Skip to main content

How Important Is It to Get Movie-Goers Onside?

  • Chapter
  • First Online:
Predicting Movie Success at the Box Office
  • 928 Accesses

Abstract

The importance of audiences’ reactions to new movies has become all the more critical in the digital era in which many take to online micro-blogging and social networking sites to voice their opinions. In these environments, individual cinema-goers can enjoy readerships for their reviews that are every bit as big as those of the best-known professional critics. Opinions can also spread very rapidly or virally in the digital world. Negative opinions might therefore spread quickly enough when voiced by the initial audiences of a new movie to stymie its box office performance. The movie industry will therefore need to engage with this world of amateur reviewers to influence the nature and flow of opinion and develop promotional strategies that can respond quickly to any negativity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.00
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Apala, K. R., Jose, M., Motnam, S., Chan, C.-C., Liszka, K. J., & de Gregorio, F. (2013). Prediction of movies box office performance using social media. In IEEE/AM International Conference on Advances in Social Networks Analysis and Mining. Available at: http://dl.acm.org/citation.cfm?id=2500232

  • Asur, S., & Huberman, B. A. (2010). Predicting the future with social media. Available at: http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf. Accessed 16th August 2016.

  • Bhave, A., Kulkarni, H., Biramane, V., & Kosamkar, P. (2015). Role of different factors in predicting movie success. In Proceedings, International Conference on Pervasive Computing (pp. 1–4). IEEE.

    Google Scholar 

  • Bourdieu, P. (1984). Distinction: A Social Critique of the Judgement of Taste. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Bourdieu, P. (1985). The social space of the genesis of groups. Theory and Society, 14(6), 723–744.

    Article  Google Scholar 

  • Bourdieu, P., & Wacquant, L. J. D. (1992). An invitation to reflexive sociology. Chicago, IL: University of Chicago Press.

    Google Scholar 

  • Chen, Y., Fay, S., & Wang, Q. (2004). Marketing implications of online consumer product reviews (Working Paper). Department of Marketing, University of Florida.

    Google Scholar 

  • Chen, Y., Wu, S.-Y., & Yoon, J. (2004). The impact of online recommendations and consumer feedback on sales. In Proceedings of the International Conference on Information Systems (pp. 711–724). ICS.

    Google Scholar 

  • Chen, Y., & Xie, J. (2005). Online consumer review: Word-of-mouth as a new element of marketing communications mix. Management Science, 54(3), 477–491.

    Article  Google Scholar 

  • Chevalier, J., & Mayzlin, D. (2006). The effect of WOM on sales: Online book reviews. Journal of Marketing Research, 43(August), 345–354.

    Article  Google Scholar 

  • Chiang, I., Wen, Y.-F., Luo, Y.-C., Li, M.-C., & Hsu, C.-Y. (2014). Using text mining technique to analyse how movie forums affect the box office. International Journal of Electronic Commerce, 5(1). Available at: https://www.questia.com/library/journal/1P3-3389834701/using-text-mining-techniques-to-analyze-how-movie

  • Chintagunta, P. K., Gopinath, S., & Venkatarman, S. (2010). The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.

    Article  Google Scholar 

  • Craig, C. S., Greene, W. H., & Versaci, A. (2015). E-word of mouth: Early predictor of audience engagement – How pre-release “E-WOM” drives box-office outcomes of movies. Journal of Advertising Research, 55(1), 62–72. Available at: http://cn.cnstudiodev.com/uploads/document_attachment/attachment/654/jar_buzz_predicts_movie_success_feb2015.pdf. Accessed 19th August 2016.

  • Dellarocas, C., Awad, N., & Zhang, M. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing, 21(40), 23–45.

    Article  Google Scholar 

  • Dellarocas, C., Narayan, R., & Smith, R. H. (2006). What motivates consumers to review a product online? A study of the product-specific antecedents of online movie reviews. Available at: http://ebusiness.mit.edu/wise2006/papers/2B-3_FinalWISE2006abstract-dell-narayan.pdf. Accessed 12th August 2016.

  • Desai, K. K., & Basuroy, S. (2005). Interactive influence of genre familiarity, star power, and critics’ reviews in the cultural goods industry: The case of motion pictures. Psychology & Marketing, 22, 203–223.

    Article  Google Scholar 

  • Dhar, V., & Chang, E. (2009). Does chatter matter? The impact of user-generated content on music sales. Journal of Interactive Marketing, 23(November), 300–307.

    Article  Google Scholar 

  • Duan, W., Gu, B., & Whinston, A. B. (2005). Do online reviews matter? An empirical investigation of panel data. Department of Management Science and Information Systems, University of Texas at Austin. Available at: https://www.researchgate.net/publication/220196606_Do_Online_Reviews_Matter_-_An_Empirical_Investigation_of_Panel_Data

  • Duan, W., Gu, B., & Whinston, A. B. (2008). The dynamics of online word-of-mouth and product sales – An empirical investigation of the movie industry. Journal of Retailing, 84(2), 233–242.

    Article  Google Scholar 

  • 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), 68–78.

    Article  Google Scholar 

  • Eliashberg, J., & Shugan, S. M. (1997). Film critics: Influencers or predictors? Journal of Marketing, 61(2), 68–78.

    Article  Google Scholar 

  • Gmerek, N. (2015). The determinants of polish movies’ box office performance in Poland. Journal of Marketing and Consumer Behaviour in Emerging Markets, 1(1), 15–35.

    Google Scholar 

  • Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545.

    Article  Google Scholar 

  • Holbrook, M. B. (1999). Popular appeal versus expert judgments of motion pictures. Journal of Consumer Research, 26, 144–155.

    Article  Google Scholar 

  • Holbrook, M. B., & Addis, M. (2008). Art versus commerce in the movie industry: A two-path model of motion picture success. Journal of Cultural Economics, 32, 87–107.

    Article  Google Scholar 

  • Holt, D. B. (1998). Does cultural capital structure American consumption? Journal of Consumer Research, 25(1), 1–25.

    Article  Google Scholar 

  • Hon, L. Y. (2014). Experts versus audience’s opinion at the movies: Evidence from North American box office. Marketing Bulletin, 25, Article 1. Available at: http://marketing-bulletin.massey.ac.nz/V25/MB_V25_A1_Hon_FINAL.pdf

  • Jain, V. (2013). Prediction of movie success using sentiment analysis of tweets. International Journal of Soft Computing and Software Engineering, 3, 308–313.

    Google Scholar 

  • Lassner, R. (1944). Sex and age determinants of theatre and movie interests. Journal of General Psychology, 31, 241–271.

    Article  Google Scholar 

  • Lazarsfeld, P. (1947). Audience research in the movie field. Annals of the American Academy of Political and Social Sciences, 254, 160–168.

    Article  Google Scholar 

  • Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70, 74–89.

    Article  Google Scholar 

  • Mestyan, M., Yasseri, T., & Kertesz, J. (2013). Early prediction of movie box office successes based on Wikipedia activity big data. PLoS ONE, 8(8), e71226.

    Article  Google Scholar 

  • Neelamegham, R., & Chintagunta, A. (1999). A Bayesian model to forecast new product performance in domestic and international markets. Marketing Science, 18(2), 115–136.

    Article  Google Scholar 

  • Panaligan, R., & Chen, A. (2013). Quantifying movie magic with Google Search (White paper at Google Think). Available at: http://www.google.com.au/think/research-studies/quantifying-movie-magic.html

  • Plucker, J. A., Kaufman, J. C., Temple, J. S., & Qian, M. (2009). Do experts and novices evaluate movies the same way? Psychology & Marketing, 26(5), 397–478.

    Article  Google Scholar 

  • Ravid, S. A., & Sarig, O. (1991). Dividend policy and capital structure: An optimal choice of combined signal. Journal of Financial and Quantitative Analysis, 26, 165–180.

    Article  Google Scholar 

  • Reinstein, D. A., & Snyder, C. M. (2005). The influence of expert reviews on consumer demand for experience goods: A case study of movie critics. Journal of Industry Economics, 1, 27–51.

    Article  Google Scholar 

  • Sharda, R., & Delen, D. (2006). Predicting box office success of motion pictures with neutral networks. Expert Systems with Application, 30(2), 243–254, 277.

    Google Scholar 

  • Turner, R., & Emshwiller, J. R. (1993). Movie-research Czar is said by some to sell manipulated findings. The Wall Street Journal, 17(December), A1.

    Google Scholar 

  • Yeung, K. (2013, June 6). Google: Search hits, YouTube views are key predictors of a movie’s box office performance. The Next Web. Available at: https://thenextweb.com/google/2013/06/06/google-search-hits-youtube-views-are-key-predictors-of-a-movies-box-office-performance/#.tnw_XLUXX91j

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2018 The Author(s)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gunter, B. (2018). How Important Is It to Get Movie-Goers Onside?. In: Predicting Movie Success at the Box Office. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-71803-3_14

Download citation

Publish with us

Policies and ethics