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Crowding in or crowding out: the link between academic entrepreneurship and entrepreneurial traits

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Abstract

The entrepreneurship literature has identified several entrepreneurial traits as being important to become a successful entrepreneur. Using the Five Factor personality model we analyze differences between two types of entrepreneurs: Individuals founding an enterprise out of university employment and graduates who are not employed at the university before starting a company. To analyze potential differences in personality between these two groups we use a unique data set of former students from a large German university. We show that entrepreneurs out of the university context possess lower levels of openness to experience as well as higher levels of agreeableness. Also, we provide evidence for the importance of the predominant type of knowledge upon which academic ventures are built. The findings confirm that entrepreneurs out of the university context overly focus on the scientific aspects of their start-up idea and thus may pursue it in a potentially suboptimal manner, but that this can be mitigated by dedicated support measures and structures within the university, for which we also provide specific examples.

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Notes

  1. Four of the founders already founded a company and are in the process of founding another company. For these, data will only be used for the first company founded.

  2. Also, we estimated a logistic regression model with the dependent variable of being an entrepreneur or not. Results yield a positive significant influence of the dimension extraversion on the probability to engage in entrepreneurship. Furthermore, we find a significant positive influence of economics as the study field. Results of these additional estimations are available upon request from the authors.

  3. We estimate predicted probabilities and provide the results and the number of correctly identified entrepreneurs in Table 6, finding 60 of 65 cases being correctly specified, which strongly supports our model. Thus sample size seems not an issue in the analysis.

  4. We also use Kernel Matching method and receive qualitatively the same results. Kernel Matching uses all observations of the control group and weights the distance between the propensity scores inversely whereas Nearest Neighbor Matching searches the nearest observation of each treated unit in a forward and backward exploration. For both methods we excluded observations outside the common support region. Results of the Kernel Matching method are available upon request.

  5. Results of the additional estimations are available upon request from the authors.

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Acknowledgments

Previous versions of this paper were presented at the Technology Transfer Conference 2011 and at the Workshop on Academic Policy and the Knowledge Theory of Entrepreneurship 2012 both at the University of Augsburg. We are grateful to two anonymous reviewers and the editor Erik Lehmann for useful comments that much improved the paper.

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Authors and Affiliations

  1. Chair in Entrepreneurship and Management, Department of Business Administration and Economics, University of Wuerzburg, Stephanstr. 1, 97070, Wuerzburg, Germany

    Cornelia Kolb & Marcus Wagner

  2. Service Center for Research and Technology Transfer, University of Wuerzburg, Sanderring 2, 97070, Wuerzburg, Germany

    Cornelia Kolb

  3. Bureau d’Economie Théorique et Appliquée, Université de Strasbourg, 61 Avenue de la Fôret Noire, 67000, Strasbourg, France

    Marcus Wagner

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  1. Cornelia Kolb
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  2. Marcus Wagner
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Correspondence to Cornelia Kolb.

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Table 8 Variable descriptions and items in the questionnaire
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Kolb, C., Wagner, M. Crowding in or crowding out: the link between academic entrepreneurship and entrepreneurial traits. J Technol Transf 40, 387–408 (2015). https://doi.org/10.1007/s10961-014-9346-y

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