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.
The present project, from one side tries to fill the gaps highlighted above, by introducing different novelties for basic and applied research. In particular:
-it attempts to build a dataset as rich as possible, including spin offs from different European countries;
-it proposes a dynamic approach, trying to understand the factors that allow spin off to survive and grow in different stages of life cycle. Specifically, it considers a large number of external and internal factors. All the internal variables (whom proxy are suggested by the literature) are going to be fitted into the following different areas of the Canvas business model: Infrastructures (Key Resources, Key Activities, Partners Network), Value Propositions,Customers (Customer Segments, Channels, Customer Relationships), Financials. Each variable has one or more proxies. The project is going to consider also the spin offs¿ performance such as: the annual employees¿ growth rate, the annual revenue growth rate, the annual sales growth rate, the annual ROI growth rate, the annual ROE growth rate. These values are going to be collected for the period 2003- 2016. Among the external factors, the dataset includes both micro and macro level variables. Micro level variables are the following: Parent institutions¿ characteristics, Academic incubators, Technology transfer office. Macro level variable are related to the characteristics of the region where the spinoff is located.
-it attempts to explore which is the winner business model through the different stages of spin offs¿ life cycle, related to firms¿ performance.
-It proposes an innovative tool if compared to the presrnt It proposes a new tool: the AMRSO 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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