guest experience and satisfaction?
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“allow for a better understanding of consumer behavior, interactions, and experiences with a product or service [77,78]”
From Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020) · cites this paper as [78]
In the citing paper
“Studies, such as those of Marine-Roig and Clavé [47] and Xiang et al. [74], suggested that big data provides a huge amount of detail that contains deep-structure information on experiences, feelings, interests and opinions.”
From A Big Data Analytics Method for Tourist Behaviour Analysis (2017) · cites this paper as [74]
Citations
Cites
Herne, H., Cooley, W. W., & Lohnes, P. R. (1973) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344428 · Cited by 35,801
Content Analysis: An Introduction to its Methodology.
Shelley, M., & Krippendorff, K. (1984) · Journal of the American Statistical Association · doi:10.2307/2288384 · Cited by 24,346
Content Analysis: An Introduction to Its Methodology
Krippendorff, K. (2019) · doi:10.4135/9781071878781 · Cited by 15,448
Multivariate Data Analysis, 6th ed
Tatham, R. L., Anderson, R. E., Babin, B. J., Black, W. C., & Hair, J. F. (2006) · Cited by 9,807
Hanrahan, G. (2008) · CRC Press eBooks · doi:10.1201/9781420067972-10 · Cited by 8,196
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957
Opinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372
boyd, D., & Crawford, K. (2012) · Information, Communication & Society · doi:10.1080/1369118x.2012.678878 · Cited by 5,479
(2011) · Cited by 5,203
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
Research works printed but not linked: 4
- Aiden, 2014, The Predictive Power of Big Data, Newsweek
- Marcus, 2014, Eight (no, nine!) problems with big data, The New York Times
- Min, H., Lim, Y., Magnini, V.P., in press. Responding to negative online hotel reviews: the impacts of empathy statements, paraphrasing and speed. Cornell Hosp. Q.
- Oliver, 1981, Measurement and evaluation of satisfaction processes in retail settings, J. Retail., 57, 25
Cited by
Sorted by citations
Big data in tourism research: A literature review
Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018) · Tourism Management · doi:10.1016/j.tourman.2018.03.009 · Cited by 1,077
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020) · Information & Management · doi:10.1016/j.im.2019.05.004 · Cited by 1,373
“Insight generated through a strong BDAC can enable more precise needs identification through sentiment sensing and social media monitoring for instance [46,76], allow for a better understanding of consumer behavior, interactions, and experiences with a product or service [77,78], facilitate more detailed and real-time…”
BackgroundXiang, Z., Du, Q., Ma, Y., & Fan, W. (2016) · Tourism Management · doi:10.1016/j.tourman.2016.10.001 · Cited by 1,192
Self-citationMariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022) · Psychology & Marketing · doi:10.1002/mar.21619 · Cited by 631
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
Metadata
- Authors
- Xiang, Z., Schwartz, Z., Gerdes, J. H., & Uysal, M.
- Affiliations
- Virginia Tech (US)
- University of Delaware (US)
- University of South Carolina (US)
- Corresponding author
- Xiang, Zheng
- Venue
- International Journal of Hospitality Management
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 44
- Pages
- 120-130
- ISSN
- 0278-4319 (ISSN-L)
- OpenAlex
- W1992777957
- Cited by
- 953
- References
- 64
- Citations by year
- 2026: 62 · 2025: 80 · 2024: 66 · 2023: 92 · 2022: 117 · 2021: 120 · 2020: 107 · 2019: 111 · 2018: 66 · 2017: 73 · 2016: 41 · 2015: 16
- FWCI
- 96.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Hospitality Management (publishedVersion)
- eSpace (Curtin University) (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Customer Service Quality and Loyalty
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Tourism, Leisure and Hospitality Management › Diverse Aspects of Tourism Research
- Keywords
- hotel guest experience, guest satisfaction, big data analytics, text analysis, consumer reviews, Expedia, user-generated content, social media, semantic composition, hospitality
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.
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