Under Review

(Revise & Resubmit, Strategic Management Journal)Abstract: This study examines the tension over appropriation strategy between firms and scientists, a key human capital. Whereas scientists prefer to publish, firms tend to minimize outgoing knowledge to maintain competitive advantage. This study investigates how a tight labor market, which affords scientists higher bargaining power, can influence firm publications. Using a novel dataset of 200 million job posts and 1.1 million publications from the US Artificial Intelligence (AI) industry, I show that recruitment efforts increase the number of AI publications, but primarily in the same fields of heightened demand. For identification strategy, I exploit the variation in AI exposure at the firm level, which directly influences firm-level demand for AI talents but not AI publications. A machine learning-based approach demonstrates that to balance the trade-off between knowledge leakage and recruiting, firms publish papers that are less commercially valuable. Further mechanism tests on the use of AI research in patents and the science intensiveness of AI patents bolster our theoretical explanation. Findings underscore the importance of human capital in firms’ appropriation strategies.Finalist, Best Paper Award, Israel Strategy Conference, 2022

   (under review, Strategy Science). We use a regression discontinuity design to document which organizations captured more digital value from a major policy shock. 

     Honorable Mention, Organization Theory Division, ASAC, Canada 2019

      Best Paper Award, Strategic IT track, AMCIS, USA 2018  

Working Papers 

        (Target: Management Science). We use synthetic control method and NLP methods to showcase how deep learning was a specific technological disconinuity, which in turn, changed the competitive dynamics in the research field.

Nominated, Best PhD Paper Prize, SMS, 2020     

        Nominated, Research Methods Paper Prize, SMS 2020

Media coverage:   VentureBeat, Scientific American, Axios, Marginal Revolution, The National Security Commission on Artificial Intelligence and Stanford AI Index (2021). 

(Draft available, target: Strategic Management Journal)

I use diff-in-diff to show that political capabilities play an important role in firms' decision to engage in responsible AI research.

(Early draft available. Presented at NBER, Harvard LISH, Northeastern E&I, Columbia MAD conference and at AOM)

Work in Progress

Nominated, Best PhD Paper Prize, SMS, 2020     

Finalist, Max Boisot Award, EGOS, July 2018

     Winner, Lazaridis Institute Entrepreneurship Poster Competition at WLU, June 2018

Dormant projects: 

#GirlPower: Women’s Political Empowerment and Female Entrepreneurship (With M Seong)

      Annual Meeting of the Academy of Management, 2018 

      1st Runner-up, Lazaridis Institute Entrepreneurship Poster Competition at WLU June 2018

Determinants of Firms Nonmarket Strategy: Ownership and Lobbying in Emerging Economies

      Annual Meeting of the Academy of Management, 2017

Research Position

Research Fellow, The Scotiabank Digital Banking Lab, Ivey Business School, October 2018 - June 2021