Human capital of entrepreneurial teams in nascent high-tech sectors: a comparison between Cleantech and Internet

ABSTRACT

We use an inductive approach to understand what types of founders’ human capital, at individual and team levels, are necessary to recognise and exploit entrepreneurial opportunities. A sample of 195 founders who teamed up in the nascent phases of Cleantech and Interned-based sectors is analysed. The results suggest a twofold moderating effect of the sectoral context. First, a more hard science-based and complex sector like Cleantech demands technically more skilled entrepreneurs, but at the same time, it still requires fairly commercially experienced and economically competent individuals. Furthermore, the business context also appears to exert an important influence on team formation dynamics: individuals are more prone to team-up with cofounders possessing complementary know-how when they are starting a new business venture in the Cleantech rather than in the Internet-based sector. Overall, these results stress the role of the specific high-tech business context at stake when analysing entrepreneurial team composition patterns.

KEYWORDS:

• Entrepreneurial team composition
• founders’ human capital
• business context
• homophily
• functionality

Acknowledgements

The authors wish to thank two anonymous referees for their useful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Paola Garrone is Full Professor of Business and Industrial Economics at the Politecnico di Milano.

Luca Grilli is Associate Professor of Business and Industrial Economics at the Politecnico di Milano.

Boris Mrkajic is a Postdoctoral Research Fellow at the Politecnico di Milano.

Notes

1. We do not consider here another interesting aspect which is the motivational forces behind the choice of creating the entrepreneurial venture, and how these interact with entrepreneurial team formation and the human capital of founders. We have only partial information about these aspects, where it has been difficult to ex-post collect reliable ex ante information about the motivations of entrepreneurs at time of founding, due to likely cognitive and retrospective biases at work. However, we did not find any strong correlation between specific motivations and the investigated human capital dimensions that are the object of our study. Therefore we do not expect this omission to seriously interfere with the dynamics highlighted in our study.

2. For more details regarding VICO dataset, see Bertoni and Marti (2011).

3. Further details can be found in NuWire Investor (2008).

4. To keep the complexity of the analysis to a manageable degree we do not distinguish in this case, and in the subsequent one on complementary measures, the different typologies of disciplines and functions across education and experience.

5. The chosen clustering procedure is the partitioning method, that is, the k-means method, which is superior to the other methods (e.g. hierarchical) as it is less affected by the presence of outliers and irrelevant clustering variables, and less computationally demanding (Mooi and Sarstedt 2011). The usual problem associated with the application of k-means, which relates to the fact that the number of clusters needs to be pre-specified, is overcome by definition in our context. The clustering variables are normalized in order to have a fair participation of all the variables, regardless of their scale. Finally, the recommended sample size of at least $2^n$, where n equals the number of clustering variables, is given by Mooi and Sarstedt (2011). Having 195 observations in our analysis makes us hence comfortable to use up to seven clustering variables.

6. Furthermore we also explore dimensions such as serial entrepreneurial activity and prior managerial experience on which we have information only for a sub-sample of entrepreneurs. This additional analysis yields no differences between the two sectors along the additional dimensions.