Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies
Open accessThe 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
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?
Positive word of mouth has little to no effect on consumer preferences, while negative word of mouth can have a negative effect on consumer preferences.
“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)
The Discussion and implications section reports that negative Twitter word of mouth affected early adoption of 105 movies, while positive word of mouth did not. That supports the claimed contrast for opening-weekend movie choices, but the study does not establish little or no effect of positive word of mouth on consumer preferences generally; the Why MWOM could affect early adoption section instead proposes that positive reviews increase preference. (LLM, conf=0.85)
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.
Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524 · see citing works
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 32,700 · see citing works
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 · see citing works
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 · see citing works
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957 · see citing works
Opinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
The theory and practice of econometrics
(1986) · Journal of Macroeconomics · doi:10.1016/0164-0704(86)90027-3 · Cited by 4,369 · see citing works
Attribution: Perceiving the Causes of Behavior
JONES, E. E. (1987) · Medical Entomology and Zoology · Cited by 3,775 · see citing works
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× · see citing works
“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 · see citing works
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 2,740 · see citing works
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 · see citing works
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 · see citing works
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 2,190 · see citing works
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 · see citing works
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 1,613 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 914 · see citing works
Film Critics: Influencers or Predictors?
Eliashberg, J., & Shugan, S. M. (1997) · Journal of Marketing · doi:10.1177/002224299706100205 · Cited by 884 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Elberse, A., & Eliashberg, J. (2003) · Marketing Science · doi:10.1287/mksc.22.3.329.17740 · Cited by 661 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Neglected Outcomes of Customer Satisfaction
Homburg, C., & Luo, X. (2006) · MADOC (University of Mannheim) · Cited by 487 · see citing works
Kanouse, D. E., & Hanson, L. R. (1987) · Cited by 483 · see citing works
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 · see citing works
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 · see citing works
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 384 · see citing works
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 379 · see citing works
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 · see citing works
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 · see citing works
Film Critics: Influencers or Predictors?
Eliashberg, J., & Shugan, S. M. (1997) · Journal of Marketing · doi:10.2307/1251831 · Cited by 293 · see citing works
Levin, D. Z., Cross, R., & Abrams, L. C. (2002) · Academy of Management Proceedings · doi:10.5465/apbpp.2002.7517527 · Cited by 288 · see citing works
Chakravarty, A., Yong, L., & Mazumdar, T. (2010) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2010.04.001 · Cited by 238 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Explaining Negativity Biases in Evaluation and Choice Behavior: Theory and Research
Kanouse, D. E. (1984) · ACR North American Advances · Cited by 147 · see citing works
Hennig‐Thurau, T. (2006) · Journal of the Academy of Marketing Science · doi:10.1177/0092070306286935 · Cited by 123 · see citing works
Self-citationAkdeniz, M. B., & Talay, M. B. (2013) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-013-0338-5 · Cited by 88 · see citing works
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 · see citing works
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 54 · see citing works
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 · see citing works
Gemser, G., Leenders, M. A. A. M., & Weinberg, C. B. (2012) · Marketing Letters · doi:10.1007/s11002-012-9208-z · Cited by 21 · see citing works
Determinants of revenues in the motion picture industry
Kim, M. H. (2013) · Applied Economics Letters · doi:10.1080/13504851.2013.783678 · Cited by 16 · see citing works
Sales Patterns of Hit Music in Japan
Asai, S. (2009) · Journal of Media Economics · doi:10.1080/08997760902900106 · Cited by 13 · see citing works
Flagged first, then by citations
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 · see citing works
“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 · see citing works
“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 · see citing works
Xia, L., Shin, H., & Burns, A. C. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.04.042 · Cited by 539 · see citing works
Chae, B. (. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.12.037 · Cited by 481 · see citing works
Rosario, A. B., Valck, K. D., & Sotgiu, F. (2019) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00706-1 · Cited by 517 · see citing works
Comparing automated text classification methods
Hartmann, J., Huppertz, J., Schamp, C., & Heitmann, M. (2019) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2018.09.009 · Cited by 399 · see citing works
Marchand, A., & Hennig‐Thurau, T. (2013) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2013.05.001 · Cited by 356 · see citing works
Self-citationSwani, K., Milne, G. R., Brown, B. P., Assaf, A. G., & Donthu, N. (2016) · Industrial Marketing Management · doi:10.1016/j.indmarman.2016.07.006 · Cited by 345 · see citing works
“…their customers – it enables marketers to influence brand outcomes and purchase decisions through fan promotion and popularization of brand content (De Vries, Gensler, and Leeflang, 2012; Kumar and Mirchandani, 2012; Hennig-Thurau, Wiertz, and Feldhaus, 2015; Nelson-Field, Riebe, and Sharp, 2012).”
BackgroundSocial media and the formation of organizational reputation
Etter, M., Ravasi, D., & Colleoni, E. (2019) · Academy of Management Review · doi:10.5465/amr.2014.0280 · Cited by 428 · see citing works
Vocal Minority and Silent Majority: How Do Online Ratings Reflect Population Perceptions of Quality1
Gao, G. G., Greenwood, B. N., Agarwal, R., & McCullough, J. S. (2015) · MIS Quarterly · doi:10.2139/ssrn.2629837 · Cited by 273 · see citing works
Unstructured data in marketing
Balducci, B., & Marinova, D. (2018) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-018-0581-x · Cited by 300 · see citing works
Hennig‐Thurau, T., Hofacker, C. F., & Bloching, B. (2013) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2013.09.005 · Cited by 236 · see citing works
Self-citationA multidisciplinary perspective of big data in management research
Sheng, J., Amankwah‐Amoah, J., & Wang, X. (2017) · International Journal of Production Economics · doi:10.1016/j.ijpe.2017.06.006 · Cited by 238 · see citing works
The Digital Divide Among Twitter Users and Its Implications for Social Research
Blank, G. (2016) · Social Science Computer Review · doi:10.1177/0894439316671698 · Cited by 225 · see citing works
“Commercial predictions are typically very short term, for example, opening weekend gross sales (Hennig-Thurau et al., 2015) or next day album sales (Frick et al., 2014).”
BackgroundFraccastoro, S., Gabrielsson, M., & Pullins, E. B. (2020) · International Business Review · doi:10.1016/j.ibusrev.2020.101776 · Cited by 263 · see citing works
Villarroel Ordenes, F., Ludwig, S., de Ruyter, K., Grewal, D., & Wetzels, M. (2017) · Journal of Consumer Research · doi:10.1093/jcr/ucw070 · Cited by 245 · see citing works
Shankar, V., Grewal, D., Sunder, S., Fossen, B. L., Peters, K., & Agarwal, A. (2021) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2021.09.005 · Cited by 205 · see citing works
Kumar, V., Choi, J., & Greene, M. (2016) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-016-0484-7 · Cited by 196 · see citing works
de Vries, L., Gensler, S., & Leeflang, P. S. H. (2017) · Journal of Marketing · doi:10.1509/jm.15.0178 · Cited by 260 · see citing works
“…Sethuraman, Tellis, and Briesch 2011; Srinivasan, Vanhuele, and Pauwels 2 / Journal of Marketing, September 2017 2010), C2C social messages (e.g., Hennig-Thurau, Wiertz, and Feldhaus 2015), and F2C social messages (e.g., Goh, Heng, and Lin 2013; Hutter et al. 2013; Kumar et al. 2016; Rishika et…”
BackgroundA Meta-Analysis of the Effects of Brands’ Owned Social Media on Social Media Engagement and Sales
Liadeli, G., Sotgiu, F., & Verlegh, P. W. J. (2022) · Journal of Marketing · doi:10.1177/00222429221123250 · Cited by 207 · see citing works
“Important distinctions between social networks (e.g., Instagram, Facebook) and microblogs (e.g., Twitter, Sina Weibo) are audience expectations and goals (e.g., Hennig-Thurau, Wiertz, and Feldhaus 2015; Marchand, Hennig-Thurau, and Wiertz 2017).”
BackgroundWhat Are Likes Worth? A Facebook Page Field Experiment
Mochon, D., Johnson, K., Schwartz, J., & Ariely, D. (2017) · Journal of Marketing Research · doi:10.1509/jmr.15.0409 · Cited by 162 · see citing works
“…(Godes and Mayzlin 2004; Stephen andGalak 2012), blogs (Dhar and Chang 2009;Onishi and Manchanda 2012; Stephen and Galak 2012), social networks (Hennig-Thurau, Wiertz, and Feldhaus 2014; Rui, Liu, and Whinston 2013; Trusov, Bucklin, and Pauwels 2009), review sites (Archak, Ghose, and Ipeirotis…”
BackgroundHansen, N., Kupfer, A.-K., & Hennig‐Thurau, T. (2018) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2018.08.001 · Cited by 164 · see citing works
“…systematic differences between traditional, face-to-face word of mouth and its digital forms, such as articulations on review/retail sites (consumer reviews or electronic word of mouth) and those on social media platforms (social media or microblogging word of mouth) (Hennig-Thurau et al., 2015).”
BackgroundSelf-citationOnline sentiment analysis in marketing research: a review
Rambocas, M., & Pacheco, B. G. (2018) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-05-2017-0030 · Cited by 170 · see citing works
Effects of the intensity of use of social media on brand equity
Stojanović, I., Andreu, L., & Pérez, R. C. (2018) · European Journal of Management and Business Economics · doi:10.1108/ejmbe-11-2017-0049 · Cited by 162 · see citing works
“While social media researchers have focussed primarily on analysing particular social networks in isolation, such as Twitter (Hennig-Thurau et al., 2015), Facebook (De Vries et al.”
BackgroundRabbanee, F. K., Roy, R., & Spence, M. T. (2020) · European Journal of Marketing · doi:10.1108/ejm-03-2018-0221 · Cited by 170 · see citing works
Marchand, A., Hennig‐Thurau, T., & Wiertz, C. (2016) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2016.09.003 · Cited by 153 · see citing works
Self-citationCasaló, L. V., Flavián, C., & Ibáñez‐Sánchez, S. (2017) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2016.0360 · Cited by 160 · see citing works
Exploring the effect of user engagement in online brand communities: Evidence from Twitter
Ibrahim, N. F., Wang, X., & Bourne, H. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.03.005 · Cited by 148 · see citing works
Song, T., Huang, J., Tan, Y., & Yu, Y. (2019) · Information Systems Research · doi:10.1287/isre.2018.0797 · Cited by 148 · see citing works
Dhaoui, C., & Webster, C. M. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.06.005 · Cited by 138 · see citing works
The power of the “like” button: The impact of social media on box office
Ding, C., Cheng, H. K., Duan, Y., & Jin, Y. J. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.11.002 · Cited by 133 · see citing works
Social media in marketing research: Theoretical bases, methodological aspects, and thematic focus
Li, F., Larimo, J. A., & Leonidou, L. C. (2022) · Psychology and Marketing · doi:10.1002/mar.21746 · Cited by 130 · see citing works
Marketing ecosystem: An outside-in view for sustainable advantage
Zhang, J. Z., & Watson, G. F. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2020.04.023 · Cited by 134 · see citing works
How does brand-related user-generated content differ across social media? Evidence reloaded
Roma, P., & Aloini, D. (2018) · Journal of Business Research · doi:10.1016/j.jbusres.2018.11.055 · Cited by 132 · see citing works
Liu, X. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.02.021 · Cited by 140 · see citing works
Qualitative research in marketing: what can academics do better?
Crick, J. M. (2021) · Journal of Strategic Marketing · doi:10.1080/0965254x.2020.1743738 · Cited by 108 · see citing works
“If scholars choose to manually-code their data, they are recommended to use a robust system, in which they code their key and sub-themes, such as using numbers, letters, or colours – whatever is clearest for them (Hennig-Thurau et al., 2015; Miles & Huberman, 1994; Walker et al., 2017).”
MethodSantana, A. A., & Gil, S. M. (2017) · Journal of Destination Marketing & Management · doi:10.1016/j.jdmm.2017.02.003 · Cited by 121 · see citing works
Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?
Kübler, R. V., Colicev, A., & Pauwels, K. H. (2019) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2019.08.001 · Cited by 121 · see citing works
Shen, Z. (2021) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-10-2019-0161 · Cited by 116 · see citing works
“Thus, eWOM is defined as “any positive or negative statement is made available to a multitude of people and institutions via the Internet” (Hennig et al., 2015, p. 39).”
BackgroundAleti, T., Pallant, J., Tuan, A., & van Laer, T. (2019) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2019.03.003 · Cited by 97 · see citing works
Creators, multipliers, and lurkers: who contributes and who benefits at online review sites
Munzel, A., & Kunz, W. H. (2014) · Journal of service management · doi:10.1108/josm-04-2013-0115 · Cited by 101 · see citing works
Vilnai‐Yavetz, I., & Levina, O. A. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.10.034 · Cited by 99 · see citing works
“Extrinsic and Intrinsic Motives for Sharing E-Business Content: Self-Reports versus Experimental Findings As noted earlier, previous findings (Hennig-Thurau et al., 2014) support the notion that when share buttons for e-business content are available, some proportion of users will click them.”
BackgroundResultInfluentialSay what? How the interplay of tweet readability and brand hedonism affects consumer engagement
Davis, S. W., Horváth, C., Gretry, A., & Belei, N. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.01.071 · Cited by 99 · see citing works
Battle of the Brand Fans: Impact of Brand Attack and Defense on Social Media
Ilhan, B. E., Kübler, R. V., & Pauwels, K. H. (2018) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2018.01.003 · Cited by 97 · see citing works
Online to Offline: The Impact of Social Media on Offline Sales in the Automobile Industry
Wang, Y.-Y., Guo, C., Susarla, A., & Sambamurthy, V. (2017) · Information Systems Research · doi:10.1287/isre.2020.0984 · Cited by 96 · see citing works
Fetais, A. H. M. A., Algharabat, R., Aljafari, A. M., & Rana, N. P. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10264-7 · Cited by 95 · see citing works
Online Review Helpfulness and Firms’ Financial Performance: An Empirical Study in a Service Industry
Mariani, M. M., & Borghi, M. (2020) · International Journal of Electronic Commerce · doi:10.1080/10864415.2020.1806464 · Cited by 90 · see citing works
“The latter ones enable potential, actual, or former consumers to articulate and share their opinions and perceptions about products, services, experiences, brands, and companies on the Internet [62].”
BackgroundInfluentialWu, L., Dodoo, N. A., Wen, T. J., & Li, K. (2021) · International Journal of Advertising · doi:10.1080/02650487.2021.1920218 · Cited by 88 · see citing works
#IHateYourBrand: adaptive service recovery strategies on Twitter
Abney, A. K., Pelletier, M. J., Ford, T.-R. S., & Horky, A. B. (2017) · Journal of Services Marketing · doi:10.1108/jsm-02-2016-0079 · Cited by 85 · see citing works
Governing social media: the emergence of hybridised boundary objects
Arnaboldi, M., AZZONE, G. F., & Sidorova, Y. (2017) · Accounting Auditing & Accountability Journal · doi:10.1108/aaaj-07-2015-2132 · Cited by 87 · see citing works
“Social media started to spread through personal use, but now have become an instrument at business level (Richardson, 2009; Jin, 2012; Chikandiwa et al., 2013; Guinan et al., 2014), and was first used in the fields of communication and marketing (Gopinath et al., 2014; Hennig-Thurau et al., 2015).”
BackgroundMethodInfluentialSchivinski, B., Muntinga, D. G., Pontes, H. M., & Łukasik, P. (2019) · Journal of Strategic Marketing · doi:10.1080/0965254x.2019.1572641 · Cited by 84 · see citing works
The effect of social media interactions on customer relationship management
Maecker, O., Barrot, C., & Becker, J. U. (2016) · BuR - Business Research · doi:10.1007/s40685-016-0027-6 · Cited by 83 · see citing works
“For instance, consumers can like, rate, review, or comment on the brand (Hennig-Thurau et al. 2004, 2015).”
Bartschat, M., Cziehso, G. P., & Hennig‐Thurau, T. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.11.035 · Cited by 81 · see citing works
Self-citationOnline fashion shopping paradox: The role of customer reviews and facebook marketing
Kawaf, F., & Istanbulluoglu, D. (2019) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2019.02.017 · Cited by 80 · see citing works
Dimitriu, R., & Guesalaga, R. (2017) · Psychology and Marketing · doi:10.1002/mar.21007 · Cited by 79 · see citing works
Hussain, S., Song, X., & Niu, B. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.03055 · Cited by 98 · see citing works
The impact of online movie word-of-mouth on consumer choice
Chiu, Y.-L., Chen, K., Wang, J., & Hsu, Y. (2019) · International Marketing Review · doi:10.1108/imr-06-2018-0190 · Cited by 86 · see citing works
Consumer-based strategy: using multiple methods to generate consumer insights that inform strategy
Hamilton, R. W. (2016) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-016-0476-7 · Cited by 77 · see citing works
“Observational data (such as publicly available online data; Hennig-Thurau et al. 2015; Thompson et al. 2015), experiments (Heidenreich et al. 2015; Xie et al. 2015), surveys (Brocato et al. 2015; Gallan et al. 2013), customer purchase data (Carter and Curry 2013; Papies et al. 2011), and…”
MethodMarolt, M., Zimmermann, H., Žnidaršič, A., & Pucihar, A. (2020) · Journal of theoretical and applied electronic commerce research · doi:10.4067/s0718-18762020000200104 · Cited by 77 · see citing works
“In this context, relationships with customers became more challenging to control and manage [49].”
BackgroundTechnology in the 21st century: New challenges and opportunities
Sheng, J., Amankwah‐Amoah, J., & Wang, X. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.06.009 · Cited by 73 · see citing works
Making sense of consumers’ tweets
Pantano, E., Giglio, S., & Dennis, C. (2018) · International Journal of Retail & Distribution Management · doi:10.1108/ijrdm-07-2018-0127 · Cited by 73 · see citing works
A roadmap for driving customer word-of-mouth
Keiningham, T. L., Rust, R. T., Larivière, B., Aksoy, L., & Williams, L. (2018) · Journal of service management · doi:10.1108/josm-03-2017-0077 · Cited by 72 · see citing works
Cheng, M., Liu, J., Qi, J., & Wan, F. (2021) · Industrial Marketing Management · doi:10.1016/j.indmarman.2021.07.001 · Cited by 73 · see citing works
Empirical generalizations on the impact of stars on the economic success of movies
Hofmann, J., Clément, M., Völckner, F., & Hennig‐Thurau, T. (2016) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2016.08.006 · Cited by 69 · see citing works
Self-citationTopaloglu, O., & Dass, M. (2019) · Decision Sciences · doi:10.1111/deci.12378 · Cited by 70 · see citing works
The economics of movies (revisited): A survey of recent literature
McKenzie, J. (2022) · Journal of Economic Surveys · doi:10.1111/joes.12498 · Cited by 69 · see citing works
Debates and assumptions about motion picture performance: a meta-analysis
Carrillat, F. A., Legoux, R., & Hadida, A. L. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0561-6 · Cited by 76 · see citing works
“…likely apply to other experiential products, such as books, music, video games, live shows, fine dining, travels, or education (e.g., Eliashberg and Sawhney 1994; Hennig-Thurau et al. 2015), for which ingredient brands, opinions of experts, and reviews by other consumers provide important cues.”
BackgroundArtificial intelligence and empirical consumer research: A topic modeling analysis
Vaid, S., Puntoni, S., & Khodr, A. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114110 · Cited by 71 · see citing works
Harnessing stakeholder input on Twitter: A case study of short breaks in Spanish tourist cities
Bigné, E., Oltra, E., & Andreu, L. (2019) · Tourism Management · doi:10.1016/j.tourman.2018.10.013 · Cited by 56 · see citing works
Attitude toward social media reviews and restaurant visit intention: a Bangladeshi perspective
Popy, N. N., & Bappy, T. A. (2020) · South Asian Journal of Business Studies · doi:10.1108/sajbs-03-2020-0077 · Cited by 73 · see citing works
Effects of online brand communities on millennials' brand loyalty in the fashion industry
Ozuem, W., Willis, M., Howell, K. E., Helal, G., Ranfagni, S., & Lancaster, G. (2021) · Psychology and Marketing · doi:10.1002/mar.21466 · Cited by 66 · see citing works
The bright and dark sides of humorous response to online customer complaint
Shin, H., & Larson, L. R. L. (2020) · European Journal of Marketing · doi:10.1108/ejm-08-2018-0522 · Cited by 65 · see citing works
Talk Bursts: The Role of Spikes in Prerelease Word-of-Mouth Dynamics
Gelper, S., Peres, R., & Eliashberg, J. (2018) · Journal of Marketing Research · doi:10.1177/0022243718817007 · Cited by 60 · see citing works
Creating identification with brand communities on Twitter
López, M., Piñero, M. S., & Moyeda-Carabaza, A. A. (2017) · Internet Research · doi:10.1108/intr-12-2013-0258 · Cited by 60 · see citing works
Jung, S. H., & Jeong, Y. J. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101409 · Cited by 67 · see citing works
Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales
Yu, Y., Yang, Y., Huang, J., & Tan, Y. (2023) · MIS Quarterly · doi:10.25300/misq/2022/16600 · Cited by 55 · see citing works
“Consumers tend to search the review for diagnostic information that they have not considered (Hennig-Thurau et al. 2015), which requires a higher level of cognitive effort (Yin et al.”
InfluentialZhao, K., Zhang, P., & Lee, H.-M. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113684 · Cited by 58 · see citing works
Huang, V., & Yeo, T. E. D. (2017) · Internet Research · doi:10.1108/intr-08-2016-0248 · Cited by 55 · see citing works
Making new products go viral and succeed
Nguyen, H. T., & Chaudhuri, M. (2018) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2018.09.007 · Cited by 61 · see citing works
Bazi, S., Hajli, A., Hajli, N., Shanmugam, M., & Lin, X. (2019) · Information Technology and People · doi:10.1108/itp-09-2018-0415 · Cited by 58 · see citing works
The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets
Xu, P., Dredze, M., & Broniatowski, D. A. (2020) · Journal of Medical Internet Research · doi:10.2196/21499 · Cited by 53 · see citing works
Strategic Decision Making in the Digital Age: Expert Sentiment and Corporate Capital Allocation
Nauhaus, S., Luger, J., & Raisch, S. (2021) · Journal of Management Studies · doi:10.1111/joms.12742 · Cited by 56 · see citing works
Self-presentation, privacy and electronic word-of-mouth in social media
Pasternak, O., Veloutsou, C. A., & Morgan‐Thomas, A. (2017) · Journal of Product & Brand Management · doi:10.1108/jpbm-04-2016-1150 · Cited by 56 · see citing works
Data properties and the performance of sentiment classification for electronic commerce applications
Choi, Y. T., & Lee, H. (2017) · Information Systems Frontiers · doi:10.1007/s10796-017-9741-7 · Cited by 53 · see citing works
Iyer, P., Yazdanparast, A., & Strutton, D. R. (2017) · Journal of Consumer Marketing · doi:10.1108/jcm-11-2015-1605 · Cited by 53 · see citing works
Predicting information diffusion on Twitter – Analysis of predictive features
Hoang, T. B. N., & Mothe, J. (2017) · Journal of Computational Science · doi:10.1016/j.jocs.2017.10.010 · Cited by 53 · see citing works
“Other examples could be found in marketing [2].”
BackgroundHouston, M. B., Kupfer, A.-K., Hennig‐Thurau, T., & Spann, M. (2018) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0572-3 · Cited by 51 · see citing works
Self-citationMai, E. S., & Liao, Y. (2021) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-05-2021-0269 · Cited by 50 · see citing works
Ryoo, J. H., Wang, X., & Lu, S. (2020) · Journal of Marketing · doi:10.1177/0022242920937703 · Cited by 49 · see citing works
Ruiz‐Equihua, D., Romero, J., & Casaló, L. V. (2019) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2019.1630698 · Cited by 48 · see citing works
Azer, J., & Ranaweera, C. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.068 · Cited by 51 · see citing works
Bernabé-Moreno, J., Tejeda-Lorente, Á., Porcel, C., Fujita, H., & Herrera‐Viedma, E. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.12.033 · Cited by 48 · see citing works
A Temporal Study of the Effects of Online Opinions: Information Sources Matter
Huang, J., Boh, W. F., & Goh, K. H. (2017) · Journal of Management Information Systems · doi:10.1080/07421222.2017.1394079 · Cited by 47 · see citing works
To buy or not to buy: how young consumers approach new smart products in the social media context
Guan, J., Lau, Y., Yang, H., & Ren, L. (2021) · Young Consumers Insight and Ideas for Responsible Marketers · doi:10.1108/yc-12-2020-1272 · Cited by 46 · see citing works
Izogo, E., Mpinganjira, M., Karjaluoto, H., & Liu, H.-F. (2021) · Journal of Travel Research · doi:10.1177/00472875211050420 · Cited by 41 · see citing works
Developing effective social media messages: Insights from an exploratory study of industry experts
Majid, S., López, C., Megicks, P., & Lim, W. M. (2019) · Psychology and Marketing · doi:10.1002/mar.21196 · Cited by 49 · see citing works
Trains and Twitter: Firm generated content, consumer relationship management and message framing
Nisar, T. M., & Prabhakar, G. P. (2018) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2018.04.026 · Cited by 45 · see citing works
Shan, W., Wang, J., Shi, X., & Evans, R. D. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114404 · Cited by 43 · see citing works
Seeking the Support of the Silent Majority: Are Lurkers Valuable to the UGC Platform?
Chen, X., Li, X., Yao, D., & Zhou, Z. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-018-00624-8 · Cited by 45 · see citing works
- 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 · 2013: 7 · 2012: 1
- 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 › Sociology and Political Science › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior and Marketing Influence
- Physical Sciences › Computer Science › Computer Networks and Communications › 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
