Assistant Professor of Business Administration
Pian Shu is an Assistant Professor of Business Administration in the Technology and Operations Management Unit. She teaches the Field Immersion Experiences for Leadership Development II (FIELD II) course in the MBA required curriculum. She received the Berol Corporation Fellowship from the Harvard Business School in July 2013.
Professor Shu’s research focuses on the empirical analysis of factors that affect innovation and productivity at the micro level. She contributes to the field by taking a labor economics perspective and investigating the decisions of individuals. Her current research examines how talented individuals develop into innovators, the impact of early career choices on long-term productivity, the uncertainty associated with becoming an innovator through entrepreneurship, and the impact of technology and trade shocks on innovation.
A recipient of the Kauffmann Dissertation Fellowship in Entrepreneurship, Professor Shu earned her Ph.D. in economics at the Massachusetts Institute of Technology. She graduated from Colgate University with a BA in mathematics and mathematical economics.
Are "Better" Ideas More Likely to Succeed? An Empirical Analysis of Startup Evaluation
Entrepreneurs face high uncertainty, and often make costly investments in new business ideas without knowing the expected payoff. This paper empirically examines whether ex-ante assessment of early-stage startup ideas can predict their subsequent commercialization. We leverage an entrepreneurship program at the Massachusetts Institute of Technology in which early-stage venture ideas, presented in the form of succinct standardized summaries, elicit subjective evaluations from a large set of experienced entrepreneurs and executives. Using data on 652 ventures in multiple industry sectors, evaluated over an 8-year period, we find that ideas that elicit more positive evaluations are significantly more likely to ultimately reach commercialization. We further show that these results are driven by venture ideas with documented intellectual capital in research-and-development-intensive sectors, such as life sciences and medical devices. We find no evidence, by contrast, that experts can effectively assess the commercial potential of venture ideas in non-R&D-intensive sectors such as consumer web and enterprise software. Finally, we find that industry-specific and scientific expertise is not critical to experts’ collective ability to predict ventures’ commercial viability.