Essays on the Evaluation of Novel Ideas

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Essays on the Evaluation of Novel Ideas Essays on the Evaluation of Novel Ideas by Jesse Michael Wahlen B.B.A., Loyola University Chicago (2009) M.Sc., London School of Economics (2011) S.M., Massachusetts Institute of Technology (2018) Submitted to the MIT Sloan School of Management in Partial Fulfillment of the Requirements for the Degree of: Doctor of Philosophy at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY May 2020 c 2020 Massachusetts Institute of Technology. All rights reserved. Author...................................................................................................................Department of Management April 30, 2020 Certified by.............................................................................................................. Ezra W. Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship and Strategy Thesis Supervisor Accepted by........................................................................................................................... Catherine Tucker Sloan Distinguished Professor of Management Chair, MIT Sloan PhD Program Essays on the Evaluation of Novel Ideas by J. Michael Wahlen Submitted to the Sloan School of Management on April 30, 2020, in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Management Abstract In this dissertation, I examine the evaluation of novel ideas in two essays. In the first, I, along with Hong Luo and Jeffrey Macher, study a novel, low-cost approach to aggregating judgment from a large number of industry experts on ideas that they encounter in their normal course of business in the film industry, where customer appeal is difficult to predict and investment costs are high. The Black List, an annual publication, ranks unproduced scripts based on anonymous nominations from film executives. This approach entails an inherent trade-off: low participation costs enable high response rates; but nominations lack standard criteria, and which voters see which ideas is unobservable and influenced by various factors. Despite these challenges, we find that such aggregation is predictive: listed scripts are substantially more likely to be released than observably similar but unlisted scripts, and, conditional on release and investment levels, listed scripts generate higher box office revenues. We also find that this method mitigates entry barriers for less-experienced writers as: (i) their scripts are more likely to be listed and to rank higher if listed; (ii) being listed is associated with a higher release rate. Yet, the gap in release probabilities relative to experienced writers remains large, even for top-ranked scripts. In the second essay, I move from the question of the abstract value of an idea to the relative value given one’s past and resources. In markets which require architectural changes for established firms, prior research claims incumbents must best the young firms imprinted by the new technology at the dominant design. If they cannot, they should seek to avoid the market altogether. In contrast, this paper suggests that rather than compete for the dominant design, incumbents can succeed by focusing on niche segments that are proximate to their core business. This paper finds support for this strategy in console game makers’ response to the rise of mobile games. Using a novel method to measure distance from the dominant design, the analysis shows that deviating from the dominant design was associated with greater revenue for console publishers, while mobile publishers observed the opposite result. Thesis Supervisor: Ezra W. Zuckerman Sivan Title : Alvin J. Siteman (1948) Professor of Entrepreneurship and Strategy 2 Acknowledgments There are many people who have guided and supported me in completing this degree. My committee members — Ezra Zuckerman, Pierre Azoulay, and Hong Luo — have each invested heavily in me, generously providing their time and advice to help me develop a “taste” for academic research. Each of them was kind enough to not only invite me to work with them on a research project, but also to guide me in my own work. More generally, the BPS faculty have been hugely supportive of me and provided helpful feedback in seminars, lunches, and discussions. Catherine Turco, Emilio Castilla, Kate Kellogg, Ray Reagans, Roberto Fernandez, and Susan Silbey, among many others, have provided invaluable advice and encouragement during my time at MIT. Additionally, Hillary Ross and Davin Schnappauf have been instrumental in guiding me through the entire PhD process. One of the greatest joys during my time at MIT has been getting to know the other students. I gained so much more from classes than the readings, lectures, and problem sets by getting to study along side Alex Kowalski, Brittany Bond, Carolyn Fu, Ethan Poskanzer, James Houghton, Jenna Myers, Heather Yang, Mahreen Kahn, Simon Friis, Summer Jackson, and Vanessa Conzon. My office mates Ari Galper, Avi Collis, Daniel Fehder, Daniel Rock, Hagay Volvosky, and Sebastian Steffen made being physically present at MIT fun and stimulating. Finally, I owe a huge thanks to the (many) students ahead of me who took the time to point me in the right direction, including Abhishek Nagaraj, Daniel Kim, Erik Duhaime, James Riley, Joshua Krieger, Julia DiBenigno, Minjae Kim, Samantha Zyontz, and Tristan Botelho. Finally, I owe a special thanks to my family. My wife, Megan has been at my side every step of this journey. She encouraged me to apply, helped me through the admissions process, moved with me so I could attend, and has cheered me on ever after. My parents, Jan and Kurt, made this all possible through their constant care and support. And my brother Sam and sister Hannah, who have always encouraged me and have had to put up with me their entire lives. Thank you all. 3 Contents 1 Judgment Aggregation in Creative Production: Evidence from the Movie Industry 5 1.1 Introduction . .6 1.2 Background and Conceptual Framework . 11 1.2.1 Background . 11 1.2.2 Conceptual Framework . 12 1.3 Data . 19 1.3.1 Samples . 19 1.3.2 Variables . 20 1.4 Results . 21 1.4.1 Black List Nominations and Release Outcomes . 22 1.4.2 The Black List and Writers of Different Experience Levels . 25 1.4.3 Generalizability . 30 1.5 Conclusion . 33 1.6 References . 35 1.7 Tables and Figures . 37 1.8 Appendix . 45 2 Architectural Misfits: Console Video Game Makers and the Rise of Mobile Games 73 2.1 Introduction . 74 2.2 Theory . 76 2.3 Setting . 79 2.3.1 Industry Background . 79 2.3.2 Mobile Games’ Dominant Design . 81 2.3.3 The Architectural Changes Required for Mobile Games . 82 2.3.4 Relation to Theory . 84 2.4 Data . 84 2.4.1 Data Sources and Sampling . 84 2.4.2 Summary Statistics . 86 2.5 Results . 87 2.5.1 Evidence of an Architectural Problem . 87 2.5.2 Deviating from the Dominant Design . 89 2.5.3 Timing and Sustainability . 93 2.6 Conclusion . 94 2.6.1 Limitations . 94 2.6.2 Implications . 96 2.7 References . 98 2.8 Tables and Figures . 101 2.9 Appendix . 109 4 Chapter 1 Judgment Aggregation in Creative Production: Evidence from the Movie Industry (With Hong Luo and Jeffrey Macher) Abstract We study a novel, low-cost approach to aggregating judgment from a large number of industry experts on ideas that they encounter in their normal course of business. Our context is the movie industry, in which customer appeal is difficult to predict and investment costs are high. The Black List, an annual publication, ranks unproduced scripts based on anonymous nominations from film executives. This approach entails an inherent trade-off: low participation costs enable high response rates; but nominations lack standard criteria, and which voters see which ideas is unobservable and influenced by various factors. Despite these challenges, we find that such aggregation is predictive: listed scripts are substantially more likely to be released than observably similar but unlisted scripts, and, conditional on release and investment levels, listed scripts generate higher box office revenues. We also find that this method mitigates entry barriers for less-experienced writers as: (i) their scripts are more likely to be listed and to rank higher if listed; (ii) being listed is associated with a higher release rate. Yet, the gap in release probabilities relative to experienced writers remains large, even for top-ranked scripts. These results can be explained by the premise that scripts from less-experienced writers are more visible among eligible voters than scripts from experienced writers. This highlights idea visibility as an important determinant of votes and surfaces the trade-offs, as well as potential limitations, associated with such methods. We thank Dan Gross, Aseem Kaul, Ramana Nanda, Catherine Turco, Ezra Zuckerman, and seminar participants at Harvard Business School, Strategic Research Forum, Strategy Science Conference, University of Toronto, and MIT Economic Sociology Working Group for helpful comments. Tejas Ramdas and Esther Yan provided excellent research assistance. All errors and omis- sions are our own. 5 1.1 Introduction Successfully evaluating and selecting early-stage ideas is immensely important and yet extremely difficult. This is especially true in domains that lack concrete and objective quality signals, such as creative industries or those with low R&D-intensity (Waldfogel, 2017; Scott et al., forthcoming). In these settings, even industry experts may be limited in their ability to predict success. Moreover, the volume of ideas is often large, making screening costly. To address these challenges, decision makers often limit their searches to ideas from creators with strong, observable track records or rely on reputable gatekeepers. However, such remedies may lead to high entry barriers for those lacking prior experience or industry connections, as well as to an inefficient allocation of resources (Caves 2000; Kaplan et al. 2009; Stuart and Sorensen 2010). One promising approach to addressing these challenges is the so-called ‘wisdom of crowds’ (Surowiecki 2005). Though specific applications differ, the basic idea is rooted in the statistical principle that the aggregation of noisy but relatively independent estimates systematically increases precision (Condorcet 1785; Bates and Granger 1969).
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