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Bayesian belief network for box-office performance: A case study on Korean movies

Cited 26 time in Web of Science Cited 36 time in Scopus
Authors
Lee, Kyung Jae; Chang, Woojin
Issue Date
2009-01
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
EXPERT SYSTEMS WITH APPLICATIONS; Vol.36 1; 280-291
Keywords
Bayesian belief networkDomain knowledgeCasual belief networkBox-office performance
Abstract
Due to their definition as experience goods with short product lifetime cycles, it is difficult to forecast the demand for motion pictures. Nevertheless, producers and distributors of new movies need to forecast box-office results in an attempt to reduce the uncertainty in the motion picture business. Previous research demonstrated the ability of certain movie attributes such as early box-office data and release season to forecast box-office revenues. However, no previous research has focused on the causal relationship among various movie attributes, which have the potential to increase the accuracy of box-office predictions. In this paper a Bayesian belief network (BBN), which is known as a causal belief network, is constructed to investigate the causal relationship among various movie attributes in the performance prediction of box-office success. Subsequently, sensitivity analysis is conducted to determine those attributes most critically related to box-office performance. Finally, the probability of a movie''''''''s box-office success is computed using the BBN model based on the domain knowledge from the value chain of theoretical motion pictures. The results confirm the improved forecasting accuracy of the BBN model compared to artificial neural network and decision tree. (C) 2007 Elsevier Ltd. All rights reserved.
ISSN
0957-4174
Language
English
URI
https://hdl.handle.net/10371/75368
DOI
https://doi.org/10.1016/j.eswa.2007.09.042
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Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
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