The Entrepreneurial Profile after Failure

ISI: The Entrepreneurial Profile after Failure


2019 - International Journal of Entrepreneurial Behavior & Research


Abstract Purpose: The objective of this article is to examine the profile of entrepreneurs who failed in previous businesses, but despite their initial failure, decide to start or maintain another business. Based on the literature, re-entrepreneurs present some unique characteristics that allow them to overcome and learn from their previous ventures. Moreover, following the institutional dimensions literature, the work shows these dimensions should also affect the prevalence and type of re-entrepreneurial profiles.   Methodology: Traditional ANOVA and logistic regression analysis were used as a way to test several hypotheses. Moreover, decision trees were implemented to provide further insights with respect to the characteristics of the relationships under study. Data from the Glo­bal Entrepreneurship Monitor Survey (GEM, Adult Population Survey 2016 for Chile) is the main source of information.   Findings: Results show that for Chilean entrepreneurs, there is a different profile between those who fail and abandon and those who decide to start a new business after failing. These differences show that post-failure entrepreneurs behave like angel investors, have better business skills and are more experienced. Additionally, they are more likely to share ownership of their new business.   Value: This work contributes to the literature with empirical results that validate that failure determines special characteristics in entrepreneurs through a methodology that is relatively new in the community. Also, discuss how institutions (institutional theory) could play a role in this phenomenon. This can help decision makers and the academic community better understand the nature of this type of entrepreneur and thus provide insights for more focused public policies and better-informed business decisions.


Keywords: Exit, Entrepreneur Profile, Institutional Dimensions, Decision Trees.    JEL Classification L26.

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