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Academic spin-offs’ entrepreneurial teams and performance: a subgroups approach

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Abstract

Academic spin-offs’ entrepreneurial teams generally concentrate high levels of research and development experience while they are often found lacking in commercial skills. This prompts the integration of surrogate entrepreneurs (practitioners) but the literature questions the effectiveness of these artificially created teams. We argue that faultline theory applied to this context of different identity-based subgroups in a team can provide important insight, and complement the traditional approaches to top team diversity such as upper echelons theory. Our research compares the impact of the three main possible academic spin-off entrepreneurial team configurations on the two principal success-related tasks, innovation and sales, and considers the role important stakeholders, such as public research institutions and industrial partners, can have. In a sample of 164 academic spin-offs, we show that certain configurations are more suited to certain objectives and that faultline theory does indeed contribute to better our understanding of the entrepreneurial teams’ outcomes. Furthermore, the expectations one might have with regard to the extended entrepreneurial team as a possible remedy for weak core team configurations are not supported by our data. Implications for theory and practice are provided.

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Notes

  1. As an anonymous reviewer noted, we acknowledge that this may however impact on measures of innovation from ET configurations involving fewer or no academics.

  2. Since we measure innovation by the total number of patents and licenses for each ASO from its inception and ASOs have different ages, direct comparison could be misleading and modeling the number of patents and licenses per unit of time (i.e. per year) is more relevant. For example, two patents over 1 year should not amount to the same as two patents over 5 years. In our Poisson regression model, this potential issue is handled by including the ASO age as an offset variable. See Hilbe (2011) for additional details.

  3. Two other common remedies for over-dispersion imply the estimation of other models such as the quasi-Poisson regression model (in which an additional dispersion parameter is included in the model) and the negative binomial regression model (which is an alternative model for count data). See Zeileis et al. (2008) for additional details.

  4. Unreported variance inflation factor (VIF) analysis reveals that multicollinearity is not a problem in our application.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and the Guest Editors James Cunningham and Paul O’Reilly for their comprehensive work, as well as participants of the Dublin 2015 Technology Transfer Conference for their feedback on an earlier draft of this paper.

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Authors and Affiliations

  1. IÉSEG School of Management (LEM-CNRS), Socle de la Grande Arche, 1 Parvis de La Défense, 92044, Paris La Défense, France

    Cyrine Ben-Hafaïedh

  2. Università E-Campus, Via Isimbardi, Novedrate, Italy

    Alessandra Micozzi

  3. Center of Innovation and Entrepreneurship, Università Politecnica delle Marche, Via Brecce Bianche, 1, Ancona, Italy

    Alessandra Micozzi

  4. Department of Management, University of Bologna, Via Capo di Lucca 34, 40126, Bologna, Italy

    Pierpaolo Pattitoni

  5. The Rimini Centre for Economic Analysis (RCEA), Rimini, Italy

    Pierpaolo Pattitoni

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  1. Cyrine Ben-Hafaïedh
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Correspondence to Cyrine Ben-Hafaïedh.

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Ben-Hafaïedh, C., Micozzi, A. & Pattitoni, P. Academic spin-offs’ entrepreneurial teams and performance: a subgroups approach. J Technol Transf 43, 714–733 (2018). https://doi.org/10.1007/s10961-017-9623-7

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