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.
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