Ryan Thomas Allen - Faculty & Research - Harvard Business School
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Ryan Thomas Allen


Doctoral Student

Ryan is a doctoral student in Harvard Business School’s Technology and Operations Management unit. He specializes in the areas of innovation and technology strategy. Recently his research has focused on Artificial Intelligence: the process by which new AI technology is produced, how it is adopted in incumbent organizations (and to what effect), and how management and economics researchers can better use tools from machine learning to conduct empirical and exploratory research. He is also interested in studying the emergence and influence of Chinese technology companies.

Prior to joining HBS, Ryan worked for Amazon in the Amazon Business division, did strategy consulting for L-3 Technologies, and managed an international team for several years at a nonprofit dedicated to building positive online content. He graduated from Brigham Young University summa cum laude with a BS in Economics and minors in Mathematics and Strategy. As an undergraduate he collaborated with his professors to publish several environmental health papers, and developed working papers in both strategy and behavioral economics. Ryan also spent two years living in Taiwan as a full-time service missionary for the Church of Jesus Christ of Latter-day Saints. Outside of work, Ryan enjoys spending time exploring Boston with his wife and two children.

Working Papers
  1. Developing Theory Using Machine Learning Methods

    Prithwiraj Choudhury, Ryan Allen and Michael G. Endres

    We describe how to employ machine learning (ML) methods in theory development. Compared to traditional causal inference methods, ML methods make far fewer a priori assumptions about the functional form of the underlying model that best represents the data. Given this, researchers could use such methods to explore novel and robust patterns in the data that could lead to inductive theory building. ML strengths include replicable identification of novel patterns in the data. Additionally, ML methods address several concerns (such as ‘p-hacking’ and confounding local effects for global effects) raised by scholars relative to the norms of empirical research in the fields of strategy and management. We develop a step-by-step roadmap that illustrates how to use four ML methods (decision trees, random forests, K-nearest neighbors and neural networks) to reveal patterns in data that could be used for theory building. We also illustrate how ML methods could better illuminate interactions and non-linear effects, relative to traditional methods. In summary, ML methods could act as a complementary tool to both existing inductive theory-creating methods such as multiple case inductive studies and traditional methods of causal inference.

    Keywords: ""Machine learning, theory building, induction, decision trees, random forests, k-nearest neighbors, neural network, p-hacking";

    Citation:

    Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Developing Theory Using Machine Learning Methods." Harvard Business School Working Paper, No. 19-032, September 2018.  View Details