Table of Contents Author Guidelines Submit a Manuscript
Table of Contents Alerts

To receive news and publication updates for Computational Intelligence and Neuroscience, enter your email address in the box below.

Computational Intelligence and Neuroscience
Volume 2017, Article ID 4315419, 16 pages
https://doi.org/10.1155/2017/4315419
Research Article

Box Office Forecasting considering Competitive Environment and Word-of-Mouth in Social Networks: A Case Study of Korean Film Market

Taegu Kim,1 Jungsik Hong,2 and Pilsung Kang3

1Department of Industrial and Management Engineering, Hanbat National University, Daejeon, Republic of Korea
2Department of Industrial and Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea
3School of Industrial Management Engineering, Korea University, Seoul, Republic of Korea

Correspondence should be addressed to Pilsung Kang; rk.ca.aerok@gnak_gnuslip

Received 20 February 2017; Accepted 18 June 2017; Published 27 July 2017

Academic Editor: Sandhya Samarasinghe

Copyright © 2017 Taegu Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Eliashberg, J.-J. Jonker, M. S. Sawhney, and B. Wierenga, “MOVIEMOD: An implementable decision-support system for prerelease market evaluation of motion pictures,” Marketing Science, vol. 19, no. 3, pp. 226–243, 2000. View at Publisher · View at Google Scholar · View at Scopus
  2. F. Wang, R. Cai, and M. Huang, “Forecasting movie-going behavior based on online pre-release WOM and opening strength,” in Proceedings of the 2nd International Workshop on Intelligent Systems and Applications, (ISA'10), Wuhan, China, May 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. Motion Picture Association of America, Theatrical Market Statistics, 2012.
  4. Pricewaterhouse Coopers, “PwC global media outlook: 2013–2017,” New York, 2013.
  5. S. Shugan, Service Strategy, Richard D. Irwn, Homewood, Il, USA, 1995.
  6. F. Wang, Y. Zhang, X. Li, and H. Zhu, “Why do moviegoers go to the theater? the role of prerelease media publicity and online word of mouth in driving moviegoing behavior,” Journal of Interactive Advertising, vol. 11, no. 1, pp. 50–62, 2010. View at Publisher · View at Google Scholar
  7. J. Eliashberg, S. K. Hui, and Z. J. Zhang, “From story line to box office: A new approach for green-lighting movie scripts,” Management Science, vol. 53, no. 6, pp. 881–893, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Eliashberg and M. S. Sawhney, “Modeling goes to hollywood: predicting individual differences in movie enjoyment,” Management Science, vol. 40, no. 9, pp. 1151–1173, 1994. View at Publisher · View at Google Scholar
  9. M. B. Holbrook and E. C. Hirschman, “The experiential aspects of consumption: consumer fantasies, feelings, and fun,” Journal of Consumer Research, vol. 9, no. 2, p. 132, 1982. View at Publisher · View at Google Scholar
  10. B. R. Litman, “Predicting success of theatrical movies: an empirical study,” The Journal of Popular Culture, vol. 16, no. 4, pp. 159–175, 1983. View at Publisher · View at Google Scholar
  11. A. Ainslie, X. Drèze, and F. Zufryden, “Modeling movie life cycles and market share,” Marketing Science, vol. 24, no. 3, pp. 508–517, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Delen, R. Sharda, and P. Kumar, “Movie forecast Guru: A Web-based DSS for Hollywood managers,” Decision Support Systems, vol. 43, no. 4, pp. 1151–1170, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. P. K. Chintagunta, S. Gopinath, and S. Venkataraman, “The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets,” Marketing Science, vol. 29, no. 5, pp. 944–957, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Basuroy and S. Chatterjee, “Fast and frequent: Investigating box office revenues of motion picture sequels,” Journal of Business Research, vol. 61, no. 7, pp. 798–803, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. P. F. Skilton, “Knowledge based resources, property based resources and supplier bargaining power in Hollywood motion picture projects,” Journal of Business Research, vol. 62, no. 8, pp. 834–840, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. E. V. Karniouchina, “Impact of star and movie buzz on motion picture distribution and box office revenue,” International Journal of Research in Marketing, vol. 28, no. 1, pp. 62–74, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. R. J. Calantone, S. Yeniyurt, J. D. Townsend, and J. B. Schmidt, “The effects of competition in short product life-cycle markets: The case of motion pictures,” Journal of Product Innovation Management, vol. 27, no. 3, pp. 349–361, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Liu, “Word of mouth for movies: its dynamics and impact on box office revenue,” Journal of Marketing, vol. 70, no. 3, pp. 74–89, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. L. Qin, “Word-of-blog for movies: A predictor and an outcome of box office revenue?” Journal of Electronic Commerce Research, vol. 12, no. 3, pp. 187–198, 2011. View at Google Scholar · View at Scopus
  20. W. Duan, B. Gu, and A. B. Whinston, “Do online reviews matter? - An empirical investigation of panel data,” Decision Support Systems, vol. 45, no. 4, pp. 1007–1016, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Gazley, G. Clark, and A. Sinha, “Understanding preferences for motion pictures,” Journal of Business Research, vol. 64, no. 8, pp. 854–861, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. M. Clement, S. Wu, and M. Fischer, “Empirical generalizations of demand and supply dynamics for movies,” International Journal of Research in Marketing, vol. 31, no. 2, pp. 207–223, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. M. S. Sawhney and J. Eliashberg, “A parsimonious model for forecasting gross box-office revenues of motion pictures,” Marketing Science, vol. 15, no. 2, pp. 113–131, 1996. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Eliashberg and S. M. Shugan, “Film critics: Influencers or predictors?” Journal of Marketing, vol. 61, no. 2, pp. 68–78, 1997. View at Publisher · View at Google Scholar · View at Scopus
  25. S. A. Ravid, “Information, blockbusters, and Stars: A study of the film industry,” Journal of Business, vol. 72, no. 4, pp. 463–492, 1999. View at Publisher · View at Google Scholar · View at Scopus
  26. T. Dhar and C. B. Weinberg, “Measurement of interactions in non-linear marketing models: The effect of critics' ratings and consumer sentiment on movie demand,” International Journal of Research in Marketing, vol. 33, no. 2, pp. 392–408, 2016. View at Publisher · View at Google Scholar · View at Scopus
  27. E. M. Stoetzer, Diffusion of innovations, Simon and Schuster, 1995.
  28. C. Dellarocas, X. M. Zhang, and N. F. Awad, “Exploring the value of online product reviews in forecasting sales: the case of motion pictures,” Journal of Interactive Marketing, vol. 21, no. 4, pp. 23–45, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. Lee, S. Kim, and K. C. Cha, “A generalized Bass model for predicting the sales patterns of motion pictures having seasonality and herd behavior,” Journal of Global Scholars of Marketing Science, vol. 22, no. 4, pp. 310–326, 2012. View at Publisher · View at Google Scholar
  30. P. Marshall, M. Dockendorff, and S. Ibáñez, “A forecasting system for movie attendance,” Journal of Business Research, vol. 66, no. 10, pp. 1800–1806, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. L. Zhang, J. Luo, and S. Yang, “Forecasting box office revenue of movies with BP neural network,” Expert Systems with Applications, vol. 36, no. 3, pp. 6580–6587, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. A. Chakravarty, Y. Liu, and T. Mazumdar, “The differential effects of online word-of-mouth and critics' reviews on pre-release movie evaluation,” Journal of Interactive Marketing, vol. 24, no. 3, pp. 185–197, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. J. Eliashberg, A. Elberse, and M. A. A. M. Leenders, “The motion picture industry: Critical issues in practice, current research, and new research directions,” Marketing Science, vol. 25, no. 6, pp. 638–661, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. R. E. Krider and C. B. Weinberg, “Competitive dynamics and the introduction of new products: the motion picture timing game,” Journal of Marketing Research, vol. 35, no. 1, pp. 1–15, 1998. View at Publisher · View at Google Scholar · View at Scopus
  35. F. S. Zufryden, “Linking advertising to box office performance of new film releases - A marketing planning model,” Journal of Advertising Research, vol. 36, no. 4, pp. 29–41, 1996. View at Google Scholar · View at Scopus
  36. W. Duan, B. Gu, and A. B. Whinston, “The dynamics of online word-of-mouth and product sales—an empirical investigation of the movie industry,” Journal of Retailing, vol. 84, no. 2, pp. 233–242, 2008. View at Publisher · View at Google Scholar · View at Scopus
  37. F. Abel, E. Diaz-Aviles, N. Henze, D. Krause, and P. Siehndel, “Analyzing the blogosphere for predicting the success of music and movie products,” in Proceedings of the 2010 International Conference on Advances in Social Network Analysis and Mining (ASONAM'10), pp. 276–280, Odense, Denmark, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. T. Kim, J. Hong, and P. Kang, “Box office forecasting using machine learning algorithms based on SNS data,” International Journal of Forecasting, vol. 31, no. 2, pp. 364–390, 2015. View at Publisher · View at Google Scholar · View at Scopus
  39. Korean Film Council, “Korea Box office Information System,” 2013, http://www.kobis.or.kr/.
  40. K. Jedidi, R. E. Krider, and C. B. Weinberg, “Clustering at the movies,” Marketing Letters, vol. 9, no. 4, pp. 393–405, 1998. View at Publisher · View at Google Scholar · View at Scopus
  41. A. Elberse and J. Eliashberg, “Demand and supply dynamics for sequentially released products in international markets: the case of motion pictures,” Marketing Science, vol. 22, no. 3, pp. 329–435, 2003. View at Publisher · View at Google Scholar · View at Scopus
  42. R. J. Kuo and L. M. Lin, “Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering,” Decision Support Systems, vol. 49, no. 4, pp. 451–462, 2010. View at Publisher · View at Google Scholar · View at Scopus
  43. S. J. Cho and M. A. Hermsmeier, “Genetic algorithm guided selection: Variable selection and subset selection,” Journal of Chemical Information and Computer Sciences, vol. 42, no. 4, pp. 927–936, 2002. View at Publisher · View at Google Scholar · View at Scopus
  44. R. M. Jarvis and R. Goodacre, “Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data,” Bioinformatics, vol. 21, no. 7, pp. 860–868, 2005. View at Publisher · View at Google Scholar · View at Scopus
  45. S. M. Ross, Introduction to probability and statistics for engineers and scientists, Academic Press, San Diego, CA, USA, 2004. View at MathSciNet
  46. B. Jonsson, “Prediction with a linear regression model and errors in a regressor,” International Journal of Forecasting, vol. 10, no. 4, pp. 549–555, 1994. View at Publisher · View at Google Scholar · View at Scopus
  47. A. Goia, C. May, and G. Fusai, “Functional clustering and linear regression for peak load forecasting,” International Journal of Forecasting, vol. 26, no. 4, pp. 700–711, 2010. View at Publisher · View at Google Scholar · View at Scopus
  48. J. Zhang and L. C. Thomas, “Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD,” International Journal of Forecasting, vol. 28, no. 1, pp. 204–215, 2012. View at Publisher · View at Google Scholar · View at Scopus
  49. H. Lee, S. G. Kim, H.-W. Park, and P. Kang, “Pre-launch new product demand forecasting using the Bass model: a statistical and machine learning-based approach,” Technological Forecasting and Social Change, vol. 86, pp. 49–64, 2013. View at Publisher · View at Google Scholar · View at Scopus
  50. A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, vol. 14, no. 3, pp. 199–222, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  51. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), The MIT Press, Boston, Mass, USA, 2006. View at MathSciNet
  52. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, New York, NY, USA, 2001. View at Publisher · View at Google Scholar · View at MathSciNet
  53. Proceedings 2003 VLDB Conference, Elsevier, 2003.
  54. X. Yu, K. Q. Pu, and N. Koudas, “Monitoring k-nearest neighbor queries over moving objects,” in Proceedings of the 21st International Conference on Data Engineering (ICDE'05), pp. 631–642, Tokoyo, Japan, April 2005. View at Publisher · View at Google Scholar · View at Scopus
  55. M. Stone, “Cross-validatory choice and assessment of statistical predictions,” Journal of the Royal Statistical Society, vol. 36, pp. 111–147, 1974. View at Google Scholar · View at MathSciNet
  56. T. Evgeniou, M. Pontil, and A. Elisseeff, “Leave one out error, stability, and generalization of voting combinations of classifiers,” Machine Learning, vol. 55, no. 1, pp. 71–97, 2004. View at Publisher · View at Google Scholar · View at Scopus