Outsourcing R&d Buying R&dservicesfromotherorganizations, suchas universities, publicresearchorganizations, commercial engineers orsuppliers. Inward IP licensing Buying orusingintellectualproperty, suchaspatents
Trends, motives and management challenges Vareska van de Vrande*RSM Erasmus University E-mail: vvrande@rsm. nl Jeroen P. J. De Jong EIM Business and Policy Research E-mail:
jjo@eim. nl Wim Vanhaverbeke Hasselt University, Faculty of business Studies E-mail: wim. vanhaverbeke@uhasselt. be Maurice de Rochemont Eindhoven University of Technology E-mail:
m d. rochemont@tm. tue. nl February 2008*Corresponding author Vareska van de Vrande RSM Erasmus University Department of Strategic Management and Business
Environment Room T7-33 P o box 1738,3000 DR Rotterdam, The netherlands T:++31 10 408 2208, F:+
In addition to internal R&d, established companies need to get access to external knowledge, such as startups, universities, suppliers,
In addition, more and more SME firms are entering into research collaborations with universities (e g. George et al. 2002).
) Additionally, interaction with suppliers & customers can provide missing external inputs into the learning process
Part of current movement towards open innovation is related to a different approach of universities, research labs and companies vis-à-vis technology and IP.
public knowledge centers (e g. universities), customers, suppliers, and investors (e g. banks, venture capital firms). 20 Finally, we looked at the degree firms participate by equity investments in new or existing companies,
The proximity of universities, research labs, large companies and lead users may play a role in the deployment of open innovation in SMES.
Harvard Business school Press: Boston, MA. Chesbrough, H.,2006. Open business models: How to thrive in a new innovation landscape.
Harvard Business school Press: Boston, MA. Chesbrough, H.,Crowther, A k.,2006. Beyond high tech: early adopters of open innovation in other industries.
Harvard Business school, Boston: MA. Christensen, J-F.,Oleson, M. H.,Kjaer, J. S.,2005. The industrial dynamics of Open innovation Evidence from the transformation of consumer electronics.
Uneasy Partners in the Cause of Technological 40 Advance, in Challenges to the University, Brookings Institution Press, WASHINGTON DC.
Factors affecting university industry R&d projects: The importance of searching, screening and signaling. Research Policy 35,309 323.41 Foxall, G r.,Johnston, B.,1987.
The effects of business-university alliances on innovative output and financial performance: A study of publicly traded biotechnology companies.
evidence from Dutch SMES H200703 26-1-2007 Family orientation, strategy and organizational learning as predictors of knowledge management in Dutch SMES H200702 3-1-2007
Outsourcing R&d Buying R&dservicesfromotherorganizations, suchas universities, publicresearchorganizations, commercial engineers orsuppliers. Inward IP licensing Buying orusingintellectualproperty, suchaspatents
Flanders DC focuses on entrepreneurs, teachers, students, policy-makers and the general public. Among the many options Flanders DC offers are:
or at your event, take part in the De Bedenkers (The Inventors) classroom competition and an online game to discover how you score as an innovative manager.
July 2007, published in English How entrepreneurial are our Flemish students, Hans Crijns and Sabine Vermeulen,
Ondernemen. meerdan. ondernemen, an online learning platform Creativity Class for young high-potentials Flanders DC Fellows, inspiring role models in business creativity Creativity Talks, monthly
At the end of Chapters 2 to 6, we include key learning points. These lists of learning points can be consulted as a checklist
when you are setting up a new business with your innovation partners. These learning points are gathered at the end of each chapter
so you can easily check them whenever you want a quick review of what you have learned 1. 3. Research method To explore the link between open innovation and market success of SMES,
including the Glostrup Hospital of the University of Copenhagen. These contacts introduced the founders to the science of sleep and the clinical practice of sleep medicine.
cross-industry learning process led by sleep experts. The QOD case illustrates that developing a successful business model that ultimately changes the industry starts with nothing more than the conviction of a well-informed entrepreneur.
In this case, most technologies are developed co with knowledge partners such as universities, research labs, and lead-customers.
and technologies developed at universities, research labs, or large companies. Finally, small firms must make choices 32 about the way they will profit from their technology.
Dingens wanted to collaborate with the University of Hasselt and knowledge partner Sirris to develop a completely new instrument The new barometer should have the same advantages of the mercury barometer (accurate, legible, durable,
therefore, developing technology based business opportunities should no longer be limited to university and corporate spin-offs. Start-ups can use their organizational agility, application know-how,
or market intelligence to commercialize technologies that they license from universities or larger, technology-savvy companies.
The experience eventually transforms the customer into a restyled person using personalized advice from a professional. 34 Key Learning points Analyzing open innovation in SMES in traditional industries starts with conceiving
such as a learning innovation network, design networks, research programs, and so on. Design was the second step.
Examples include newsletters from universities and knowledge centers and publications of Design Vlaanderen among others.
It is thus too early to evaluate its effects on the company's bottom line. 53 Key learning points Successful SMES do not remain with one business model forever.
and a few additional knowledge partners such as universities, research labs, and knowledge intermediaries. This strong reliance on value chain partners is partially due to the fact that most companies are active in low-and medium-tech industries.
Therefore, they visited several renowned sleep institutes located in Danish hospitals such as the Glostrup Hospital of the University of Copenhagen.
where universities would be invited to participate in the product days with their own ideas. They would also have access to factory resources
and stay focused on the joint value they create. 74 Key Learning Points Open innovation as an integral part of business model innovations In the past,
Universities, research labs, crowds of experts, lead users, and knowledge brokers are just a few examples of potential external sources of knowledge.
Developing new flavors has traditionally been completed with different universities in Europe, with DSM, and with other innovation partners.
including several European universities, research labs, DSM and other value chain partners. The technology licensed from DSM is a technological platform that can be used for different applications.
it could build on the reputation of DSM to get access to universities, technology labs, and commercial partners.
established companies are increasingly aware of the growing technological capabilities of universities, research labs, and high-tech start-ups.
Philips relies recurrently on new technologies from universities, specialized research labs, and high-tech start-ups. The electronic giant endeavors to be preferred the partner for small,
Case Airfryer 87 88 Key learning points In the past, collaboration between large and small firms has been prone to different types of problems.
Small firms should do their homework before they start collaborating with large companies. Some large companies are trustworthy innovation partners
Partners may be technology partners such as universities, research labs, or other companies, but in most cases these are not the most important partners in the network.
One of the major learning points to emerge from the cases is that open innovation networks are sustainable only when the value that is jointly created is several times larger than
and deepen learning about open innovation among entrepreneurs One way to accelerate the use of open innovation in small firms is to diffuse successful cases using audiovisual tools on the Internet.
institutional sources (universities and university colleges (v), government and public research organizations (vi)), and other available sources (professional and industrial associations (vii), trade fairs, exhibitions,
universities (v; and public research organizations (vi. Collaborative innovation is captured by calculating the average score of the six questionnaire items registering the firm's use of cooperative agreements with innovation partners.
and profiting from technology, Harvard Business school Press, Harvard: Boston: MA and Chesbrough, H. W. 2006), Open business models:
How to thrive in the new innovation landscape, Harvard Business school Press, Harvard: Boston: MA. 4 Van de Vrande, V.,De Jong J. P. J.,Vanhaverbeke, W. and De Rochemont, M. 2009), Open innovation in SMES:
and profiting from technology, Harvard Business school Press, Harvard: Boston: MA and Chesbrough, H. W. 2006), Open business models:
How to thrive in the new innovation landscape, Harvard Business school Press, Harvard: Boston: MA. 7 Chesbrough, H. W. 2007), Why companies should have open business models, MIT Sloan Management Review, 48 (2),
Osterwalder, A. 2004), The business model ontology a proposition in a design science approach, Ph d. Thesis University Lausanne, Ecole des Hautes Etudes Commerciales HEC. 173 p;
and profiting from technology, Harvard Business school Press, Harvard: Boston; and Chesbrough, H. W. and Rosenbloom, R. S. 2002), The role of the business model in capturing value from innovation:
C. 2005), The 10 rules for strategic innovation, Harvard Business school Press, Harvard: MA; Christensen, C. M. 1997), The innovator's dilemma:
When new technologies cause great firms to fail, Harvard Business school Press, Harvard: MA. Christensen, C. M. 1997), The innovator's solution:
Creating and sustaining successful growth, Harvard Business school Press, Harvard: MA. 19 This is exactly what Mcgrath and Macmillan call discovery driven growth.
Authenticity, Harvard Business school Press, Boston: MA. 97 21 These conditions have been analysed in detail by Gans, J. S and Stern, S. 2003), The product market and the market for ideas:
What the new dynamics of business ecosystems mean for strategy, innovation and sustainability, Harvard Business school Press, Boston, MA. 28 There is a rapidly growing literature stream.
Networks of learning in biotechnology, Administrative Science Quarterly, 41,116-145.32 In 2006, Netflix, a major movie rental company, organized a crowdsourcing contest on the Internet.
What the new dynamics of business ecosystems mean for strategy, innovation and sustainability, Harvard Business school Press, Boston:
Authenticity, Harvard Business school Press, Boston: MA. Chapter 5 35 Katila, R. Rosenberger, J. D.,Eisenhardt K. M. 2008), Swimming with Sharks:
Harvard Business school Press, Boston, MA.;and Vanhaverbeke, W.,Van de Vrande, V. and Chesbrough, H. 2008.
This requires institutional mechanisms that foster severe selection of scholars from a large base, student and researcher mobility,
companies which used to be national TANDREA Bonaccorsi is at the Department of energy and Systems Engineering, Faculty of engineering, University of Pisa, Largo Lucio Lazzarino 1-56122 Pisa, Italy;
Andrea Bonaccorsi is professor of economics and management at the University of Pisa Italy. His main research interests include:
and empirical analysis of higher education. He has recently coordinated a large project for the publication of microdata from all European higher education institutions.
European competitiveness: IT and long-term scientific performance Science and Public Policy August 2011 523 Initial contributions pointed to the larger size of the ICT sector in the US industry and the earlier adoption of ICT
and learning curves. What is the relationship between technological progress in this industry and scientific progress in underlying fields?
A few years ago we asked a small panel of scientific authorities in computer science, in both European and US universities,
all the major breakthroughs originated from academic research carried out by US scientists and/or in US universities.
University research played a key role in the growth of the US computer industry. Universities were important sites for applied,
as well as basic, research in hardware and software and contributed to the development of new hardware.()
and the production of graduates who sought employment elsewhere, universities served as sites for the dissemination and diffusion of innovation throughout the industry.
and built at Moore School of Electrical engineering, University of Pennsylvania by Eckert and Mauchly, during WWII,
resulting from on a joint effort between IBM and the University of Harvard, which was established in 1939 (Moreau, 1984.
Interestingly, as early as in 1946 the Moore School of the University of Pennsylvania and the US ARMY sponsored a course on the theory and techniques for the design of electronic digital computers.
However, the role of the university was not unambiguous: in the same year one administrator of the Moore School:
Another company, Engineering Research Associates, starting from code-breaking activities during WWII, hired engineers from the University of Minnesota, among whom was Seymour R Cray,
Thus in the early days of the computer industry we witness many universities building their own machines, based on Von neumann or Turing architectures.
The role of universities greatly increased after a commercial move by IBM. In 1954 IBM delivered the 650
Thomas Watson Jr decided that a university could benefit from a discount up to 60%on the price of the 650
if that university agreed to offer courses in business data processing or scientific computing (Watson, 1990). This opened the way to a large diffusion of courses in computer science across US universities.
Meanwhile, US universities started to be involved in research on the component technologies underlying the computer.
Soon after WWII, the University European competitiveness: IT and long-term scientific performance 526 Science and Public Policy August 2011 of Illinois, Harvard and Massachusetts institute of technology (MIT) worked on magnetic core memories (Pugh, 1984;
Wildes and Lindgren, 1985. Bassett (2002) has shown that even in industrially sensitive fields such as metal-oxide semiconductor technology,
the ALGOL 60 was created by a committee convened by F L Bauer from the University of Munich (Germany) in 1958,
and COBOL was promoted by a group of universities and computer users which held a meeting at the Computation Center of the University of Pennsylvania in 1959.
In turn, the LISP LANGUAGE was developed by John Mccarthy at MIT in 1958 (Moreau, 1984), PASCAL was developed by Niklaus Wirth at ETH in Zurich (Switzerland) in the period 1968 1969 (Wirth,
1996) and PROLOG was born in 1972 after the work of several French researchers mostly based at the University of Marseille (Colmerauer and Roussel, 1996).
Universities did not play a direct scientific role in this massive bottom-up effort, but were a crucial element for the mass culture that fostered entrepreneurial activities:
usually not trained past the bachelor's degree level, who crank out code in an intuitive but effective fashion.
Campbell-Kelly, 2003: 308) Programmers do not necessarily come from postgraduate studies at universities, but benefit from an environment in which new ideas are generated
In particular, Frederick Terman, dean of the School of engineering and then provost at Stanford, promoted large military patronage in electronics
and then supported graduate engineers in the creation of new corporations (for a critical view,
Lécuyer (2006) has shown how Stanford students benefitted from updates in technology provided by companies located in the area,
and desires of()university researchers eager to investigate new computing techniques. Throughout its entire life, IPTO followed the rules prescribed by its early director, Joseph C R Licklider,
Universities changed their role in the early history: in the heroic period until 1959 they were involved directly in full-scale design and prototype production of computers,
In the case of Europe, the role of universities must be considered jointly with large public research organizations (PROS), such as Max Planck in Germany,
Two university groups were active in that period in the UK, one at Manchester and another at Cambridge.
I, built in collaboration with the Manchester University Group and delivered in 1951. A commercial computer, known as LEO, was installed at a company in 1951, well before ENIAC (Campbell-Kelly, 1989;
These companies used to establish strong linkages with universities, particularly in Paris and Grenoble and PROS.
Initially, universities were involved directly in the production of prototypes. With the advent of the 1960s, the heroic period of prototype building was over
In the USA, this structural change did not bring a diminishing role for universities but a redesign or their role around fundamental research, education, scientific advice and consultancy.
(or have their students develop) software code in order to test their results. This is facilitated by the fact that the test of theories can be done in a relatively cheap way, by writing and running programs
populated with visionary professors, hardworking Phd students, brilliant undergraduate students, rather than of corporate laboratories. The role of abstraction is crucial here.
From the Moore School and the University of Iowa, from Aiken and Wilkes to Algol, the vast majority of the essential steps were achieved on academic grounds.
as well as ambitious goals in the selection of students. The two reputational processes reinforce each other and make it credible to raise government or private money for research.
Although it cannot be said that university research has been the source of most inventions, it has played a prominent role in creating new concepts and ideas,
and in supporting the entrepreneurial attitude of students and graduate researchers. Also, deep and radically new ideas often originated in academic environments,
Patterns of educational mobility We identified the location of the universities at which top scientists received their academic degrees.
Such information can be retrieved with certainty for 855 scientists in the case of Phd 457 for the Master degree and 641 for the Bachelor degree (see Table 2). In terms of information availability,
it is likely that not all scientists received a Master degree, which is not formally necessary to receive the Phd in several academic systems.
US universities gave the degree to future top scientists in 76.5%of observable cases, against 16.6%in the case of Europe.
and tend to be considered a first step towards the Phd for talented students. Very interestingly, the geographical distribution is concentrated much less in the case of Bachelors.
Here a good 15%of students come from Asia and 10.9%from other countries. It seems that the US academic system has been historically able to attract talented graduate students from all over the world
offering Master and Phd degrees as intermediate steps towards a scientific career. In evolutionary terms, it seems that the US academic system has superior properties of variety generation,
in the sense that is able to identify, select, and motivate a continuous flow of intellectual talent, irrespective of the culture of origin,
In terms of cohorts, it is interesting to observe that by end of the 1960s the US universities had granted already 89 Phd degrees to those that eventually became top scientists.
there is a progression in the number of degrees in US universities, while the same is not true for European universities.
This finding sheds light on the puzzle identified in the section of this paper on‘Technological competitiveness and long-term scientific performance:
In the period 1980 1989, a period of explosion of computer science and information technology, US Table 2. Distribution of degrees of top computer scientists by geographical area Area Phd degree Master degree
IT and long-term scientific performance 532 Science and Public Policy August 2011 universities were able to attract 207 high potential candidates(+55%with respect to the previous decade), against only
%Something must have happened in that period, probably a manifestation of the accumulation of weaknesses. It is highly informative to examine the identity of those universities that granted undergraduate and postgraduate degrees to those brilliant scientists in their early days.
Again, we focus on the upper tail of the distribution of universities, because we are interested more in understanding the dynamics at the extreme, rather than the average properties.
This is more informative about the real conditions of mobility and capacity building in a highly turbulent scientific field.
Therefore we select the top 15 universities in which the top scientists have received their degree
at each of the three levels of education, i e. Phd, Master, and Bachelor (see Table 4), in descending order for the Phd.
The top 15 universities represent 56.2%of all universities granting a Phd to the 855 top scientists for
which we are able to reconstruct the information. In turn, the top 15 universities represent 47.1%of those granting the Master degree (n=457) and 41.3%of those granting the Bachelor (n=641.
The differences in the coverage rate shows that postgraduate education is concentrated more than undergraduate. Nevertheless, the top 15 universities cover between 40%and almost 60%of the sample, a reasonable proportion for our analysis. We start from Phd education.
A few comments are in order. First, the top ranking covers mostly US universities, with two Europeans featuring in the 10th position (Cambridge,
UK) and 14th position (Edinburgh, UK) and a Canadian one in the 13th position (Toronto).
Second, the distribution is concentrated highly. As stated, the first 15 universities attract 56.2%of all scientists for
whom we have full information. But this is not enough: the first four (MIT, Stanford, Berkeley, Carnegie mellon) attract Table 3. Distribution of year
Total 552 118 8 38 3 719 Table 4. Ranking of top 15 universities granting Phd,
Master and Bachelor degrees to top scientists in computer science Phd degree Master degree Bachelor degree Number%Number%Number%MIT 82 9. 6 47
10.3 45 7. 0 Stanford university 78 9. 1 29 6. 3 10 1. 6 University of California at Berkeley 69 8. 1
27 5. 9 20 3. 1 Carnegie mellon University 43 5. 0 13 2. 8 Harvard university 35 4. 1 14 3
. 1 25 3. 9 Cornell University 27 3. 2 12 2. 6 11 1. 7 Princeton university 26 3. 0 15
2. 3 University of Illinois 22 2. 6 12 2. 6 University of michigan 20 2. 3 9 2. 0 18 2. 8
University of Cambridge 16 1. 9 18 2. 8 Yale university 15 1. 8 7 1. 5 14 2.
2 University of Wisconsin 14 1. 6 10 2. 2 University of Toronto 13 1. 5 7 1. 5 9 1. 4
University of Edinburgh 13 1. 5 University of Pennsylvania 13 1. 5 University of Massachusetts 8 1. 8 University of washington 7 1. 5 University
of California at Los angeles 7 1. 5 Indian Institute of technology 7 1. 5 34 5. 3 National Taiwan University 13 2. 0 California
Institute of technology 12 1. 9 Technion Israel Institute of technology 11 1. 7 Brown University 10 1. 6 Total number of observations 855 457
universities not in USA are in italics European competitiveness: IT and long-term scientific performance Science and Public Policy August 2011 533 almost one-third of the total.
Brilliant students target top universities because there they have the opportunity to meet and to work with the best scientists.
Top universities actively target talented students to confirm their reputation. Postgraduate education seems to be a promising candidate to explain the success of the scientific careers of these scientists.
Understanding the extraordinary success of the US Phd model in turbulent fields is therefore a key for policy learning.
When examining the distribution of universities granting the Master degree the top list is slightly different.
There are a few new entries from the USA (e g. University of Massachusetts and University of California at Los angeles),
but the most interesting new entry is the Indian Institute of technology, which is not a single institution but an umbrella organization for several universities.
The situation changes quite drastically when we move to the Bachelor degree, the entry point for students considering a career in computer science.
In this list the Indian Institute of technology ranks second, contributing with 34 undergraduate students to the flow of future star scientists.
Interestingly, here we find many more universities outside the USA: from Europe (Cambridge), Taiwan (National Taiwan University), Israel (Technion Institute of technology) and Canada (Toronto.
Our interpretation is as follows. The talent pool for a career in computer science is worldwide. Entry points are good universities offering strong basic scientific knowledge
but also giving brilliant students sufficient motivation to emerge. After that stage, however, future top scientists must be channelled into foreign universities, most
of which are in the USA. In preparing for this migration of talent, Asian countries have been more strategic,
investing heavily into the preparation of undergraduate students to be selected and sent to top US universities.
European universities, in contrast, cultivate the ambition to organize graduate education particularly Phd education, in isolation.
They actively practice endogamy, by selecting students from internal Master programmes, which in turn select bright students from the Bachelor.
With few exceptions, European postgraduate education in computer science is not globally competitive. If it were competitive we would see more students migrating from Asia and the rest of the world into Europe, instead of the USA,
and we would see more students moving from the USA to Europe. In other words, Europe seems to play a game of limited mobility.
Patterns of disciplinary mobility Where do top computer scientists come from in terms of disciplinary affiliations? The data do not allow a full-scale analysis,
because we do not have control samples of scientists in related fields. Therefore the evidence should be interpreted in terms of overall mobility, rather than of specific discipline-todiscipline pathways.
More than half of them graduated either in mathematics or engineering, not computer science (see Table 5). The entry point of a scientific career is not in the specialised field,
but in some of the underlying knowledge bases, either theoretical or technical. Also interesting is the group of graduate students in physics who are recognized as key leaders in computer science.
Not surprisingly computer science is number one at the level of Master degrees, a stage in which some focusing is required.
Still, it covers only 34.1%of observable cases (including missing observations. Finally, at the Phd stage the disciplinary affiliation of computer science dominates with 38.2%of cases.
a Table 5. Distribution of Phd, Master and Bachelor degrees by discipline Phd degree Master degree Bachelor degree Number%Number%Number%Computer science 327 38.2
Students may start with a degree in fundamental disciplines (mathematics, physics) and find this new discipline as attractive as old fields for a brilliant career.
students with a background in human sciences (literature, linguistics, psychology) and social sciences (economics) may combine their domain expertise with advanced computer science.
Again, the European higher education systems are equipped less to deal with this kind of cognitive complementarity. Disciplinary mobility in Phd education, for example, is encouraged not.
to have good colleagues and students, to strengthen their CV and to increase their reputation.
we particularly note the ranking of academic institutions by the number of career moves that have involved them in the life of top scientists (see Table 7). All top 15 institutions are based in the USA, with the exception of Toronto,
It is not surprising that top universities try to attract top scientists, what is impressive is the extreme concentration of this process.
The first 15 universities account for 1051 moves, or 33.7%of the total number of academic moves in the entire careers of 1, 010 top scientists.
Even more impressive, the first four universities, namely MIT, Stanford, Berkeley and Carnegie mellon, account for 544 moves,
Assuming only one stop in one of these universities per scientist we find that almost 54%of all scientists in the sample,
coming from all countries in the world, have spent at least a period of their career at just these four universities.
Alternatively, assuming multiple career steps within these four universities (admittedly a more realistic scenario) slightly changes the situation:
if all average 4. 36 moves would have been made in the four universities, we would still find a large group of 136 scientists,
however, what is remarkable is the gravitational pull of highly prestigious universities on the career decisions of top scientists.
We limit the analysis to academic careers and investigate four career transitions: from postdoctoral researcher to assistant professor (or researcher in other academic systems,
The dynamics we observe are the result of intense competition among universities to attract the best young researchers, then the best young professors.
Without strong competition among universities, Table 7 Ranking of top 15 affiliations (only academic positions) in total number of positions over career Institution Number MIT 174 Stanford university
166 University of California at Berkeley 102 Carnegie mellon University 102 University of Illinois 59 University of maryland 58 Cornell University 52 University of washington 45 University of Pennsylvania
44 Harvard university 44 Princeton university 44 University of Texas 44 University of Massachusetts 42 Brown University 41 University of Toronto 34 Note:
universities not in USA are in italics Table 8 Descriptive statistics of duration of stay in academic career positions Duration of career steps Number Min Max Mean Std dev As postdoctoral
It is because competitors are ready to offer good prospects that all universities, subject to their budget constraints and reputation layer, try to compete.
in which all talented scientists are allocated to universities that make best use of their talent,
and all universities allocate their budget in the best possible way. If top scientists receive better offers,
If universities increase their reputation and have extra budget, they try to improve the quality of their potential candidates.
This ecology is nurtured by the interaction between universities and companies, and between companies and large (public and private) customers.
Universities can contribute to this ecology in two main ways: by producing top class research and education,
Implications for higher education policy The interesting question is now whether this search regime has been compatible with the institutional features of European higher education in the relevant historical period,
and why. The answer is negative. A search regime characterized by a turbulent rate of growth, proliferation,
competition based on peer review, a competitive Phd education system, cross-disciplinary mobility and industry academia mobility (Bonaccorsi, 2011).
According to our data, top scientists move from the university that awarded their Bachelor degree to the USA,
fight to enter top class universities as students, change affiliations several times in their career, combine different disciplines around computer science, enjoy a rapid career,
Computer science has been based on a fierce competition for students and researchers worldwide. Knowing how severe these demands are,
top class universities fight to attract the best students and try to offer the best conditions to professors.
But European universities have not been attractive for top computer scientists and increasingly have also become less attractive for students.
Among wellreputed old European universities just a few have international visibility at the top. These findings support the importance of fostering the reform agenda for European universities.
This will require dedicated efforts to build up globally competitive Phd programs, more transparent and competitive recruitment procedures for researchers, larger mobility of researchers.
The creation of the European Research Council has been an important step in this direction, but more is needed.
The situation is rapidly changing, with these issues on top of the reform agenda in many European countries.
) This will continue to put pressure on European higher education systems in the near future. Implications for innovation policy In the relevant historical period most European countries did not have
the US system already had several decades of trial-and-error, failures and institutional learning on which it was possible to capitalize.
while for European service companies the learning curve, in the same period, was much less favourable.
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