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 to 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 as 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 – it reduces the odds of pursuing projects with false positive returns, and increases the odds of pursuing projects with false negative returns.

A scientific approach to entrepreneurial decision making: evidence from a randomized control trial

Camuffo, Arnaldo;Cordova, Alessandro;Gambardella, Alfonso;SPINA, CHIARA
2020

Abstract

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 to 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 as 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 – it reduces the odds of pursuing projects with false positive returns, and increases the odds of pursuing projects with false negative returns.
2020
2019
Camuffo, Arnaldo; Cordova, Alessandro; Gambardella, Alfonso; Spina, Chiara
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4013977
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