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• Case | HBS Case Collection | May 2017 (Revised November 2018)

  Talent@Tencent

  Tarun Khanna, Nancy Hua Dai and Shu Lin

  On November 11, 2016, Xi Dan, Senior Vice President of Tencent, and Ma Yongwu, Dean of Tencent Academy, were discussing how Tencent could develop new capabilities to sustain its growth miracle and entry into new technologies, expansion into B2B businesses, and internationalization.

  Keywords: Talent and Talent Management; Training; Emerging Markets; Entrepreneurship; Globalization; Human Resources; Innovation and Management; Leadership Development; Business Strategy; Technology; Internet; China;

  Citation:

  Khanna, Tarun, Nancy Hua Dai, and Shu Lin. "Talent@Tencent." Harvard Business School Case 717-500, May 2017. (Revised November 2018.)  View Details

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• Case | HBS Case Collection | October 2018

  Corruption and Business in Emerging Markets

  Geoffrey Jones, Tarun Khanna and Nataliya Langburd Wright

  Citation:

  Jones, Geoffrey, Tarun Khanna, and Nataliya Langburd Wright. "Corruption and Business in Emerging Markets." Harvard Business School Case 319-054, October 2018.  View Details

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• Working Paper | HBS Working Paper Series | 2018

  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. Thus researchers could use such methods to explore novel and robust patterns in data, which could in turn lead to inductive theory building. ML’s strengths include replicable identification of novel patterns in data. ML methods also address several issues raised by scholars (such as “p-hacking” and confounding local effects with global effects) pertinent to the norms of empirical research in the fields of strategy and management. We provide 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 can illuminate interactions and non-linear effects better than traditional methods. In summary, ML methods could serve as a complement 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. (Revised October 2018.)  View Details

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