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Devising a Practical Model for Predicting Theatrical Movie Success: Focusing on the Experience Good Property

Author

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  • Byeng-Hee Chang
  • Eyun-Jung Ki

Abstract

This study attempts to devise a new theoretical framework to classify and develop predictors of box office performance for theatrical movies. Three dependent variables including total box office, first-week box office, and length of run were adopted. Four categories of independent variables were employed: brand-related variables, objective features, information sources, and distribution-related variables. Sequel, actor, budget, genre (drama), Motion Picture Association of America rating (PG and R), release periods (Summer and Easter), and number of first-week screens were significantly related to total box office performance.

Suggested Citation

  • Byeng-Hee Chang & Eyun-Jung Ki, 2005. "Devising a Practical Model for Predicting Theatrical Movie Success: Focusing on the Experience Good Property," Journal of Media Economics, Taylor & Francis Journals, vol. 18(4), pages 247-269.
    • Handle: RePEc:taf:jmedec:v:18:y:2005:i:4:p:247-269
    DOI: 10.1207/s15327736me1804_2
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    Cited by:

    1. Nam, Kyungjin & Kim, Hye-jin, 2020. "The determinants of mobile game success in South Korea," Telecommunications Policy, Elsevier, vol. 44(2).
    2. Xingyao Xiao & Yihong Cheng & Jong-Min Kim, 2021. "Movie Title Keywords: A Text Mining and Exploratory Factor Analysis of Popular Movies in the United States and China," JRFM, MDPI, vol. 14(2), pages 1-19, February.
    3. Kim, Taegu & Hong, Jungsik & Kang, Pilsung, 2015. "Box office forecasting using machine learning algorithms based on SNS data," International Journal of Forecasting, Elsevier, vol. 31(2), pages 364-390.
    4. Legoux, Renaud & Larocque, Denis & Laporte, Sandra & Belmati, Soraya & Boquet, Thomas, 2016. "The effect of critical reviews on exhibitors' decisions: Do reviews affect the survival of a movie on screen?," International Journal of Research in Marketing, Elsevier, vol. 33(2), pages 357-374.
    5. Gazley, Aaron & Clark, Gemma & Sinha, Ashish, 2011. "Understanding preferences for motion pictures," Journal of Business Research, Elsevier, vol. 64(8), pages 854-861, August.
    6. Rafal Zbyrowski & Natalia Gmerek, 2016. "Pooled Modelling of the Product Life Cycle of Feature Films in Poland (Modelowanie panelowe cyklu zycia filmu kinowego w Polsce)," Research Reports, University of Warsaw, Faculty of Management, vol. 2(22), pages 185-193.
    7. Mathys, Juliane & Burmester, Alexa B. & Clement, Michel, 2016. "What drives the market popularity of celebrities? A longitudinal analysis of consumer interest in film stars," International Journal of Research in Marketing, Elsevier, vol. 33(2), pages 428-448.
    8. Hofmann, Julian & Clement, Michel & Völckner, Franziska & Hennig-Thurau, Thorsten, 2017. "Empirical generalizations on the impact of stars on the economic success of movies," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 442-461.
    9. Wang Qingshi & Li Naiqian & Hashmat Ali, 2019. "Prediction Model of Box Office Based on Arbitrage Pricing Theory: An Empirical Analysis from China," International Journal of Economics and Financial Issues, Econjournals, vol. 9(5), pages 16-23.
    10. Perano, Mirko & Casali, Gian Luca & Liu, Yulin & Abbate, Tindara, 2021. "Professional reviews as service: A mix method approach to assess the value of recommender systems in the entertainment industry," Technological Forecasting and Social Change, Elsevier, vol. 169(C).

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