The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies
Open accessOverview
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.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Hennig-Thurau et al. (2015), show that while garnering positive WOM has little to no effect on consumer preferences, negative WOM can have a negative effect on consumer preferences.”
From The future of social media in marketing (2020) · cites this paper as Hennig-Thurau et al. (2015)
Experimental — accuracy not yet measured
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
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
Prospect theory: An analysis of decision under risk
Kahneman, D. J., & Tversky, A. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511609220.014 · Cited by 32,720
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Organizational Information Requirements, Media Richness and Structural Design
Daft, R. L., & Lengel, R. H. (1986) · Management Science · doi:10.1287/mnsc.32.5.554 · Cited by 9,868
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
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
Self-citationAdvances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
Platt, J. C. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/1130.003.0016 · Cited by 5,501
Research works printed but not linked: 1
- Hayes, D. (2002). Tentpoles fade fast after opening ad blast. Variety Magazine, 4, 2002.
Cited by
Sorted by citations
The future of social media in marketing
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00695-1 · Cited by 1,893
“2019) , as well as how it interacts with other forms of media such as television (e.g., Schweidel 2016, 2019) and affects new product adoption through diffusion of information mechanisms (e.g., Hennig-Thurau et al.”
BackgroundInfluentialBabić Rosario, A., Sotgiu, F., De Valck, K., & Bijmolt, T. H. A. (2016) · Journal of Marketing Research · doi:10.1509/jmr.14.0380 · Cited by 1,073
“Whereas operationalizations using relative terms, such as the ratio of positive tweets (e.g., Hennig-Thurau, Wiertz, and Feldhaus 2014) or the percentage of one-star ratings (e.g., Chen, Wang, and Xie 2011), are a nonconfounded representation of sentiment (positive or negative valence),…”
BackgroundMethodUniting the Tribes: Using Text for Marketing Insight
Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2020) · Journal of Marketing · doi:10.1177/0022242919873106 · Cited by 742
Xia, L., Shin, H., & Burns, A. C. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.04.042 · Cited by 539
Chae, B. (. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.12.037 · Cited by 481
Metadata
- Authors
- Hennig-Thurau, T., Wiertz, C., & Feldhaus, F.
- Affiliations
- City, University of London (GB)
- University of Münster (DE)
- Corresponding author
- Wiertz, Caroline
- Venue
- Journal of the Academy of Marketing Science
- Published
- 2014-06-01
- Volume
- 43
- Issue
- 3
- Pages
- 375-394
- ISSN
- 0092-0703 (ISSN-L)
- 1552-7824
- OpenAlex
- W3124914435
- Cited by
- 434
- References
- 54
- Citations by year
- 2026: 13 · 2025: 28 · 2024: 33 · 2023: 38 · 2022: 47 · 2021: 44 · 2020: 38 · 2019: 48 · 2018: 54 · 2017: 49 · 2016: 19 · 2015: 10 · 2014: 4
- FWCI
- 89.02
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of the Academy of Marketing Science (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- City Research Online (City University London) (acceptedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior and Marketing Influence
- Social Sciences › Communication › Media and Digital Communication
- Keywords
- negativity bias, purchase decision
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
