“Data-enabled learning, network effects and competitive advantage” by Dr. Andrei Hagiu
Dr. Andrei Hagiu
Associate Professor of Information Systems
Questrom School of Business
We provide a model of competition when firms can improve their products through learning from the data they obtain on customers they serve. The model is used to explore the implications for competitive dynamics of three new features of such learning compared to traditional learning-by-doing settings: (i) learning increases a firm’s demand rather than reducing its marginal cost, (ii) firms can improve their products for individual customers based on each customer’s particular usage experience, and (iii) the learning happens while a firm’s customers are still consuming the product. We show when and how network effects arise from these new features. We also analyze the role of consumer beliefs, the nature of the learning curve, and other factors that affect an incumbent’s competitive advantage.
Dr. Hagiu will also discuss how the authors write a HBR article for practitioners based on this project.