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A forecasting system for movie attendance

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  • Marshall, Pablo
  • Dockendorff, Monika
  • Ibáñez, Soledad

Abstract

The main objective of this paper is to develop a system that uses historical data to forecast new movie attendance. In contrast to most models in the literature that consider aggregated prediction or the demand for a cross-section of movies, this paper analyzes the dynamic behavior of attendance at the movie level. The paper considers two alternative models for the weekly adoption or consumption of newly released movies. The Bass (1969) explains adoption through innovation and imitation effects. The Sawhney and Eliashberg (1996) model characterizes the adoption process through time-to-decide and time-to-act effects. The basis of the paper's results is a sample of 117 movies exhibited in Chile between 2001 and 2003. The two models present very similar results. For the Bass model, the innovation effect is greater than the imitation effect; but, in Sawhney and Eliashberg's model, the time-to-act is more significant than the time-to-decide. The sample prediction errors of these models present values between 2.7% and 17.1%, depending on the prediction horizon and the amount of historical data available.

Suggested Citation

  • Marshall, Pablo & Dockendorff, Monika & Ibáñez, Soledad, 2013. "A forecasting system for movie attendance," Journal of Business Research, Elsevier, vol. 66(10), pages 1800-1806.
  • Handle: RePEc:eee:jbrese:v:66:y:2013:i:10:p:1800-1806
    DOI: 10.1016/j.jbusres.2013.01.013
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    References listed on IDEAS

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    6. Fan, Zhi-Ping & Che, Yu-Jie & Chen, Zhen-Yu, 2017. "Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis," Journal of Business Research, Elsevier, vol. 74(C), pages 90-100.
    7. Lee, Youseok & Kim, Sang-Hoon & Cha, Kyoung Cheon, 2021. "Impact of online information on the diffusion of movies: Focusing on cultural differences," Journal of Business Research, Elsevier, vol. 130(C), pages 603-609.
    8. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.
    9. Björn Bohnenkamp & Ann-Kristin Knapp & Thorsten Hennig-Thurau & Ricarda Schauerte, 2015. "When does it make sense to do it again? An empirical investigation of contingency factors of movie remakes," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 39(1), pages 15-41, February.
    10. Hong, Jungsik & Koo, Hoonyoung & Kim, Taegu, 2016. "Easy, reliable method for mid-term demand forecasting based on the Bass model: A hybrid approach of NLS and OLS," European Journal of Operational Research, Elsevier, vol. 248(2), pages 681-690.
    11. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.
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