Research

Review paper covering organizational learning from performance feedback (specifically failure and success) in the context of entrepreneurship across three levels of analysis: indviduals, teams, and organizations.

What enables serial entrepreneurs to improve performance of subsequent ventures following a failed venture? Prior research on serial entrepreneurs tends to highlight the fact that entrepreneurs with failed startups in their history are less likely to found future successful ventures than their peers. However, entrepreneurs themselves and organizational learning research, both, tout the value of failure experience. I resolve the tension between these two sets of findings by establishing a series of boundary conditions within which experience with failed ventures can be an asset to entrepreneurs. In doing so, I aim to contribute to the literature on entrepreneurship by providing evidence that failure can, in fact, be beneficial to entrepreneurs in a similar fashion to failure in other contexts. Additionally, I will contribute to the literature on organizational learning by delineating a series of conditions that enhance the ability of individuals to learn from idiosyncratic failures.

This study seeks to reconcile the contradictory findings of prior research regarding whether it is easier to learn from small or large failures. By examining how failure size and locus of experience interact we will develop a better understanding of the optimal conditions for learning from failure. Relying on theory regarding attribution bias and indirect learning, we hypothesize that entrepreneurs will learn best from their own small failures and from other people's large ones. We test our hypotheses by analyzing the text-based communication data of entrepreneurial teams participating in a university-based accelerator class.

Finalizing IRB approval to distribute survey to large pool of entrepreneurs to investigate the main barriers that underlie the funding gap for minority entrepreneurs seeking venture funding. Will merge data set with a previously collected data set on venture fund raising figures for entrepreneurs of different ethnicities (collected from Crunchbase, Pitchbook, and LinkedIn) .