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


  • Byeng-Hee Chang
  • Eyun-Jung Ki


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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    References listed on IDEAS

    1. Jeffrey K. MacKie-Mason & Hal Varian, 1994. "Economic FAQs About the Internet," Journal of Economic Perspectives, American Economic Association, vol. 8(3), pages 75-96, Summer.
    2. Shenker, Scott & Clark, David & Estrin, Deborah & Herzog, Shai, 1996. "Pricing in computer networks: Reshaping the research agenda," Telecommunications Policy, Elsevier, vol. 20(3), pages 183-201, April.
    3. Bodnar & Peter Dilworth & Salvatore Iacono, Judith, 1988. "Cross-sectional analysis of residential telephone subscription in Canada," Information Economics and Policy, Elsevier, vol. 3(4), pages 355-378.
    4. MacKie-Mason, J.K. & Varian, H.L., 1993. "Some Economists of the Internet," Papers 93-16, Michigan - Center for Research on Economic & Social Theory.
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    Cited by:

    1. 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.
    2. Gazley, Aaron & Clark, Gemma & Sinha, Ashish, 2011. "Understanding preferences for motion pictures," Journal of Business Research, Elsevier, vol. 64(8), pages 854-861, August.

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