• Understand the current debates around innovation in the digital economy, strategy, as it relates to digital economy and data.
• Explain how digital changes and the availability of data transform the business landscape.
• Discuss relevant theories and explain their assumptions, causal dynamics and processes.
• Assess the role of data in business innovation and specify success and failure factors
• Understand central concepts around management in the context of digital businesses.
• Discuss ethical issues of the digital economy and organizations’ use of data.

This course will give students an introduction to central issues of innovation and strategy in the digital economy. It forms the background for the study of data in the business context. The growth of the digital economy is predicting a data rich future and a transformation of the business landscape with implications for existing firm, entrepreneurial ventures, and individual workers. In some of the most dynamic sectors of the modern economy, such as, apps for smartphones, video games, scientific and technical problems solving, Internet of Things, blockchain and crypto technologies, companies’ overall performance already rely on data, setting requiring a whole new set of skills and organizational capabilities. The course will develop the conceptual foundations, frameworks and methods for analyzing the relationships between firms, crowds, and data. It introduces student to the process of digital transformation. The course gives students a systematic basis for assessing the economic potential of different sorts of data and organizational imperatives to unlocking this potential. The first part of the course introduces business models of the digital economy. The latter part will focus on organizing for innovation in a data rich business context. From this point of departure, the course will develop the conceptual foundations, frameworks, and methods for analyzing the relationships between innovation, strategy, and data in a digital context. The focus will be on how to manage and strategize, how existing organizations adapt to increasing digitization of business and development models. Topics will include:

• Digital economy and innovation
• Digital business models and strategy
• Internet of Things
• The role of crowds in generating innovation and predictions.
• Crowdsourcing, collaborative innovation
• Digital platforms, platform markets and platform disruption 
• Managing organizations in a data rich future
• Data networks and markets

The literature can be changed before the semester starts. Students are advised to find the final literature on canvas before they buy any material.

Boudreau K., Jeppesen, LB., 2015, Unpaid Crowd Complementors: The Platform Network Effect Mirage, Strategic Management Journal

Boudreau, K., & Lakhani, K. (2009). How to manage outside innovation. MIT Sloan management review, 50(4), 69.

Cennamo & Santalo 2015 – “How to avoid platform traps”, MIT Sloan Management Review, https:/​/​sloanreview.mit.edu/​article/​how-to-avoid-platform-traps/​

Cennamo et al. (2022) – Assessing Incumbents’ risk of digital platform disruption, MIS Quarterly Executive,

Cennamo, Dagnino, Diminin and Lanzolla (2020) - Managing Digital Transformation: Scope of Transformation and Modalities of Value Co-Generation and Delivery, California Management Review, 

Chesbrough, H. W., & Appleyard, M. M. (2007). Open innovation and strategy. California Management review, 50(1), 57-76.

Dahlander Jeppesen Piezunka 2019 Define, Broadcast, Attract and Select: A Framework for Crowdsourcing, INSEAD Knowledge

Dahlander, L., & Gann, D. M. (2010). How open is innovation?. Research policy, 39(6), 699-709.

Davenport, T. H. (2009). How to design smart business experiments. Harvard business review, 87(2), 68-76.

Evans and Schmalensee (2016) – The business that platforms are actually disrupting. Harvard Business Review 

Goldfarb, Avi and Tucker, Catherine E., Digital Economics (August 2017). NBER Working Paper No. w23684.

Hagui, Andrei & Julian Wright (2020): When data creates competitive advantage… and when it doesn’t. Harvard Business Review, January-February 2020, pp. 94-101.

Iansiti, M., & Lakhani, K. (2020): Competing in the Age of AI. Harvard Business Review. January-February 2020, pp. 3-9.

Israeli, A., & Avery, J. (2017). Predicting consumer tastes with big data at Gap. Harvard Business School Publication (July 10).

Lambrecht, A. & Tucker, C. (2015): Can Big Data Protect a Firm from Competition? Available at SSRN: https:/​/​ssrn.com/​abstract=2705530

Lambrecht, A. & Tucker, C. (2019): Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads. Management Science 65(7):2966-2981. https:/​/​doi.org/​10.1287/​mnsc.2018.3093

Laursen, K., & Salter, A. (2006). Open for innovation: the role of openness in explaining innovation performance among UK manufacturing firms. Strategic management journal, 27(2), 131-150.

Mazzei, M. J., & Noble, D. (2017). Big data dreams: A framework for corporate strategy. Business Horizons, 60(3), 405-414.

McAfee A. & Brynjolfsson, E. (2012): Big data: The management revolution. Harvard Business Review, October 2012, pp 1-9.

Ozalp, Ozcan, Dinckol, Zachariadis, Gawer (2022) – “Digital Colonization” of Highly Regulated Industries: An Analysis of Big Tech Platforms’ Entry into Health Care and Education”, California Management Review, https:/​/​journals.sagepub.com/​doi/​10.1177/​00081256221094307

Parker, van Alstyne and Choudary, 2016, Platform Revolution. Norton &Company, pp: 1-34

Suarez & Kirtley (2012) - "Dethroning an established platform”, MIT Sloan Management Review, https:/​/​sloanreview.mit.edu/​article/​dethroning-an-established-platform/​

Thomke, Stefan, and Jim Manzi. "The Discipline of Business Experimentation." Harvard Business Review 92, no. 12 (December 2014): 70–79.

von Hippel, E. (2005) Democratizing Innovation Chapter 1. MIT Press, Introduction and Overview, in Democratizing Innovation, Cambridge MA: MIT Press: 1-18

von Hippel, E., & Kaulartz, S. (2021). Next-generation consumer innovation search: Identifying early-stage need-solution pairs on the web. Research Policy, 50(8), 104056.