Abstract

Education is the main activity for universities but the innovations resulting from their research become more and more a prominent and very lucrative business for them. University research and its transfer to industry has been a topic of interest in the management of technology literature over decades and several researchers focused the performances of university TTOs and many metrics have been proposed during last years . The primary role of a TTO is to manage and perform technology transfer activities (AUTM 2004), but how to control and monitoring the performance of university TTOs? In literature there are many studies regarding this theme, but many of these focused on the analysis of the driving forces of TTO performances that may help policy makers and university managers to improve technology transfer process (Hulsbeck et al., 2013), while in this paper, the approach to this theme regards the use of operative tools to control and monitoring the performance of university TTOs. TT managers oriented to use statistical tools, as a control chart, here proposed, to do this, face with an operative problem related the small samples of TT available data that can generate bias of the process only owned to this condition and not as a consequence of a bias really occurred. In this paper, to overcome this problem, opportune graphs and tables, can be used by TT managers, are proposed to determine a reasonable number of subgroups of available TT data, for constructing suitable control limits. Hulsbeck et al., (2013) used the number of invention disclosures as a performance measure, to analyze how variance in performance can be explained by different organizational structures and variables of TTO. In this paper we refer to the same performance measure to be monitored. This proposed model and solution may be appealing to managers and technology transfer agents since the graphs and tables proposed could be reproduced in a number of standard optimization software.

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