Deadline May 26th 2020
The course paper abstract should contain four elements. First, a brief description of a topic related to the management of AI, digitization, or digital innovation should be presented as well as a motivation for why this is an important area to do research on. Next, it is useful both for the student and the course instructors to think about what kind of theoretical framework that could be used to elucidate the topic the student is interested in. This should take the form of a short theoretical discussion ending up with some ideas about how to apply it to the topic of choice. Third, the students should provide some initial thoughts on the methods they intend to employ including data, data capture and analytical techniques. Although many of the articles on the syllabus and examples in the lectures are quantitative, we strongly encourage qualitative work in this field as well. Finally, we encourage and motivate the students to think briefly about what kind of outlet the paper could fit into.
We recognize that this process will be very much a “work in progress” type of process, but it is always useful to have done some reading and thinking prior to taking part in the class. The instructors will use the abstracts to form groups and for individual discussions with the instructors. Everyone will get the opportunity to discuss their ideas with Avi Goldfarb.
Costs:
No tuition fees. Costs for semester registration and course literature apply.
Course evaluation:
Evaluation using final survey.
Course description
Through this course we aim to understand how digitization in general and artificial intelligence in particular shapes how management and firms are being understood. Particular emphasis will be on identifying research questions and formulating the theoretical insights needed to understand how digitization and artificial intelligence impact organizations and markets.
Learning outcomes:
After completing the course, the candidates will have acquired the following:
Knowledge- The candidate knows the state of the art of theoretical and empirical work being undertaken within the field of digitization and artificial intelligence and their impact on organizations, markets and the economy
- The candidate is able to evaluate different approaches to studying the interaction between artificial intelligence and organizational development and strategy.
- The candidate knows the most novel empirical techniques for studying digitization and artificial intelligence in the context of economics, management and innovation.
- The candidate knows core theoretical development on how innovation and strategy is shaped by digitization (largely platforms) and artificial intelligence
Skills- The candidate can identify gaps in the literature and formulate relevant research questions in order to contribute to the economics, management and/or innovation literature within the topic of the course.
- The candidate is able to clearly define the key concepts of the literature, their interdependencies and their causal mechanisms
General competence- The candidate is able to assess organization´s options for adopting digitization and artificial intelligence and can formulate relevant capabilities for the organization.
- The candidate can identify and assess multidisciplinary connections in studying and understanding how digitization and artificial intelligence impact organizations
- The candidate can discuss, reflect and identify ethical and societal issues
Prerequisites:
Must fulfill the requirements for admission to the PhD program
Recommended previous knowledge:
Knowledge of core microeconomics at the undergraduate level, management, innovation and organizations is an advantage.
Mode of delivery:
Online using Zoom lectures and group discussions
Learning activities and teaching methods:
Lectures and practice sessions
Assessment:
Paper (maximum 3000 words) which is graded pass/not pass.
Course literature and recommended reading
The reading list is subject to amendments at semester start.
* denotes required reading. Other readings are optional.
Day 1: Tuesday June 2
09:00-11:30 Introduction to the course – how digital changes everything
*Mayer-Schönberger, V., & Ramge, T. (2018). Reinventing capitalism in the age of big data.(selected chapters)
*McAfee, A., & Brynjolfsson, E. (2016). Machine, platform, crowd: harnessing our digital future (selected chapters)
Luca, Michael, Jon Kleinberg, and Sendhil Mullainathan. “Algorithms Need Managers Too,” Harvard Business Review Vol. 94, No. 1 (January–February 2016): 96–101.
Raj, M., & Seamans, R. (2019). Primer on artificial intelligence and robotics. Journal of Organization Design, 8(1). https://doi.org/10.1186/s41469-019-0050-0
12:30-13:30 Introduction to the economics of digitization
*Goldfarb, A., & Tucker, C. (2019). Digital Economics, Journal of Economic Literature 57, 3–43.
Economides, Nicholas, and Przemyslaw Jeziorski. 2017. “Mobile Money in Tanzania.” Marketing Science, 36(6):815-837.
Forman, C., Goldfarb, A., & Greenstein, S. (2012). The Internet and Local Wages: A Puzzle. American Economic Review, 102(1), 556–575.
Forman, C., & van Zeebroeck, N. (2019). Digital technology adoption and knowledge flows within firms: Can the Internet overcome geographic and technological distance? Research Policy, 48(8), 103697.
Gentzkow, Matthew and Jesse M. Shapiro. 2011. “Ideological Segregation Online and Offline.” The Quarterly Journal of Economics, 126(4), pp. 1799-839.
Greenstein, Shane. 2015. How the internet became commercial. Princeton University Press.
Greenstein, Shane, and Frank Nagle. 2014. “Digital Dark Matter and the Economic Contribution of Apache.” Research Policy 43 (4): 623–31.
Seamans, Robert, Feng Zhu. 2014. Responses to Entry in Multi-Sided Markets: The Impact of Craigslist on Local Newspapers. Management Science. 60(2): 476-493.
Zhu, Feng, and Marco Iansiti. 2019. Why some platforms thrive and others don’t. Harvard Business Review. Jan-Feb Issue, p. 118-125.
13:00-16:00 Advanced topics in the economics of digitization: Copyright, voice, monitoring
*Goldfarb, A., & Tucker, C. (2019). Digital Economics, Journal of Economic Literature 57, 3–43.
*Zhang, Laurina. 2018. Intellectual Property Strategy and the Long Tail: Evidence from the Recorded Music Industry. Management Science 64(1), 24-42.
Allcott, Hunt, Luca Braghieri, Sarah Eichmeyer, and Matthew Gentzkow. 2019. The Welfare Effects of Social Media. Forthcoming American Economic Review.
Athey, Susan, and Michael Luca. 2019. "Economists (and Economics) in Tech Companies." Journal of Economic Perspectives, 33 (1): 209-30.
Gans, Joshua, Avi Goldfarb, and Mara Lederman. 2020. “Exit, Tweets, and Loyalty.” Working paper, University of Toronto.
Hirschman, Albert O. 1970. Exit, Voice, and Loyalty. Harvard University Press, Cambridge MA.
Smith, Michael, and Rahul Telang. 2016. Streaming, Sharing, Stealing MIT Press. Cambridge MA.
Tirole, Jean. 2020. Digital Dystopia. Working paper, Toulouse.
Waldfogel, Joel. 2018. Digital Renaissance. Princeton University Press.
Yang, David Y. 2019. The Impact of Media Censorship: 1984 or Brave New World? Forthcoming American Economic Review.
Zhu, F, Liu, Q. Competing with complementors: An empirical look at Amazon.com. Strat Mgmt J. 2018; 39: 2618– 2642.
Day 2: Wednesday June 3
*Adner R. (2017). Ecosystem as structure: An actionable construct for strategy. Journal of Management, 43(1): 39–58.
Furr, N., & Shipilov, A. (2018). Building the right ecosystem for innovation. MIT Sloan Management Review, 59(4), 59-64.
*Granstrand, O & M. Holgersson, (2020) Innovation ecosystems: A conceptual review and a new definition, Technovation, Volumes 90–91.
Jacobides, M., Cennamo, C., & Gawer, A. (2018). Towards a theory of ecosystems. Strategic Management Journal, 39
Jacobides, M. (2019). In the ecosystem economy: What is your strategy? Harvard Business Review, September-October.
Kapoor, R. (2018). Ecosystems: Broadening the locus of value creation, Journal of Organization Design, 7(1), 1-16.
Shipilov, A. & Gawer, A. (2020). Integrating Research on Inter-Organizational Networks and Ecosystems, Academy of Management Annals.
13:00-16:00 General Purpose Technologies & Introduction to artificial intelligence
*Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. 2019. Introduction. In The Economics of Artificial Intelligence: An Agenda. Eds, Agrawal, Gans, and Goldfarb. University of Chicago Press, Chicago IL. pages 1-7 only.
*Bresnahan, T., Greenstein, S. The economic contribution of information technology: Towards comparative and user studies. J Evol Econ 11, 95–118 (2001).
Aghion, Philippe, Benjamin Jones, and Charles Jones. 2018. Artificial Intelligence and Economic Growth. In Agrawal, Gans, and Goldfarb Eds. The Economics of Artificial Intelligence: An Agenda. University of Chicago Press.
Agrawal, Ajay, Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. USA: Harvard Business Review Press.
Bresnahan, Timothy F., and Shane Greenstein. 1996. Technical progress in computing and in the uses of computers Brookings Papers on Economic Activity: Microeconomics, 1-78.
Bresnahan, F., & Trajtenberg, M. (1995). General purpose technologies 'Engines of growth’? Journal of Econometrics, 65, 83–108.
Brynjolfsson, Erik, Daniel Rock, and Chad Syverson. 2019. Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. In Agrawal, Gans, and Goldfarb Eds. The Economics of Artificial Intelligence: An Agenda. University of Chicago Press.
Cockburn, Iain, Rebecca Henderson, and Scott Stern. 2019. The Impact of Artificial Intelligence on Innovation. In Agrawal, Gans, and Goldfarb Eds. The Economics of Artificial Intelligence: An Agenda. University of Chicago Press
Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2018. “Human Decisions and Machine Predictions.” Quarterly Journal of Economics 133(1): 237–93.
Day 3: Thursday June 4
09:00-12:00 Management of digital transformation
TBD
13:00-16:00 Managing Artificial Intelligence: Disruption and Organizations
*Agrawal, Gans, and Goldfarb. 2017. How AI Will Change the Way We Make Decisions. Harvard Business Review Online. https://hbr.org/2017/07/how-ai-will-change-the-way-we-make-decisions.
*Agrawal, Gans, and Goldfarb. 2017. The Trade-Off Every AI Company Will Face. Harvard Business Review Online. https://hbr.org/2017/03/the-trade-off-every-ai-company-will-face.
Agrawal, Ajay, Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. USA: Harvard Business Review Press.
Brynjolfsson, E., X Hui and M Liu. 2019. “Does machine translation affect international trade? Evidence from a large digital platform.” Management Science 65(12), 5449-5460.
Fleder, Daniel, and Kartik Hosanagar. 2009. “Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity.” Management Science, 55(5), 697-712.
Milgrom, Paul R. and Steven Tadelis. 2019. How Artificial Intelligence and Machine Learning Can Impact Market Design. In The Economics of Artificial Intelligence. Eds. Agrawal, Gans, Goldfarb. University of Chicago Press.
Day 4: Friday June 5
12:00-13:00 Digital technologies as solver of complexity
*George, G, R. Merrill & S. Schillebeeckx (2020) Digital Sustainability and Entrepreneurship: How Digital Innovations Are Helping Tackle Climate Change and Sustainable Development. Éntrepreneurship Theory and Practice
13:00-16:00 AI Policy
*Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. 2019. “Economic Policy for Artificial Intelligence.” Innovation Policy and the Economy, Volume 19, Josh Lerner and Scott Stern editors.
Acemoglu, Daron, and Pascual Restrepo. 2019. Automation and New Tasks: The Implications of the Task Content of Technology for Labor Demand. Journal of Economic Perspectives
Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. 2019. Introduction. In The Economics of Artificial Intelligence: An Agenda. Eds, Agrawal, Gans, and Goldfarb. University of Chicago Press, Chicago IL.
Agrawal, Ajay, Joshua Gans, and Avi Goldfarb (Editors). 2019. The Economics of Artificial Intelligence: An Agenda. University of Chicago Press, Chicago.
Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. 2019. Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction. Journal of Economic Perspectives 33(2), 31-50.
Furman, Jason, and Robert Seamans. 2018. AI and the Economy. Innovation Policy and the Economy, Volume 19, Josh Lerner and Scott Stern editors.
Goldfarb, Avi, and Daniel Trefler. 2018. Artificial Intelligence and Trade. In Agrawal, Gans, and Goldfarb Eds. The Economics of Artificial Intelligence: An Agenda. University of Chicago Press.
Day 5: Wednesday June 17
TBD
Overlapping courses:
None