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Entertainment Product Decisions, Episode 4: How to Develop New Successful Entertainment Products

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Abstract

In this chapter, we investigate how entertainment firms can manage their innovation activities to create new entertainment products on a continuous basis. Finding a way to respectfully balance artistic and economic goals is the foundation for the chapter; our analysis shows that this can only be achieved by creating a culture that combines autonomy and responsibility. Such a culture must be supported by an organizational structure that attracts people with the right skills and values and equips and enables them to be creative, but with discipline. We complement this firm-level analysis of factors that contribute to prolific innovation activities with a product-level analysis of approaches that can improve managers’ understanding of a new product’s commercial potential. We review the different econometric prediction methods that are available for such a purpose and discuss concrete scientific prediction models for new product success and their use at different stages of the innovation process.

Ronny Behrens co-authored this chapter with us.

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Notes

  1. 1.

    Let us note that with regard to the timing dimension of innovation, some particularities also exist for entertainment—we discuss them in our chapter on distribution decisions.

  2. 2.

    Think of the wonderful animated travel map in Raiders of the Lost Ark —which the film’s director Steven Spielberg invented “[t]o save money” (Total Film 2006).

  3. 3.

    Other reasons, such as culture, also contributed to this decision—we will get back to them.

  4. 4.

    Please see our link between learning and the Goldman mantra in our introduction to this book.

  5. 5.

    We discuss the role of timing for success predictions later in this chapter.

  6. 6.

    We outlined these challenges when discussing the “art-for-art’s-sake” property of entertainment earlier in the book.

  7. 7.

    See also our analysis of the entertainment industry’s value chain today in our chapter on entertainment business models.

  8. 8.

    See also Smith and Telang’s (2016) detailed analysis of how digitalization is transforming the entertainment business.

  9. 9.

    Any scholar will be reminded of the ideal version of the peer-review process in academia.

  10. 10.

    Flat structures and open communication should not be confused with eternal harmony: at least four of the five substituted director were no longer with Pixar in 2013 (Spiegel 2013).

  11. 11.

    But this causal nature should not be taken lightly.

  12. 12.

    Please see Algobeans (2016) for a more accessible description of artificial neural networks and Bishop (2006) for a more technical overview.

  13. 13.

    We have similar issues with other studies that predict box office using neural networks, such as Sharda and Delen (2010), Ghiassi et al. (2014), and Zhou et al. (2017).

  14. 14.

    Josh Eliashberg and his colleagues apply the method in their analysis of movie script features, whereas Parimi and Caragea use it for a general prediction exercise.

  15. 15.

    A separate approach to include distribution into a model of movie diffusion is the approach by Jones (1991), who essentially suggests a modification of the Bass model.

  16. 16.

    A study by Radas and Shugan (1998) focuses on how seasonal variations of demand for entertainment could be embedded in diffusion models. Please see our chapter on entertainment distribution decisions for a discussion of the impact of products’ release timing on success.

  17. 17.

    Please see our discussion of the crucial role of buzz in today’s entertainment marketplace in our chapter on earned entertainment communication.

  18. 18.

    In the case of MOVIEMOD, the variables range from product variables (such as the quality, theme, story, and cast) to advertising and distribution.

  19. 19.

    Please note that we make a clear distinction between experience-based word of mouth and other kinds of (speculative) consumer articulations.

  20. 20.

    Let us note that Dellarocas et al.’s study measures only the amount of word of mouth and only compares the word-of-mouth model with one that does include neither word of mouth nor sales data.

  21. 21.

    At the time Neelamegham and Chintagunta developed their model, sequential international releases were dominant in film. Please see also our discussion of such “intermarket success-breed-success effects” in the context of entertainment distribution decisions.

  22. 22.

    In the article, we refer to the standard error of the (regression) estimate, or SEE, which is mathematically the same as the RMSE.

References

  • Albers, S., & Eggers. S. (1991). Organisatorische Gestaltung von Produktinnovations-Prozessen – Führt der Wechsel des Organisationsgrades zu Innovationserfolg? Zeitschrift für betriebswirtschaftliche Forschung, 43, 44–46.

    Google Scholar 

  • Algobeans (2016). Artificial neural networks (ANN) introduction, March 13, https://goo.gl/hprXNQ.

  • Ainslie, A., Drèze, X., & Zufryden, F. (2005). Modeling movie life cycles and market share. Marketing Science, 24, 508–517.

    Article  Google Scholar 

  • Amabile, T. M. (1996). Creativity and innovation in organizations. Boston: Harvard Business School Background Note.

    Google Scholar 

  • Archer, A., & Walcyzk, D. (2006). Driving creativity and innovation through culture. Design Management Review, 17, 15–20.

    Article  Google Scholar 

  • Arrow, K. (1962). Economic welfare and the allocation of resources for invention. In The rate and direction of inventive activity: Economic and social factors (pp. 609–626). Princeton University Press.

    Google Scholar 

  • Asad, K. I., Ahmed, T., & Rahman, S. (2012). Movie popularity classification based on inherent movie attributes using C4.5, PART and correlation coefficient. In Proceedings of ICIEV (pp. 747–752).

    Google Scholar 

  • AT5 (2017). Herman Hulst (Guerrilla Games) We can make what we want of Playstation. AT5, March 16, https://goo.gl/AJkgG1.

  • Austin, B. A. (1989). Immediate seating: A look at movie audiences. California: Wadsworth Pub. Co.

    Google Scholar 

  • Avirgan, J. (2015). Podcast: The guy who predicts whether a movie will bomb, months before it’s made. FiveThirtyEight, September 7, https://goo.gl/eNTnSC.

  • Bailey, S. (2016). Kanye West: Free form. Surface, November 18, https://goo.gl/P5e8HD.

  • Barber, N. (2015). Heaven’s Gate: From Hollywood disaster to masterpiece. BBC, December 4, https://goo.gl/TvSji6.

  • Bass, F. M. (1969). A new product growth model for consumer durables. Management Science, 15, 215–227.

    Article  Google Scholar 

  • Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process management: The productivity dilemma revisited. The Academy of Management Review, 28, 238–256.

    Article  Google Scholar 

  • Bishop, C. M. (2006). Pattern recognition and machine learning. Cambridge: Springer.

    Google Scholar 

  • Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16, 199–231.

    Article  Google Scholar 

  • Brownlee, Alexander E. I., Regnier-Coudert, O., McCall, J. A. W., Massie, S., & Stulajter, S. (2013). An application of a GA with Markov network surrogate to feature selection. International Journal of Systems Science, 44, 2039–2056.

    Article  Google Scholar 

  • Brunsdale, M. M. (2010). Icons of crime and detection. Santa Barbara: Greenwood.

    Google Scholar 

  • Cameron, K. S., & Freeman, S. J. (1991). Cultural congruence, strength, and type: Relationships to effectiveness. Research in Organizational Change and Development, 5, 23–58.

    Google Scholar 

  • Catmull, Ed. (2008). How Pixar fosters collective creativity. Harvard Business Review, 86, 64–72.

    Google Scholar 

  • Catmull, Ed., & Wallace, A. (2014). Creativity, Inc. New York: Random House LLC.

    Google Scholar 

  • Chang, B.-H., & Ki, E.-J. (2005). Devising a practical model for predicting theatrical movie success: Focusing on the experience good property. Journal of Media Economics, 18, 247–269.

    Article  Google Scholar 

  • Clarkson, N. (2016). Why failure is a key part of Pixar’s culture. Virgin, January 8, https://goo.gl/LfWY1t.

  • Coase, R. H. (1937). The nature of the firm. Economica, 4, 386–405.

    Article  Google Scholar 

  • Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of Research in Personality, 19, 109–134.

    Article  Google Scholar 

  • Deci, E. L., & Ryan, R. M. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.

    Article  Google Scholar 

  • Dellarocas, C., Zhang, X., & Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing, 21, 23–45.

    Article  Google Scholar 

  • Deng, Z., Navarthna, R., Carr, P., Mandt, S., Yue, Y., Matthews, I., & Mori, G. (2017). Factorized variational autoencoders for modeling audience reactions to movies. In IEEE Conference on Computer Vision and Pattern Recognition.

    Google Scholar 

  • Deshpande, R., Farley, J. U., & Webster, F. E., Jr. (1993). Corporate culture, customer orientation, and innovativeness in Japanese Firms: A quadrad analysis. Journal of Marketing, 57, 23–37.

    Article  Google Scholar 

  • DeVault, G. (2016). The market research behind Hollywood movies. The Balance, September 14, https://goo.gl/KjfQtb.

  • Dougherty, D., & Heller, T. (1994). The illegitimacy of successful product innovation in established firms. Organization Science, 5, 200–218.

    Article  Google Scholar 

  • Disney, W. (2017) Walt Disney: Quotes. IMDb, no date, https://goo.gl/5FaCr3.

  • Edmonson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44, 350–383.

    Article  Google Scholar 

  • Eliashberg, J., Jonker, J., Sawhney, M., & Wierenga, B. (2000). MOVIEMOD: An implementable decision-support system for prerelease market evaluation of motion pictures. Marketing Science, 19, 226–243.

    Article  Google Scholar 

  • Eliashberg, J., Hui, S. K., & Zhang, J. Z. (2007). From story line to box office: A new approach for green-lighting movie scripts. Management Science, 53, 881–893.

    Article  Google Scholar 

  • Faughnder, R. (2017). The reason Hollywood’s studio leadership is in flux: The business model is changing. Los Angeles Times, March 26, https://goo.gl/KWkK2D.

  • Fleming Jr., M. (2016). ‘Big Bad Wolves’ helmers Aharon Keshales & Navot Papushado exit Bruce Willis Death Wish remake. Deadline, May 4, https://goo.gl/xUV3uz.

  • Franklin-Wallis, O. (2015). How Pixar embraces a crisis. Wired, November 17, https://goo.gl/dfVMm8.

  • Friend, T. (2016). The mogul of the middle. The New Yorker, January 11, https://goo.gl/8hYXxT.

  • Gartner (2016). Gartner’s 2016 hype cycle for emerging technologies identifies three key trends that organizations must track to gain competitive advantage. Gartner, August 16, https://goo.gl/pmmHPs.

  • Ghiassi, M., Lio, D., & Moon, B. (2014). Pre-production forecasting of movie revenues with a dynamic artificial neural network. Expert Systems with Applications, 42, 3176–3193.

    Article  Google Scholar 

  • Gilson, L. L. (2015). Creativity in teams: Processes and outcomes in creative industries. In C. Jones, M. Lorenzen, & J. Sapsed (Eds.), The Oxford Handbook of Creative Industries (pp. 50–74). Oxford: Oxford University Press.

    Google Scholar 

  • Gilson, L. L., & Shalley, C. E. (2004). A little creativity goes a long way: An examination of teams’ engagement in creative processes. Journal of Management, 30, 453–470.

    Article  Google Scholar 

  • Gitlin, T. (1983). Inside prime time. New York: Pantheon Books.

    Google Scholar 

  • Gomez-Uribe, C. A., & Hunt, N. (2015). The Netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems, 6, 13–19.

    Article  Google Scholar 

  • Greve, H. R. (2007). Exploration and exploitation in product innovation. Industrial and Corporate Change, 16, 945–975.

    Article  Google Scholar 

  • Gupta, A. K., Smith, K. G., & Shalley, C. E. (2006). The interplay between exploration and exploitation. Academy of Management Journal, 49, 693–706.

    Article  Google Scholar 

  • Hamel, G. (2006). Management à la Google. The Wall Street Journal, April 26, https://goo.gl/REAaRj.

  • Harvey, S. (2014). Creative synthesis: Exploring the process of extraordinary group creativity. Academy of Management Review, 39, 324–343.

    Article  Google Scholar 

  • Hastings, R. (2009). Netflix culture: Freedom & responsibility. SlideShare, August 1, https://goo.gl/QHvJAP.

  • Hennig-Thurau, T., Fuchs, S., & Houston, M. B. (2013). What’s a movie worth? Determining the monetary value of motion pictures’ TV rights. International Journal of Arts Management, 15, 4–20.

    Google Scholar 

  • Hotho, S., & Champion, K. (2011). Small businesses in the new creative industries: Innovation as a people management challenge. Management Decision, 49, 29–54.

    Article  Google Scholar 

  • Howe, S. (2013). How Martin Scorsese’s Elmore Leonard movie LaBrava is one that got away. Vulture, August 23, https://goo.gl/YLrYnq.

  • Ingram, M. (2017). Here’s what Netflix and Amazon spent millions on at Sundance. Fortune, January 30, https://goo.gl/thLE4t.

  • Jones, R. (1991). Incorporating distribution into new product diffusion models. International Journal of Research in Marketing, 8, 91–112.

    Article  Google Scholar 

  • Jones, O. (2014). Yes, we’re being bought by Microsoft. Mojang, September 15, https://goo.gl/psZGmM.

  • Jucevicius, G. (2010). Culture vs. cultures of innovation: Conceptual framework and parameters for assessment. In Proceedings of the International Conference on Intellectual Capital (pp. 236–244).

    Google Scholar 

  • Kanter, R. M. (1997). Strategies for success in the new global economy: An interview with Rosabeth Moss Kant. Strategy & Leadership, 25, 20–26.

    Article  Google Scholar 

  • Kelleher, J. D., Namee, B. M., & D’Arcy, A. (2015). Machine learning for predictive data analytics. London: The MIT Press.

    Google Scholar 

  • Kirschbaum, E., & Hopewell, J. (2008). StudioCanal buys Kinowelt. Variety, January 17, https://goo.gl/oNCSEn.

  • Kloo, A. (2016). Amazon-Video-Chef Schneider: Kreative Freiheit ist Grundbedingung für einzigartige Serien. Blickpunkt: Film, October 18, https://goo.gl/w2gjLS.

  • Kohavi, R., & Provost, F. (1998). Glossary of terms. Journal of Machine Learning, 30, 271–274.

    Google Scholar 

  • Kokonis, M. (2009). Hollywood’s major crisis and the American film ‘Renaissance’. In R. B. Ray (Ed.), A certain tendency of the Hollywood cinema, 1930–1980 (pp. 169–206). Princeton: Princeton University Press.

    Google Scholar 

  • Kurokawa, S. (1997). Make-or-buy decisions in R&D: Small technology based firms in the United States and Japan. IEEE Transactions of Engineering Management, 44, 124–134.

    Article  Google Scholar 

  • Kehr, D. (2008). United Artists—90th anniversary. New York Times, March 27, https://goo.gl/LgxWam.

  • Litman, B. R. (1983). Predicting success of theatrical movies: An empirical study. Journal of Popular Culture, 16, 159–175.

    Article  Google Scholar 

  • Liu, X., Shi, S., Teixera, T., & Wedel, M. (2018). Video content marketing: The making of clips. Journal of Marketing, 82, 86–101.

    Article  Google Scholar 

  • Lumpkin, G. T., & Dess, G. G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. Academy of Management Review, 21, 135–172.

    Article  Google Scholar 

  • Lussier, G. (2016). Walt Disney Animation is officially as good as Pixar now. Gizmodo UK, February 18, https://goo.gl/JyLJ1s.

  • March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2, 71–87.

    Article  Google Scholar 

  • Marich, R. (2013). Marketing to moviegoers: A handbook of strategies and tactics. United States: Library of Congress Cataloging-in-Publication Data.

    Google Scholar 

  • Masters, K. (2016). Steven Spielberg on Dreamworks’ past, Amblin’s present and his own future. The Hollywood Reporter, June 15, https://goo.gl/LqFjKR.

  • McNary, D. (2017). Netflix buys Martin Scorsese’s ‘The Irishman’ starring Robert De Niro. Variety, February 21, https://goo.gl/pcLFjC.

  • Moe, W. W., & Fader, P. S. (2002). Using advance purchase orders to forecast new product sales. Marketing Science, 21, 347–364.

    Article  Google Scholar 

  • Moriarty, C. (2013). Rising to greatness: The history of Naughty Dog. IGN, October 4, https://goo.gl/bXQVyS.

  • Muller, E., Peres, R., & Mahajan, V. (2009). Innovation diffusion and new product growth. Cambridge: Marketing Science Institute.

    Google Scholar 

  • Narayan, V., & Kadiyali, V. (2016). Repeated interactions and improved outcomes: An empirical analysis of movie production in the United States. Management Science, 62, 591–607.

    Article  Google Scholar 

  • Neelamegham, R., & Chintagunta, P. (1999). A Bayesian model to forecast new product performance in domestic and international markets. Marketing Science, 18, 115–136.

    Article  Google Scholar 

  • Nocera, J. (2016). Can Netflix survive in the new world it created? The New York Times Magazine, June 15, https://goo.gl/e1d2Zu.

  • Packard, G., Aribarg, A., Eliashberg, J., & Foutz, N. Z. (2015). The role of network embeddedness in film success. International Journal of Research in Marketing, 33, 328–342.

    Article  Google Scholar 

  • Parimi, R., & Caragea, D. (2013). Pre-release box-office success prediction for motion pictures. In Proceedings of International Workshop on Machine Learning and Data Mining in Pattern Recognition (pp. 571–585). Springer, Berlin, Heidelberg.

    Chapter  Google Scholar 

  • Peltoniemi, M. (2015). Cultural industries: Product-market characteristics, management challenges and industry dynamics. International Journal of Management Reviews, 17, 41–68.

    Article  Google Scholar 

  • Pond, S. (1986). Dateline Hollywood. The Washington Post, August 28, https://goo.gl/7o2UFN.

  • Poppo, L., & Zenger, T. (1998). Testing alternative theories of the firm: Transaction cost, knowledge-based, and measurement explanations for make-or-buy decisions in information services. Strategic Management Journal, 19, 853–877.

    Article  Google Scholar 

  • Radas, S., & Shugan, S. M. (1998). Seasonal marketing and timing new product introductions. Journal of Marketing Research, 35, 296–315.

    Article  Google Scholar 

  • Rao, H., Sutton, R., & Webb, A. P. (2008). Innovation lessons from Pixar: An interview with Oscar-winning director Brad Bird. McKinsey Quarterly, https://goo.gl/rHqcDv.

  • Russell, J. (2004). Foundation myths. New Review of Film and Television Studies, 2, 233–255.

    Article  Google Scholar 

  • Savitskaya, I., & Järvi, K. (2012). Culture for innovation: Case Finnish game development. Proceedings of ISPIM Conference, 23, 1–15.

    Google Scholar 

  • Sawhney, M. S., & Eliashberg, J. (1996). A parsimonious model for forecasting gross box-office revenues of motion pictures. Marketing Science, 15, 113–131.

    Article  Google Scholar 

  • Schillat, F. (2017). Amazon-Prime-Video-Chef im Interview: ‘Wir haben kein Interesse daran, dass unsere Kunden nicht mehr fernsehen’. Meedia, March 15, https://goo.gl/zWmUzg.

  • Sharda, R., & Delen, D. (2006). Predicting box-office success of motion pictures with neural networks. Expert Systems with Applications, 30, 243–254.

    Article  Google Scholar 

  • Sharda, R., & Delen, D. (2010). Predicting the financial success of Hollywood movies using an information fusion approach. Industrial Engineering Journal, 21, 30–37.

    Google Scholar 

  • Simonoff, J. S., & Sparrow, I. R. (2000). Predicting movie grosses: Winners and losers, blockbusters and sleepers. Chance, 13, 15–24.

    Article  Google Scholar 

  • Smith, S., & Paquette, S. (2010). Creativity, chaos and knowledge management. Business Information Review, 272, 118–123.

    Article  Google Scholar 

  • Smith, M. D., & Telang, R. (2016). Streaming, sharing, stealing—Big data and the future of entertainment. Cambridge: The MIT Press.

    Google Scholar 

  • Spiegel, J. (2013). The Pixar perspective on replacing directors. The Pixar Times, September 3, https://goo.gl/PhX4oH.

  • Steel, E. (2014). Those dreaded spoilers that can torpedo dramatic plot take on a new meaning. New York Times, September 21, https://goo.gl/tFapLw.

  • Slifkin, I. (2014). Cannon fodder: In praise of Golan and Globus. MovieFanFare, June 13, https://goo.gl/SZkN2y.

  • Thomson, D. (2008). The history of United Artists. The Guardian, February 23, https://goo.gl/AqaTym.

  • Total Film (2006). The story behind Raiders of the Lost Ark. Gamesradar+, August 24, https://goo.gl/h5eMrT.

  • de Ven, V., Andrew, D. P., Garud, R., & Venkataraman, S. (1999). The innovation journey. New York: Oxford University Press.

    Google Scholar 

  • Walesh, S. G. (2012). Staging a creative culture. Leadership and Management in Engineering, 12, 338–340.

    Article  Google Scholar 

  • Walker, G., & Weber, D. (1984). A transaction cost approach to make-or-buy decisions. Administrative Science Quarterly, 29, 373–391.

    Article  Google Scholar 

  • Williamson, O. E. (1985). The economic institutions of capitalism. Firms, markets, relational contracting. New York: The Free Press.

    Google Scholar 

  • Zhou, Y., Zhang, L., & Yi, Z. (2017). Predicting movie box-office revenues using deep neural networks. Neutral Computing and Applications, Online Publication, 1–11.

    Google Scholar 

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Authors and Affiliations

  1. University of Münster, Münster, Germany

    Thorsten Hennig-Thurau

  2. The Neeley School of Business, Texas Christian University, Fort Worth, TX, USA

    Mark B. Houston

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Correspondence to Thorsten Hennig-Thurau .

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Hennig-Thurau, T., Houston, M.B. (2019). Entertainment Product Decisions, Episode 4: How to Develop New Successful Entertainment Products. In: Entertainment Science. Springer, Cham. https://doi.org/10.1007/978-3-319-89292-4_10

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