My research focuses mainly on two questions:  how reputation and reputation concerns affect managerial decision-making under uncertainty, and how winning awards and grants change people’s career behaviors. My research provides implications on how to improve the quality of managerial decisions, and how to effectively utilize awards and grants for better organizational outcomes. My studies leverage various datasets including analysts’ awards and entrepreneurial activities, startup funding and VC fundraising activities, and scientific lab funding and research production.

In my first line of work, I study when decision-makers herd and knowingly put their personal reputational concerns before the interests of the firm. My research explores how, under some conditions, reputational concerns can give incentives to go against their own personal judgment due to a fear of being uniquely wrong. The specific questions my research aims to answer are: (1) which decision-makers herd? (2) among whom and under which conditions herding might lead to inferior performance? and (3) can inefficient herding be reduced to improve decision quality? I currently study the phenomenon in both equity research and the venture capital industry. 

My second line of work examines how winning awards or grants change behaviors and provides insights on how organizations should utilize these tools. I examine: (1) whether winning awards increase professionals’ mobility including starting their own business? (2) whether awarded researchers explore outside their field or exploit within it. I study the questions in equity research and scientific lab settings.

Working Paper

“Revisiting Zuckerman's (1999) Categorical Imperative: An Application of Epistemic Maps for Replication" (with Brent Goldfarb, 3rd round Review at Strategic Management Journal )

"Reputation and Herding"

I study when managers may knowingly make poor decisions. Specifically, I posit "reputational herding," whereby decision-makers herd to avoid being uniquely wrong even when they know this will make them less likely to be correct. I model this phenomenon formally and show that decision-makers will be less likely to herd when they have higher reputations and when experts have less correlated information. The model provides predictions that distinguish between learning and reputational herding. The theory is tested in the context of sell-side stock analysts. A difference-in-differences estimation compares award-winning analysts and runners-up with similar ability to identify the causal impact of a change in reputation on the likelihood of herding. The results suggest that analysts herd less after an increase in reputation, which is consistent with the reputational herding mechanism.

Work In Progress

“Reputation and Entrepreneurship: An Examination of Award-Winning Analysts' Careers”

Writing stage.

“The Durability and Fragility of Reputation”(with Kalinda Ukanwa)

Model developed. Collecting empirical evidence.

“Booms, Busts, and Research Lab Productivity” (with Waverly Ding and Chris Liu)

Data collected. 

“Do Sheep Make Unicorns? A Study of VC Herding and Entrepreneurial Crowding” (with Jiayi Bao)

In data analysis.

“To Explore or Exploit? The Effects of Winning Awards on Sell-Side Equity Research” 

In data analysis.

“External Ratings and Mutual Fund Herding” (with Jinming Xue)

In data collection.