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Modeling box office revenues of motion pictures✰

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  • Franses, Philip Hans

Abstract

Weekly box office revenues for motion pictures show a pattern where peak revenues often appear in the first week, and then new revenues slowly die out. This paper proposes a simple model to describe such box office revenues. The new model assumes that there are two types of adopters, with the first being the moviegoers who are aroused to go to a movie based on intrinsic motivation, possibly aroused by trailers, advertising and social media content, and a second type of moviegoers who enjoy shared consumption. A second key feature of the simple model, which involves basic logistic diffusion patterns, is that the first type starts adopting already before the launch of a movie, but can only go a movie when it is launched, while the second type starts to adopt right from the launch onwards. The sum of the two S-shaped diffusion processes only gets observed from the launch of a movie onwards. Parameter estimation turns out to be easy as is illustrated for forty top lifetime grosses (as per 2020) for the USA.

Suggested Citation

  • Franses, Philip Hans, 2021. "Modeling box office revenues of motion pictures✰," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
  • Handle: RePEc:eee:tefoso:v:169:y:2021:i:c:s0040162521002444
    DOI: 10.1016/j.techfore.2021.120812
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    1. John A. Norton & Frank M. Bass, 1987. "A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products," Management Science, INFORMS, vol. 33(9), pages 1069-1086, September.
    2. De Vany, Arthur & Walls, W David, 1996. "Bose-Einstein Dynamics and Adaptive Contracting in the Motion Picture Industry," Economic Journal, Royal Economic Society, vol. 106(439), pages 1493-1514, November.
    3. Jonathan Beck, 2007. "The sales effect of word of mouth: a model for creative goods and estimates for novels," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 31(1), pages 5-23, March.
    4. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415.
    5. W. D. Walls & Jordi McKenzie, 2020. "Black swan models for the entertainment industry with an application to the movie business," Empirical Economics, Springer, vol. 59(6), pages 3019-3032, December.
    6. Bae, Giwoong & Kim, Hye-jin, 2019. "The impact of movie titles on box office success," Journal of Business Research, Elsevier, vol. 103(C), pages 100-109.
    7. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    8. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    9. Shijie Lu & Xin (Shane) Wang & Neil Bendle, 2020. "Does Piracy Create Online Word of Mouth? An Empirical Analysis in the Movie Industry," Management Science, INFORMS, vol. 66(5), pages 2140-2162, May.
    10. Fildes, Robert & Kumar, V., 2002. "Telecommunications demand forecasting--a review," International Journal of Forecasting, Elsevier, vol. 18(4), pages 489-522.
    11. Thorsten Hennig-Thurau & Mark Houston & Shrihari Sridhar, 2006. "Can good marketing carry a bad product? Evidence from the motion picture industry," Marketing Letters, Springer, vol. 17(3), pages 205-219, July.
    12. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    13. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    14. Christophe Van den Bulte & Stefan Stremersch, 2004. "Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test," Marketing Science, INFORMS, vol. 23(4), pages 530-544, July.
    15. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    16. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    17. Andrew Ainslie & Xavier Drèze & Fred Zufryden, 2005. "Modeling Movie Life Cycles and Market Share," Marketing Science, INFORMS, vol. 24(3), pages 508-517, November.
    18. Daekook Kang & Yongtae Park, 2019. "Analysing diffusion pattern of mobile application services in Korea using the competitive Bass model and Herfindahl index," Applied Economics Letters, Taylor & Francis Journals, vol. 26(3), pages 222-230, February.
    19. Scaglione, Miriam & Giovannetti, Emanuele & Hamoudia, Mohsen, 2015. "The diffusion of mobile social networking: Exploring adoption externalities in four G7 countries," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1159-1170.
    20. Keeheon Lee & Shintae Kim & Chang Ouk Kim & Taeho Park, 2013. "An Agent-Based Competitive Product Diffusion Model for the Estimation and Sensitivity Analysis of Social Network Structure and Purchase Time Distribution," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(1), pages 1-3.
    21. Mohanbir S. Sawhney & Jehoshua Eliashberg, 1996. "A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures," Marketing Science, INFORMS, vol. 15(2), pages 113-131.
    22. Shin, Hyungsup & Jung, Jiyeon & Koo, Yoonmo, 2020. "Forecasting the video data traffic of 5 G services in south korea," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
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    More about this item

    Keywords

    New product diffusion; Motion pictures; Two types of adopters;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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