Skip to main content

How to Make a Successful Movie: Factor Analysis from both Financial and Critical Perspectives

  • Conference paper
  • First Online:
Book cover Information in Contemporary Society (iConference 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11420))

Included in the following conference series:

Abstract

Over the past twenty years, people have seen considerable growth in film industry. There are two common measurements for movie quality, financial metric of net profit and reception metric in the form of ratings assigned by moviegoers on websites. Researchers have utilized these two metrics to build models for movie success prediction separately, while few of them investigate the combination. Therefore, in this paper, we analyze movie success from perspectives of financial and critical metrics in tandem. Here, optimal success is defined as a film that is both profitable and highly acclaimed, while its worst outcome involves financial loss and critical panning at the same time. Salient features that are salient to both financial and critical outcomes are identified in an attempt to uncover what makes a “good” movie “good” and a “bad” one “bad” as well as explain common phenomenons in movie industry quantitatively.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    Box office mojo annual report: http://www.boxofficemojo.com/yearly/.

  2. 2.

    http://www.the-numbers.com/market/.

  3. 3.

    Please zoom in if the font size is too small to read.

References

  1. Armstrong, N., Yoon, K.: Movie rating prediction. Technical report. Citeseer (1995)

    Google Scholar 

  2. Benesty, J., Chen, Y., Huang, Y., Cohen, I.: Pearson correlation coefficient. In: Noise Reduction in Speech Processing. Springer Topics in Signal Processing, vol. 2, pp. 1–4. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00296-0_5

  3. Berg, J., Raddick, M.J.: First you get the money, then you get the reviews, then you get the internet comments: a quantitative examination of the relationship between critics, viewers, and box office success. Q. Rev. Film Video 34, 101–129 (2017)

    Article  Google Scholar 

  4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  5. Brown, A.L., Camerer, C.F., Lovallo, D.: To review or not to review? Limited strategic thinking at the movie box office. Am. Econ. J. Microecon. 4(2), 1–26 (2012)

    Article  Google Scholar 

  6. Ding, C., Cheng, H.K., Duan, Y., Jin, Y.: The power of the “like” button: the impact of social media on box office. Decis. Support Syst. 94, 77–84 (2017)

    Article  Google Scholar 

  7. Griffiths, T.: Gibbs sampling in the generative model of latent Dirichlet allocation (2002)

    Google Scholar 

  8. Karniouchina, E.V.: Impact of star and movie buzz on motion picture distribution and box office revenue. Int. J. Res. Mark. 28(1), 62–74 (2011)

    Article  Google Scholar 

  9. Lash, M., Fu, S., Wang, S., Zhao, K.: Early prediction of movie success — what, who, and when. In: Agarwal, N., Xu, K., Osgood, N. (eds.) SBP 2015. LNCS, vol. 9021, pp. 345–349. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16268-3_41

    Chapter  Google Scholar 

  10. Legoux, R., Larocque, D., Laporte, S., Belmati, S., Boquet, T.: The effect of critical reviews on exhibitors’ decisions: do reviews affect the survival of a movie on screen? Int. J. Res. Mark. 33(2), 357–374 (2016)

    Article  Google Scholar 

  11. Lehrer, S., Xie, T.: Box office buzz: does social media data steal the show from model uncertainty when forecasting for hollywood? Technical report, National Bureau of Economic Research (2016)

    Google Scholar 

  12. Liu, T., Ding, X., Chen, Y., Chen, H., Guo, M.: Predicting movie box-office revenues by exploiting large-scale social media content. Multimed. Tools Appl. 75(3), 1509–1528 (2016)

    Article  Google Scholar 

  13. Mestyán, M., Yasseri, T., Kertész, J.: Early prediction of movie box office success based on wikipedia activity big data. PloS One 8(8), e71226 (2013)

    Article  Google Scholar 

  14. Moon, S., Bergey, P.K., Iacobucci, D.: Dynamic effects among movie ratings, movie revenues, and viewer satisfaction. J. Mark. 74(1), 108–121 (2010)

    Article  Google Scholar 

  15. Oh, C., Roumani, Y., Nwankpa, J.K., Hu, H.-F.: Beyond likes and tweets: Consumer engagement behavior and movie box office in social media. Inf. Manag. (2016)

    Google Scholar 

  16. Pearson, K.: Liii on lines and planes of closest fit to systems of points in space. London, Edinburgh, Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901)

    Article  Google Scholar 

  17. Ravid, S.A.: J. Bus. 72(4), 463–492 (1999)

    Article  Google Scholar 

  18. Sharan, P.: Movie success predictor. Indian J. Appl. Res. 6(6) (2016)

    Google Scholar 

  19. Wang, H., Guo, K.: The impact of online reviews on exhibitor behaviour: evidence from movie industry. Enterp. Inf. Syst., 1–17 (2016)

    Google Scholar 

  20. Zhang, F., Yang, Y.: The effect of internet word-of-mouth on experience product sales—an empirical study based on film online reviews. Int. J. Bus. Adm. 7(2), 72 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Zheng Gao or Patrick Shih .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gao, Z., Malic, V., Ma, S., Shih, P. (2019). How to Make a Successful Movie: Factor Analysis from both Financial and Critical Perspectives. In: Taylor, N., Christian-Lamb, C., Martin, M., Nardi, B. (eds) Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science(), vol 11420. Springer, Cham. https://doi.org/10.1007/978-3-030-15742-5_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-15742-5_63

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15741-8

  • Online ISBN: 978-3-030-15742-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics