Scopes of intervention and evolutionary paths for argentinian universities transfer offices^*

* This paper would not been written without the long-standing commitment of the ‘Universidad Nacional de Quilmes’ (Argentina) and without the staff of its service of technological transfer.

ABSTRACT

The majority of researches on knowledge and technology transfer from universities focused on the topic of processes which can improve the efficiency of the commercialization of R&D results. Furthermore, the different channels of transfer have been studied mainly independent of each other. This article develops a more strategic approach and proposes an integrated view of 16 channels of transfer organized around four core competences and considering their main form of governance. The empirical analysis is based on data gathered through a survey and an interview on a sample of 29 Argentinian National Universities publicly funded. The findings highlight four types of strategies – integral, network, entrepreneurial and undetermined – and two main types of potential evolutionary paths. The ability to differentiate the scopes of the intervention of universities transfer activities has implications for universities as well as for policymakers.

KEYWORDS:

• University
• knowledge and technology transfer
• transfer strategy
• Argentina

Acknowledgements

The authors wish to express here all their gratitude to the TTO managers for their collaboration. They also acknowledge Pablo Pellegrini and Gustavo Lugones for their very fruitful support.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

* This paper would not been written without the long-standing commitment of the ‘Universidad Nacional de Quilmes’ (Argentina) and without the staff of its service of technological transfer.

1. National Universities are public funded academic institutions which offer free and unrestricted access to higher education.

2. K-Means clustering option of IBM SPSS Statistics software was applied to identify the number of clusters through Likert values, given in survey. This method uses iterative algorithm that finds the representative center point (usually called the centroid) of data points in clusters based on feature similarity by minimizing Euclidean distances between them.