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

  • Original Empirical Research
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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.

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Notes

  1. We thank an anonymous reviewer for alerting us to this interesting development.

  2. The only neutral quality-related information that has traditionally been widely available for consumers at the release of a new product is expert reviews (such as movie reviews by professional movie critics). However, there is extensive empirical evidence that such reviews have limited informational value for consumers (Eliashberg and Shugan 1997).

  3. Technical issues caused by the Twitter application programming interface (API) lead to the exclusion of 12 additional movies; we were unable to collect all tweets for them on the release day.

  4. The resulting regression equation was as follows: advertising spending = 9,570.15 + 262.72 × production budget - .76 × production budget2, with R2 = .53. The coefficient was significant at p < .01.

  5. The resulting regression equation was as follows: MWOM volume = 321.25 + 22,546.59 × pre-release buzz, with R2 = .54. The coefficient was significant at p < .01.

  6. An analysis with the unadjusted MWOMVOL variable instead of the residuals from the auxiliary regression produced the same results. The only difference was that the VIFs for MWOMVOL and PRBUZZ were higher. We treated this result as support for the superiority of the used specification.

  7. Of the responses, 105 were too short or general and were thus not classified. Inter-coder agreement was 90.4% for the MWOM characteristics and 98% for the ways in which Twitter is used in the decision-making process. The two coders discussed each case in which they disagreed until they reached an agreement on the classification.

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Acknowledgments

The authors thank three anonymous JAMS reviewers, Andre Marchand and the participants of research seminars at Cass Business School, the University of Muenster, the University of Hamburg, the Technical University of Munich, HEC Paris, ESCP Paris, the Vrije Universiteit Amsterdam and the UCLA/Mallen Workshop in Motion Picture Industry Studies for their constructive criticism on previous versions of this article. They also thank Benno Stein and Peter Prettenhofer for their help with the WEKA analysis, Mo Musse and Peter Richards for their IT help, Chad Etzel from Twitter for supporting the data collection, and Arzzita Nash for help with the coding. Finally, the authors are grateful for research funds provided by Cass Business School and City University London that supported this project.

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Authors and Affiliations

  1. Marketing Center Muenster, University of Muenster, 48143, Muenster, Germany

    Thorsten Hennig-Thurau & Fabian Feldhaus

  2. Cass Business School, City University London, London, EC1Y 8TZ, UK

    Thorsten Hennig-Thurau & Caroline Wiertz

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  1. Thorsten Hennig-Thurau
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  2. Caroline Wiertz
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Correspondence to Caroline Wiertz.

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Appendix

Table 5 Post-hoc analyses of regression model with EWOM quality measures
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Hennig-Thurau, T., Wiertz, C. & Feldhaus, F. Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies. J. of the Acad. Mark. Sci. 43, 375–394 (2015). https://doi.org/10.1007/s11747-014-0388-3

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