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

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.Eliashberg J, 2006, MARKET SCI, V25, P638, DOI 10.1287/mksc.1050.0177Sharda R, 2006, EXPERT SYST APPL, V30, P243, DOI 10.1016/j.eswa.2005.07.018Ainslie A, 2005, MARKET SCI, V24, P508, DOI 10.1287/mksc.1040.0106Chang BH, 2005, J MEDIA ECON, V18, P247*KOFIC, 2005, KOR MOT PICT IND STADe Vany AS, 2004, J ECON DYN CONTROL, V28, P1035, DOI 10.1016/S0165-1889(03)00065-4Antal P, 2004, ARTIF INTELL MED, V30, P257, DOI 10.1016/j.artmed.2003.11.007Elberse A, 2003, MARKET SCI, V22, P329YOO HS, 2002, KOREAN SOC JOURNALIS, V46, P183BAESENS B, 2002, P 16 INT C PATT REC, V3, P49CHENG J, 2001, LECT NOTES COMPUTER, V2056, P141Eliashberg J, 2000, MARKET SCI, V19, P226ZUFRYDEN FS, 2000, J ADVERTISING RE JAN, P55ARONSKY D, 2000, P AMIA S, P12Ravid SA, 1999, J BUS, V72, P463Swami S, 1999, MARKET SCI, V18, P352Neelamegham R, 1999, MARKET SCI, V18, P115BAGELLA M, 1999, J CULTURAL EC, V23, P237, DOI 10.1023/A:1007579421768JEDIDI K, 1998, MARKET LETT, V9, P393ALLBERT S, 1998, J CULTURAL EC, V22, P249LITMAN BR, 1998, MOTION PICTURE MEGA, P172Eliashberg J, 1997, J MARKETING, V61, P68Levin AM, 1997, ADV CONSUM RES, V24, P175Zufryden FS, 1996, J ADVERTISING RES, V36, P29JENSEN FV, 1996, INTRO BAYESIAN NETWOSawhney MS, 1996, MARKET SCI, V15, P113ELIASHBERG J, 1994, MANAGE SCI, V40, P1151PRAG J, 1994, J CULTURAL EC, V18, P217SCOHAY S, 1994, J MEDIA ECON, V7, P1JONES JM, 1991, INT J RES MARK, V8, P91WYATT J, 1991, CURRENT RES FILM AUD, V5, P86LITMAN BR, 1989, J MEDIA ECON, V2, P35MAHAJAN V, 1984, MANAGE SCI, V30, P1389LITMAN BR, 1983, J POP CULT, V16, P159