A classical approach to collecting and elaborating information to make entrepreneurial decisions combines search heuristics such as trial and error, effectuation, and confirmatory search. This paper develops a framework for exploring the implications of a more scientific approach to entrepreneurial decision making. The panel sample of our randomized control trial includes 116 Italian startups and 16 data points over a period of about one year. Both the treatment and control groups receive 10 sessions of general training on how to obtain feedback from the market and gauge the feasibility of their idea. We teach the treated startups to develop frameworks for predicting the performance of their idea and to conduct rigorous tests of their hypotheses very much like scientists do in their research. We let the firms in the control group, instead, follow their intuitions about how to assess their idea, which has typically produced fairly standard search heuristics. We find that entrepreneurs who behave like scientists perform better, pivot to a greater extent to a different idea, and do not drop out less than the control group in the early stages of the startup. These results are consistent with the main prediction of our theory: a scientific approach improves precision â? it reduces the odds of pursuing projects with false positive returns, and raises the odds of pursuing projects with false negative returns.
Enterprise revenue, dropout rate of firms / entrepreneurs and the number of pivots to business models.
The research finds that entrepreneurs that behave like scientists perform better, pivot their ventures to larger extents, and do not dropout less than the control group in the early stages of the startup.