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
The theory and practice of econometrics
(1986) · Journal of Macroeconomics · doi:10.1016/0164-0704(86)90027-3 · Cited by 4,369
Attribution: Perceiving the Causes of Behavior
JONES, E. E. (1987) · Medical Entomology and Zoology · Cited by 3,760
Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022 · cited 1×
“Empirical findings indicate that such information is of limited relevance (Cha et al. 2010); however, we must find out more about this metric’s role.”
Corfman, K. P., & Lynch, J. G. (1983) · University of Groningen research database (University of Groningen / Centre for Information Technology) · Cited by 2,944
The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer
Levin, D. Z., & Cross, R. (2004) · Management Science · doi:10.1287/mnsc.1030.0136 · Cited by 2,824
Using Online Conversations to Study Word-of-Mouth Communication
Godes, D., & Mayzlin, D. (2004) · Marketing Science · doi:10.1287/mksc.1040.0071 · Cited by 2,601
No Pain, No Gain: A Critical Review of the Literature on Signaling Unobservable Product Quality
Kirmani, A., & Rao, A. R. (2000) · Journal of Marketing · doi:10.1509/jmkg.64.2.66.18000 · Cited by 2,286
Thumbs up? Sentiment Classification using Machine Learning TechniquesPreprint
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0205070 · Cited by 2,211
Predicting the future with social media
Asur, S., & Huberman, B. A. (2010) · 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology · doi:10.1109/wi-iat.2010.63 · Cited by 2,081
Role of Product-Related Conversations in the Diffusion of a New Product
Arndt, J. (1967) · Journal of Marketing Research · doi:10.1177/002224376700400308 · Cited by 1,932
Word of mouth and interpersonal communication: A review and directions for future research
Berger, J. (2014) · Journal of Consumer Psychology · doi:10.1016/j.jcps.2014.05.002 · Cited by 1,469
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Das, S. R., & Chen, M. Y. (2007) · Management Science · doi:10.1287/mnsc.1070.0704 · Cited by 1,370
Testing Media Richness Theory in the New Media: The Effects of Cues, Feedback, and Task Equivocality
Dennis, A. R., & Kinney, S. T. (1998) · Information Systems Research · doi:10.1287/isre.9.3.256 · Cited by 1,023
How Critical are Critical Reviews? The Box Office Effects of Film Critics, Star Power, and Budgets
Basuroy, S., Chatterjee, S., & Ravid, S. A. (2003) · Journal of Marketing · doi:10.1509/jmkg.67.4.103.18692 · Cited by 935
Film Critics: Influencers or Predictors?
Eliashberg, J., & Shugan, S. M. (1997) · Journal of Marketing · doi:10.1177/002224299706100205 · Cited by 896
The Theory and Practice of Econometrics
Frydman, R., Judge, G. G., Griffith, W. E., Hill, R. C., & Lee, T.-C. (1985) · Journal of Business and Economic Statistics · doi:10.2307/1391738 · Cited by 842
Role of Product-Related Conversations in the Diffusion of a New Product
Arndt, J. (1967) · Journal of Marketing Research · doi:10.2307/3149462 · Cited by 747
Online Social Interactions: A Natural Experiment on Word of Mouth versus Observational Learning
Chen, Y., Wang, Q., & Xie, J. (2011) · Journal of Marketing Research · doi:10.1509/jmkr.48.2.238 · Cited by 716
Elberse, A., & Eliashberg, J. (2003) · Marketing Science · doi:10.1287/mksc.22.3.329.17740 · Cited by 661
Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews
Pan, Y., & Zhang, J. Q. (2011) · Journal of Retailing · doi:10.1016/j.jretai.2011.05.002 · Cited by 613
The Firm's Management of Social Interactions
Godes, D., Mayzlin, D., Chen, Y., Das, S. R., Dellarocas, C. N., Pfeiffer, B. E., Libai, B., Sen, S. K., Shi, M., & Verlegh, P. W. J. (2005) · Marketing Letters · doi:10.1007/s11002-005-5902-4 · Cited by 583
Neglected Outcomes of Customer Satisfaction
Homburg, C., & Luo, X. (2006) · MADOC (University of Mannheim) · Cited by 500
Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box Office?
Vany, A. D., & Walls, W. D. (1999) · Journal of Cultural Economics · doi:10.1023/a:1007608125988 · Cited by 490
The Theory and Practice of Econometrics (2nd ed.).
Seaks, T. G., Judge, G. G., Griffiths, W., Hill, R. C., Lütkepohl, H., & Lee, T.-C. (1986) · Journal of the American Statistical Association · doi:10.2307/2289038 · Cited by 481
Kanouse, D. E., & Hanson, L. R. (1987) · Cited by 476
Communication Channels and Word of Mouth: How the Medium Shapes the Message
Berger, J. A., & Iyengar, R. (2013) · Journal of Consumer Research · doi:10.1086/671345 · Cited by 415
The early bird catches the news: Nine things you should know about micro-blogging
Kaplan, A., & Haenlein, M. (2010) · Business Horizons · doi:10.1016/j.bushor.2010.09.004 · Cited by 402
Introduction Strategy for New Products with Positive and Negative Word-of-Mouth
Mahajan, V., Muller, E., & Kerin, R. A. (1984) · Management Science · doi:10.1287/mnsc.30.12.1389 · Cited by 398
The Power of Stars: Do Star Actors Drive the Success of Movies?
Elberse, A. (2007) · Journal of Marketing · doi:10.1509/jmkg.71.4.102 · Cited by 374
Whose and what chatter matters? The effect of tweets on movie sales
Rui, H., Liu, Y., & Whinston, A. B. (2013) · Decision Support Systems · doi:10.1016/j.dss.2012.12.022 · Cited by 372
Film Critics: Influencers or Predictors?
Eliashberg, J., & Shugan, S. M. (1997) · Journal of Marketing · doi:10.2307/1251831 · Cited by 293
Levin, D. Z., Cross, R., & Abrams, L. C. (2002) · Academy of Management Proceedings · doi:10.5465/apbpp.2002.7517527 · Cited by 291
Chakravarty, A., Yong, L., & Mazumdar, T. (2010) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2010.04.001 · Cited by 238
Impact of star and movie buzz on motion picture distribution and box office revenue
Karniouchina, E. V. (2010) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2010.10.001 · Cited by 181
Conceptualizing and Measuring the Monetary Value of Brand Extensions: The Case of Motion Pictures
Hennig‐Thurau, T., Houston, M. B., & Heitjans, T. (2009) · Journal of Marketing · doi:10.1509/jmkg.73.6.167 · Cited by 148
Self-citationSilverScreener: A Modeling Approach to Movie Screens Management
Swami, S., Eliashberg, J., & Weinberg, C. B. (1999) · Marketing Science · doi:10.1287/mksc.18.3.352 · Cited by 147
Explaining Negativity Biases in Evaluation and Choice Behavior: Theory and Research
Kanouse, D. E. (1984) · ACR North American Advances · Cited by 146
Hennig‐Thurau, T. (2006) · Journal of the Academy of Marketing Science · doi:10.1177/0092070306286935 · Cited by 123
Self-citationAkdeniz, M. B., & Talay, M. B. (2013) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-013-0338-5 · Cited by 90
Adapting to succeed? Leveraging the brand equity of best sellers to succeed at the box office
Joshi, A., & Mao, H. (2010) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-010-0241-2 · Cited by 69
Why watching movie tweets won't tell the whole story?
Wong, F. M. F., Sen, S., & Chiang, M. (2012) · doi:10.1145/2342549.2342564 · Cited by 53
Playoff payoff: Super Bowl advertising for movies
Ho, J., Dhar, T., & Weinberg, C. B. (2009) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2009.06.001 · Cited by 50
Gemser, G., Leenders, M. A. A. M., & Weinberg, C. B. (2012) · Marketing Letters · doi:10.1007/s11002-012-9208-z · Cited by 21
Determinants of revenues in the motion picture industry
Kim, M. H. (2013) · Applied Economics Letters · doi:10.1080/13504851.2013.783678 · Cited by 16
Sales Patterns of Hit Music in Japan
Asai, S. (2009) · Journal of Media Economics · doi:10.1080/08997760902900106 · Cited by 13
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