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Original Articles

Early Predictions of Movie Success: The Who, What, and When of Profitability

References

  • Abbasi A.; Zahedi F. M.; Zeng D.; Chen Y.; Chen H.; and Nunamaker, J.F. Jr. Enhancing predictive analytics for anti-phishing by exploiting website genre information. Journal of Management Information Systems, 31, 4 (2015), 109–157.
  • Aggarwal, C.C.; Chen, C.; and Han, J. The inverse classification problem. Journal of Computer Science and Technology, 25, 3 (2010), 458–468.
  • Apala, K.R.; Jose, M.; Motnam, S.; Chan, C.C.; Liszka, K. J.; and de Gregorio, F. Prediction of movies box office performance using social media. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Niagra Falls: IEEE Computer Society, 2013, pp. 1209–1214.
  • Asur, S., and Huberman, B.A. Predicting the future with social media. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. Toronto: IEEE Computer Society, 2010, pp. 492–499.
  • Baimbridge, M. Movie admissions and rental income: The case of James Bond. Applied Economics Letters, 4, 1 (1997), 57–61.
  • Blei, D.M.; Ng, A.Y.; and Jordan, M.I. Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 1 (2003), 993–1022.
  • Boccardelli, P.; Brunetta, F.; and Vicentini, F. What is critical to success in the movie industry? A study on key success factors in the Italian motion picture industry. Dynamics of Institutions and Markets in Europe, 46, 4 (2008), 1–22.
  • Bozdogan, Y. The determinants of box office revenue: a case based study: Thirty, low budget, highest ROI films vs. thirty, big budget, highest grossing Hollywood films. MAthesis, University of Paris, 2013.
  • Burt, R.S. Structural holes and good ideas. American Journal of Sociology, 110, 2 (2004), 349–399.
  • Burt, R.S. Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press, 1992.
  • Chi, C.L.; Street, W.N.; Robinson, J.G.; and Crawford, M.A. Individualized patient-centered lifestyle recommendations: An expert system for communicating patient specific cardiovascular risk information and prioritizing lifestyle options. Journal of Biomedical Informatics, 45, 6 (2012), 1164–1174.
  • Craney, T.A., and Surles, J.G. Model-dependent variance inflation factor cutoff values. Quality Engineering, 14, 3 (2002), 391–404.
  • Cui, G.; Wong, M.L.; and Wan, X. Cost-sensitive learning via priority sampling to improve the return on marketing and CRM investment. Journal of Management Information Systems, 29, 1 (2012), 341–374.
  • Elberse, A. The power of stars: Do star actors drive the success of movies? AMA Journal of Marketing, 71, 4 (2007), 102–120.
  • Eliashberg, J.; Hui, S.; and Zhang, Z. Assessing box office performance using movie scripts: A kernel-based approach. IEEE Transactions on Knowledge and Data Engineering, 26, 11 (2014), pp. 2639–2648.
  • Eliashberg, J.; Hui, S.K.; and Zhang, Z.J. From story line to box office: A new approach for green-lighting movie scripts. Management Science, 53, 6 (2007), 881–893.
  • Eliashberg, J.; Jonker, J.J.; Sawhney, M.S.; and Berend, W. MOVIEMOD: An implementable decision-support system for prerelease market evaluation of motion pictures. Marketing Science, 19, 3 (2000), 226–243.
  • Gopinath, S.; Chintagunta, P. K.; and Venkataraman, S. Blogs, advertising and local market movie box office performance. Management Science, 59, 12 (2013), 2635–2654.
  • Guimera, R.; Uzzi, B.; Spiro, J.; and Amaral, L.A.N. Team assembly mechanisms determine collaboration network structure and team performance. Science, 308, 5722 (April 2005), 697–702.
  • Hevner, A.R.; March, S.T.; Park, J.; and Ram, S. Design science in information systems research. MIS Quarterly, 28, 1 (2004), 75–105.
  • Kuhn M., and Johnson, K. Applied Predictive Modeling. New York: Springer, 2013.
  • Lash, M.T.; Fu, S.; Wang, S.; and Zhao, K. Early prediction of movie success: What, who, and when. In N. Agarwal, K. Xu, and N. Osgood (eds.), Proceedings of the 2015 International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. Washington, DC: Springer, 2015, pp. 345–349.
  • Lash, M.T.; Lin, Q.; Street, W.N.; and Robinson, J.G. A budget-constrained inverse classification framework for smooth classifiers. arXiv preprint, 2016. https://arxiv.org/abs/1605.09068
  • Lutter, M. Creative Success and Network Embeddedness: Explaining Critical Recognition of Film Directors in Hollywood, 1900–2010 (July 9, 2014). MPIfG Discussion Paper 14/11. Available at SSRN: https://ssrn.com/abstract=2464150
  • Magni M.; Angst C.M.; and Agarwal R. Everybody needs somebody: The influence of team network structure on information technology use. Journal of Management Information Systems, 29, 3 (2012), 9–42.
  • Meiseberg, B.; and Ehrmann, T. Diversity in teams and the success of cultural products. Journal of Cultural Economics, 37, 1 (2013), 61–86.
  • Meiseberg, B.; Ehrmann, T.; and Dormann, J. We don’t need another hero: Implications from network structure and resource commitment for movie performance. Schmalenbach Business Review, 60, 1 (2008), 74–99.
  • Mestyán, M.; Yasseri, T.; and Kertész, J. Early prediction of movie box office success based on Wikipedia activity big data. PloS ONE, 8, 8 (January 2013). http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0071226
  • Motion Picture Association of America (MPAA). 2015 Theatrical market statistics. 2015. http://www.mpaa.org/wp-content/uploads/2016/04/MPAA-Theatrical-Market-Statistics-2015_Final.pdf
  • Parimi, R.; and Caragea, D. Pre-release box-office success prediction for motion pictures. In Proceedings of the Ninth International Conference on Machine Learning and Data Mining in Pattern Recognition. New York: Springer Berlin Heidelberg, 571–585.
  • Prat, N.; Comyn-Wattiau, I.; and Akoka, J. A taxonomy of evaluation methods for information systems artifacts. Journal of Management Information Systems, 32, 3 (2015), 229–267.
  • Sharda, R.; and Delen, D. Predicting box-office success of motion pictures with neural networks. Expert Systems with Applications, 30, 2 (2006), 243–254.
  • Sinha, A.P., and May, J.H.; Evaluating and tuning predictive data mining models using receiver operating characteristic curves. Journal of Management Information Systems, 21, 3 (2004), 249–280.
  • Taylor, P.; Simonoff, J.S.; and Sparrow, R. Predicting movie grosses: Winners and losers, blockbusters and sleepers. CHANCE, 13, 2 (2014), 15–24.
  • Uzzi, B., and Spiro, J. Collaboration and creativity: The small world problem. American Journal of Sociology, 111, 2 (2005), 447–504.
  • Vany, A.D.E., and Walls, W.D. Uncertainty in the movie industry: Does star power reduce the terror of the box office? Journal of Cultural Economics, 23, 4 (1999), 285–318.
  • Wallace, W.T.; Seigerman, A.; and Holbrook, M.B. The role of actors and actresses in the success of films: How much is a movie star worth? Journal of Cultural Economics, 17, 1 (1993), 1–27.
  • Walls, W.D. Modeling movie success when nobody knows anything: Conditional stable distribution analysis of film returns. Journal of Cultural Economics, 29, 3 (2005), 177–190.
  • Zaheer, A., and Soda, G. Network evolution: Structural holes. Administrative Science Quarterly, 54, 1 (2007), 1–31.
  • Zhang, W., and Skiena, S. Improving Movie Gross Prediction through News Analysis. In Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. Milan: IEEE Computer Society, 2009, pp. 301–304.
  • Zhao, K.; Wang, X.; Yu, M.; and Gao, B. User recommendation in reciprocal and bipartite social networks: A case study of online dating. IEEE Intelligent Systems, 29, 2 (2014), 27–35.
  • Zhao, K.; Yen, J.; Ngamassi, L.M.; Maitland, C.; and Tapia, A.H. Simulating inter-organizational collaboration network: a multi-relational and event-based approach. Simulation, 88, 5 (2011), 617–633.

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