Abstract
Over the past decades, university-industry relationships have become an important subject due to the essential role played by technological progress in the economic development of countries. From a theoretical point of view, several studies have shown the close relationship between investments in research and innovative activities of universities and the economic growth of specific territories. Indeed, the strong linkages between universities and a country’s production system encourage the process of technology transfer and the commercial use of the research results. For this reason, the European Union has implemented a series of measures to promote the adoption of research findings in the real economic and social context, strengthening the linkages between universities, industries and government. As a starting point for enhancing this link, specific mechanisms have been devised by universities. In particular, technology transfer offices (TTOs) have been created to stimulate and encourage the dissemination of the research outcomes, translate them into practise, and facilitate their interrelations with the other two agents of the innovation systems: industries and government. Within this context, the present paper aims to gain knowledge on the determinants of spin-off creation in Italy with special attention to the role played by university TTOs. Specifically, an econometric probability model has been built merging the extant literature into four distinct strands. The analysis, based on the NetVal indicators and primary data survey, has allowed us to assess the Italian experience at an aggregate and disaggregate level.
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Notes
There is no standard definition of a spin-off. A narrow definition considers a spin-off as any new firm that includes a public sector or university employee as a founder. A broader meaning includes employee founders, licensees and firms in which the institution holds equity. The broadest definition comprises employees, licensees, equity, students/alumni, incubator firms, and other.
Netval is the Italian University Network for the Valorisation of Research (see http://www.netval.it).
The linear trend has an equation y = −0.0267x + 0.4476 with a R2 = 0.8986.
Making an international comparison, Italy shows nearly the same performance in terms of new spin-offs (110 spin-offs created in 2008) as Spain (102) and Japan (140), but its policy of creating new firms from the public research base has been less active than China, the U.S. and the U.K. By 2008, excluding China, whose statistics are not directly comparable due to aggregation problems, the U.S. is the leader in establishing new research spin-offs (555). Among these 555 spin-offs, several have developed a very high-profile and have become very successful; for instance, Silicon Graphics, Genentech, Hewlett Packard, Polaroid and the Internet search engine Google—many of which originated at Stanford University—are all examples of university start-ups, which have helped to attract new students, faculty, and funding (OECD 2009). Trailing the U.S. is the U.K with 256 spin-offs.
In detail, the analysis of the percentage quotas of sector activities shows that in 2009, 33% of research-based spin-offs are devoted to ITC, 16% to energy and environment, 15% to life science, 10% to electronics, 7% to bio-medical research, 7% to innovation services, 6% to factory automation, 5% to nanotechnology, 3% to cultural heritage goods and 1% to aero spatial engineering (own elaborations on NetVal data).
The number of national and international patents, indicating formal technology transfer, was initially considered as an additional explanatory variable. However, it has not been included in the final model since the correlation matrix showed high correlation between the number of patents and the number of students and the number of patents and age.
The logistic regression has been expressed in its exponential form, since there is a disadvantage in using a linear form. Namely in the latter case the maximum likelihood estimates are expressed in a logit scale and therefore are not directly interpretable as probability.
The Wald test, which calculates a statistic z = β^/ SE, is used to test the significance of each coefficient in the model. The squared value of z provides the Wald statistic with a Chi-square distribution.
We have also considered non-linear effects associated with AGE, EMPLOYEES and SIZE by including the quadratic form of the mentioned variables. When we run the logit regression, only EMPLOYEES2 was significant at 1% level, while neither AGE2 nor SIZE2 were significant. However, when we computed the marginal effects after logit, none of the three quadratic explanatory variables was significant.
This is the likelihood ratio Chi-square with 10 degrees of freedom. One degree of freedom is used for each predictor variable in the logistic regression model. The likelihood ratio Chi-square is defined as 2(L1 − L0), where L0 represents the log likelihood for the constant-only model and L1 is the log likelihood for the full model with constant and predictors.
For each explanatory variable the marginal effect has been calculated by computing the difference between the probability of success including all the predictors and the probability of success excluding the considered explanatory variable.
The average number of full-time employees per TTO increased by 36.7% between 2003 and 2008 (Netval Report 2010).
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Acknowledgments
We are grateful to the comments and suggestions offered by the Editor-in-Chief of this Journal Al Link and two anonymous referees. We would like to thank the participants at the European Network on Industrial Policy (EUNIP) Conference (“Evaluating Innovation Policy: Methods and Applications”), held in Florence, Italy, 5–6 May, 2011 for their helpful comments on an earlier draft of this paper. Financial support from the Region of Calabria (Scientific Research Program CALCOM on “Regional Competitiveness and Innovation”) is grateful acknowledged.
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Algieri, B., Aquino, A. & Succurro, M. Technology transfer offices and academic spin-off creation: the case of Italy. J Technol Transf 38, 382–400 (2013). https://doi.org/10.1007/s10961-011-9241-8
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DOI: https://doi.org/10.1007/s10961-011-9241-8