Assessment models for Research Spin offs (ARSOs)

Proponente Francesco Ricotta - Professore Ordinario
Sottosettore ERC del proponente del progetto
Componenti gruppo di ricerca
Componente Categoria
Enrico Massaroni Componenti il gruppo di ricerca
Componente Qualifica Struttura Categoria
Mariacarmela Passarelli PHD Unical Altro personale Sapienza o esterni

Research spin offs (RSOs) is a mechanism of exploitation and transfer of value of scientific knowledge produced in research institutions (Wright et al. 2004). Despite the high technological profile of most of these university start-ups, many of them have not been very successful in terms of survival and growth rate. Even if this is a crucial issue among academic entrepreneurs, experts and policy makers, few studies, in the literature, have focused both on the survival determinants of research spin off and on the main factors that encourage spin offs growth (Nerkar and Shane 2003; Zhang 2009; Galati et al., 2017). The literature lacks studies that concentrates on spin offs¿ business model and on the evolution of the business model across all the spin offs¿ life cycle (Sindakis and Carayannis, 2017). From the other side, there is the necessity for RSOs manager, for technology transfer experts and from policy makers, to have available tools to assess the science based enterprises in order to enhance their successful drivers. Thus, stemming both from the existing literature and the practioneers¿ needs, the present project has the aim to generate an assessment model for research spin off (ARSO), by identifying successful business factors for RSOs, in different stages of life cycle. This is a dynamic approach.It proposes a new tool: the ARSO that will be useful for different stakeholders. Specifically, to the academic entrepreneurs the results will suggest which strategies they have to apply in order to succeed, to the institutions they will give hints on the best support they can offer to the spin offs; policy makers can get important suggestions for innovation and technology transfer policies in terms of financial supports, entrepreneurial programs, technology transfer programs, etc.Moreover, the rich dataset will allow to develop many future researchers on other interesting topics related to the spin offs.


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