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The Promise and Potential Perils of Using Machine Learning Techniques in Management Research
Please join us for this special panel presented by the Entrepreneurship & Strategy Interest Group as a preview of this year's SMS Annual Conference program.

The abundance of data, the explosion of computing power, and the availability of open-source tools have opened up new opportunities for strategy and entrepreneurship scholars. Management scholars have started to exploit the latest machine learning techniques to develop and test new theories. This session will bring together leading management scholars, who are using the most recent machine learning techniques (e.g., Word Embedding, Hierarchical Dirichlet Process topic modeling) to build new constructs and push the theoretical boundaries of the field. Using examples from both strategy and entrepreneurship, these scholars will showcase how the latest techniques open up new opportunities for researchers. Further, this session will start a conversation around how to combine machine learning with causal inference. Finally, we will discuss the challenges associated with the peer-reviewing process when scholars use lesser-known but advanced methods.

Panelists: Riitta Katila, Stanford University; Florenta Teodoridis, University of Southern California; Jorge Guzman, Columbia University, and Jamie Song, ESMT

This session is a benefit of SMS Membership and is open to all 2021 and 2022 Members of SMS. If you are not a member, you can learn more here: https://strategicmanagement.net/home/members/overview

Aug 18, 2021 01:00 PM in Eastern Time (US and Canada)

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