Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies

  • Thorsten Hennig-Thurau
  • Caroline Wiertz
  • Fabian Feldhaus
Original Empirical Research

Abstract

This research provides an empirical test of the “Twitter effect,” which postulates that microblogging word of mouth (MWOM) shared through Twitter and similar services affects early product adoption behaviors by immediately disseminating consumers’ post-purchase quality evaluations. This is a potentially crucial factor for the success of experiential media products and other products whose distribution strategy relies on a hyped release. Studying the four million MWOM messages sent via Twitter concerning 105 movies on their respective opening weekends, the authors find support for the Twitter effect and report evidence of a negativity bias. In a follow-up incident study of 600 Twitter users who decided not to see a movie based on negative MWOM, the authors shed additional light on the Twitter effect by investigating how consumers use MWOM information in their decision-making processes and describing MWOM’s defining characteristics. They use these insights to position MWOM in the word-of-mouth landscape, to identify future word-of-mouth research opportunities based on this conceptual positioning, and to develop managerial implications.

Keywords

Word of mouth communication Microblogging Twitter Early adoption Movies 

References

  1. Ahmad, I. (2013). 30+ of the most amazing Twitter statistics. Retrieved December 16, 2013, http://socialmediatoday.com/irfan-ahmad/1854311/twitter-statistics-IPO-infographic.
  2. Akdeniz, M. B., & Talay, M. B. (2013). Cultural variations in the use of market signals: a multilevel analysis of the motion picture industry. Journal of the Academy of Marketing Science, 41, 601–624.CrossRefGoogle Scholar
  3. Anamika S. (2009). Microblogging sites–40 Twitter like websites list. Retrieved January 19, 2012 http://anamikas.hubpages.com/hub/40-Microblogging-Sites-list-for-Communication-Twitter-Alternatives.
  4. Arndt, J. (1967). Role of product-related conversations in the diffusion of a new product. Journal of Marketing Research, 4(3), 291–295.CrossRefGoogle Scholar
  5. Asai, S. (2009). Sales patterns of hit music in Japan. Journal of Media Economics, 22, 81–101.CrossRefGoogle Scholar
  6. Asur, S., & Huberman, B. A. (2010). Predicting the future with social media. In Proceedings of the 2010 IEEE/WIC/ACM international conference on Web intelligence and intelligent agent technology, 1 (pp. 492–499).CrossRefGoogle Scholar
  7. Basuroy, S., Chatterjee, S., & Ravid, S. A. (2003). How critical are critical reviews? the box office effects of film critics, star power, and budgets. Journal of Marketing, 67(4), 103–117.CrossRefGoogle Scholar
  8. beevolve (2012). An exhaustive study of Twitter users across the world. Retrieved January 17, 2014, http://www.beevolve.com/twitter-statistics/.
  9. Bennett, S. (2013). Who uses Twitter? 16% of internet users, 18-29 year olds, minorities, men more than women [REPORT]. Retrieved January 17, 2014, http://www.mediabistro.com/alltwitter/twitter-demographics-2013_b36254.
  10. Berger, J. (2012). Word-of-mouth and interpersonal communication: an organizing framework and directions for future research. Working paper Google Scholar
  11. Berger, J., & Iyengar, R. (2013). Communication channels and word of mouth: How the medium shapes the message. Journal of Consumer Research, 40(3), 567–579.CrossRefGoogle Scholar
  12. Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. P. (2010). Measuring user influence in Twitter: The million follower fallacy. Proceedings of 4th International AAAI Conference on Weblogs and Social Media Google Scholar
  13. Chakravarty, A., Liu, Y., & Mazumdar, T. (2010). The differential effects of online word-of-mouth and critics’ reviews on pre-release movie evaluations. Journal of Interactive Marketing, 24, 185–197.CrossRefGoogle Scholar
  14. Chen, Y., Wang, Q., & Xie, J. (2011). Online social interactions: a natural experiment on word of mouth versus observational learning. Journal of Marketing Research, 48(April), 238–254.CrossRefGoogle Scholar
  15. Chevalier, J., & Mayzlin, D. (2006). The effect of word of mouth on sales: online book reviews. Journal of Marketing Research, 43(August), 345–354.CrossRefGoogle Scholar
  16. Corliss, R. (2009). ‘Bruno’: Did Twitter reviews hurt movie at box office? Retrieved August 17, 2010 from http://www.time.com/time/arts/article/0,8599,1910059,00.html.
  17. Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554–571.CrossRefGoogle Scholar
  18. Das, S. R., & Chen, M. Y. (2007). Yahoo! for amazon: sentiment extraction from small talk on the Web. Management Science, 53(9), 1375–1388.CrossRefGoogle Scholar
  19. Dennis, A. R., & Kinney, S. T. (1998). Testing media richness theory in the new media: the effects of cues, feedback, and task equivocality. Information Systems Research, 9(3), 256–274.CrossRefGoogle Scholar
  20. De Vany, A., & Walls, W. D. (1999). Uncertainty in the movie industry: does star power reduce the terror of the box office. Journal of Cultural Economics, 23, 285–318.CrossRefGoogle Scholar
  21. Elberse, A. (2007). The power of stars: do star actors drive the success of movies? Journal of Marketing, 71(4), 102–120.CrossRefGoogle Scholar
  22. Elberse, A., & Eliashberg, J. (2003). Demand and supply dynamics for sequentially released products in international markets: the case of motion pictures. Marketing Science, 22(3), 329–354.CrossRefGoogle Scholar
  23. Eliashberg, J., & Shugan, S. M. (1997). Film critics: influencers or predictors? Journal of Marketing, 61(April), 68–78.CrossRefGoogle Scholar
  24. Funt, P. (2012). Theater for Twits. The New York Times, January 8, p. SR2Google Scholar
  25. Gemser, G., Leenders, M. A. A. M., & Weinberg, C. W. (2012). More effective assessment of market performance in later stages of the product development process: the case of the motion picture industry. Marketing Letters, 23(4), 1019–1031.CrossRefGoogle Scholar
  26. Godes, D., & Mayzlin, D. (2004). Using online conversations to study word of mouth communications. Marketing Science, 23(4), 545–560.CrossRefGoogle Scholar
  27. Godes, D., Mayzlin, D., Chen, Y., Das, S., Dellarocas, C., Pfeiffer, B., et al. (2005). The firm’s management of social interactions. Marketing Letters, 16(3/4), 415–428.Google Scholar
  28. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explorations, 11(1).Google Scholar
  29. Hayes, D. (2002). Tentpoles fade fast after opening ad blast. Variety Magazine, 4, 2002.Google Scholar
  30. Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer opinion platforms: what motivates consumer to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38–52.CrossRefGoogle Scholar
  31. Hennig-Thurau, T., Houston, M. B., & Heitjans, T. (2009). Conceptualizing and measuring the monetary value of brand extensions: the case of motion pictures. Journal of Marketing, 73(November), 167–183.CrossRefGoogle Scholar
  32. Hennig-Thurau, T., Houston, M. B., & Walsh, G. (2006). The differing roles of success drivers across sequential channels: an application to the motion picture industry. Journal of the Academy of Marketing Science, 34(4), 559–575.CrossRefGoogle Scholar
  33. Ho, J. Y. C., Dhar, T., & Weinberg, C. B. (2009). Playoff payoff: super bowl advertising for movies. International Journal of Research in Marketing, 26(3), 168–179.CrossRefGoogle Scholar
  34. Joshi, A., & Mao, H. (2012). Adapting to succeed? leveraging the brand equity of best sellers to succeed at the box office. Journal of the Academy of Marketing Science, 40(4), 558–571.CrossRefGoogle Scholar
  35. Judge, G. G., Griffiths, W. E., Hill, R. C., Lütkepohl, H., & Lee, T.-C. (1985). The theory and practice of econometrics (2nd ed.). New York: Wiley.Google Scholar
  36. Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(March), 263–291.CrossRefGoogle Scholar
  37. Kanouse, D. E., & Hanson, L. R., Jr. (1972). Negativity in evaluations. In E. E. Jones, D. E. Kanouse, H. H. Kelley, R. E. Nisbett, S. Valins, & B. Weiner (Eds.), Attribution: perceiving the causes of behavior (pp. 47–62). Morristown: General Learning Press.Google Scholar
  38. Kanouse, D. E. (1984). Explaining negativity biases in evaluation and choice behavior: theory and research. Advances in Consumer Research, 11, 703–708.Google Scholar
  39. Kaplan, A. M., & Haenlein, M. (2011). The early bird catches the news: nine things you should know about micro-blogging. Business Horizons, 54, 105–113.CrossRefGoogle Scholar
  40. Karniouchina, E. V. (2011). Impact of star and movie buzz on motion picture distribution and box office revenue. International Journal of Research in Marketing, 28, 62–74.CrossRefGoogle Scholar
  41. Kassim, S. (2012). Twitter revolution: how the Arab Spring was helped by social media. Retrieved January 11, 2012 from http://www.policymic.com/articles/10642/twitter-revolution-how-the-arab-spring-was-helped-by-social-media.
  42. Kim, M. H. (2013). Determinants of revenues in the motion picture industry. Applied Economics Letters, 20(11), 1071–1075.CrossRefGoogle Scholar
  43. Kirmani, A., & Rao, A. R. (2000). No pain, no gain: a critical review of the literature on signaling unobservable product quality. Journal of Marketing, 64(2), 66–79.CrossRefGoogle Scholar
  44. Lang, B. (2010). Study: the ‘Twitter effect’ does not exist. Retrieved on October 12, 2011 from http://www.thewrap.com/media/column-post/thegrill-twitter-effect-myth-21035?page=0,0.
  45. Levin, D. Z., & Cross, R. (2004). The strength of weak ties you can trust: the mediating role of trust in effective knowledge transfer. Management Science, 50(11), 1477–1490.CrossRefGoogle Scholar
  46. Luo, X., & Homburg, C. (2007). Neglected outcomes of customer satisfaction. Journal of Marketing, 71(April), 133–149.CrossRefGoogle Scholar
  47. Mahajan, V., Muller, E., & Kerin, R. A. (1984). Introduction strategy for new products with positive and negative word-of-mouth. Management Science, 30(December), 1389–1404.CrossRefGoogle Scholar
  48. Max Planck Institute (2011). The Twitter project page at MPI-SWS. Retrieved on December 23, 2011 from http://twitter.mpi-sws.org/.
  49. McGee, M. (2012). Twitter: 60% of users access via mobile. Retrieved on January 22, 2013 from http://marketingland.com/twitter-60-percent-of-users-access-via-mobile-13626.
  50. Mills, S. (2012). How Twitter is winning the 2012 US election. Retrieved on January 11, 2012 from http://www.guardian.co.uk/commentisfree/2012/oct/16/twitter-winning-2012-us-election/.
  51. MPAA (2012). Theatrical market statistics 2012. Retrieved on January 17, 2014, from http://www.mpaa.org/resources/3037b7a4-58a2-4109-8012-58fca3abdf1b.pdf.
  52. Nakashima, R. (2012). ‘John Carter’: Movie to lose $200 million, among biggest Hollywood flops ever. Retrieved on January 15, 2013 from http://www.csmonitor.com/The-Culture/Latest-News-Wires/2012/0320/John-Carter-Movie-to-lose-200-million-among-biggest-Hollywood-flops-ever.
  53. Pan, Y., & Zhang, J. Q. (2011). Born unequal: a study of the helpfulness of user-generated product reviews. Journal of Retailing, 87(4), 598–612.CrossRefGoogle Scholar
  54. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2, 1–135.CrossRefGoogle Scholar
  55. Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. Proceedings of EMNLP. Retrieved on August 10, 2010 from http://www.cs.cornell.edu/home/llee/papers/sentiment.pdf.
  56. Parker, L. (2012). The digital revolution: how consumers are driving the future of games retail. Retrieved on January 7, 2012 from http://www.gamespot.com/features/the-digital-revolution-how-consumers-are-driving-the-future-of-games-retail-6396713/.
  57. PBS (2001). The monster that ate Hollywood. Retrieved on December 28, 2011 from http://www.pbs.org/wgbh/pages/frontline/shows/hollywood/
  58. Platt, J. C. (1999). Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. Burghes, & A. Smola (Eds.), Advances in Kernel methods - support vector learning (pp. 41–65). Cambridge: MIT Press.Google Scholar
  59. Pomerantz, D. (2009). Movie critics vs. the audience. Forbes Online. Retrieved January 21, 2014 at http://www.forbes.com/2009/08/27/movies-twitter-matt-atchity-business-entertainment-tomatoes.html.
  60. Rui, H., Liu, Y., & Whinston, A. (2013). Whose and what chatter matters? The effect of tweets on movie sales. Decision Support Systems, forthcoming.Google Scholar
  61. Singh, A. (2009a). Inglourious Basterds a box office hit ’thanks to the Twitter effect’, The Telegraph. Retrieved December 16, 2013, at http://www.telegraph.co.uk/news/celebritynews/6081711/Inglourious-Basterds-a-box-office-hit-thanks-to-the-Twitter-effect.html.
  62. Singh, T. (2009b). How Hollywood embraced social media. Retrieved on December 28, 2011 from http://www.ngonlinenews.com/article/hollywood-and-social-media.
  63. Smith, C. (2013). (December 2013) By the numbers: 68 amazing Twitter stats. Retrieved on January 17, 2014 from http://expandedramblings.com/index.php/march-2013-by-the-numbers-a-few-amazing-twitter-stats/#.UtlsfxCwbYg.
  64. Stradella Road (2010). Moviegoers 2010. Company report. Google Scholar
  65. Swami, S., Eliashberg, J., & Weinberg, C. B. (1999). Silverscreener: a modeling approach to movie screens management. Marketing Science, 18(3), 352–372.CrossRefGoogle Scholar
  66. Wong, F. M. F., Sen, S., & Chiang, M. (2012). Why watching movie tweets won’t tell the whole story? Proceedings of the WOSN Conference, 1–6.Google Scholar

Copyright information

© Academy of Marketing Science 2014

Authors and Affiliations

  • Thorsten Hennig-Thurau
    • 1
    • 2
  • Caroline Wiertz
    • 2
  • Fabian Feldhaus
    • 1
  1. 1.Marketing Center MuensterUniversity of MuensterMuensterGermany
  2. 2.Cass Business SchoolCity University LondonLondonUK

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