WARN-Count in xref table is 0 at offset 11669896 Knowledge Partner Research Report OPEN INNOVATION IN SMES
ï Het gebruik van Web 2. 0 ter ondersteuning van open innovatie en collectieve creativiteit
ï The international expansion path of Bekaert, AB-Inbev and Belgacom, Priscilla Boiardi and Leo Sleuwaegen, February 2010, published in English
ï Web 2. 0 Readiness Scan ï HR Toolbox 7 Table of contents 1 Why does open innovation in SMES deserve more attention?..
has been examined in a few studies based on large quantitative databases 5 These pioneering articles have explored why SMES engage in open innovation activities,
IBM. The open innovation practices of these companies have been documented widely in the professional press. Large companies deliberately introduce open innovation practices and are
CEO€ and sent an additional email with detailed information about the study. In total, we contacted 18
the growing impact of the Internet, television, and other distractions at night. Løgstrup and Schmidt
it a user-centred approach, because the customers were not able to envisage that the properties of a
problems and needs bicycle users and value chain partners †experience. This proactive design process guaranteed that Curana would always create extraordinary products that differed from existing
manufacturers, professional organizations, user groups, social representatives, and teaching institutions that created a totally new concept for the patient hospital room:
information systems DNA Interactif Fashion (see p 29) also illustrates how an SME can transform an industry, in this case
technologies (displays and three-dimensional scanning), the company wants to change both the physical shop and the shopping experience.
large screens as a virtual, three-dimensional model dressed in clothes from various collections that the
but the software also can make choices for the customer depending in the skeleton, weight, age,
pressure differences into a convenient tool for recording weather data, the metal cells were brought into contact with a liquid that reacts to these small differences accurately
professional and ordinary users. The liquid is used also in aviation and is designed especially so that the temperature would not affect the barometer†s reading.
but it also studied the problems and needs of bicycle users and value chain partners.
however, that many professional users did not trust the barometers they used. Applications for airports, blood testing, treating lung patients,
users can fry several foods at once without mixing their flavors†no one wants their apple fritters tasting like halibut
not or only weakly related to their core business. Each firm stayed focused on its product markets and
as to why a small, innovative company should stick to its core products. First, new product markets
It had to team up with different parties to develop the two basic technologies (displays and 3d
The Strada radiator had a panel on top of the radiator that users sometimes had to remove to clean the battery
To remove this panel, most people at home used a screwdriver, which would often damage the varnish.
small pop-up device was installed now to remove the panel easily without using tools. The device was
It also was user friendly by concealing hot parts so children would not burn themselves if they were playing with it
communication systems. The small consortium used the keywords to develop a new concept of the patient room that was translated subsequently into several products
Universities, research labs, crowds of experts, lead users, and knowledge brokers are just a few examples of potential external sources of kn owledge.
and a simple user interface 84 Godwin Zwanenburg, director lead of Kitchen appliances and part of Philips Consumer Lifestyle
Philips also opened the Philips My Kitchen Web site and blogs where recipes could be added
and where people could learn inspirational ways to fry food. Finally, Philips collaborated with some snack producers, such as Mora
audiovisual tools on the Internet. Entrepreneurs and small business managers are triggered not to learn and to become more innovative by studies
10-15 minutes) of 50 to 100 interesting cases around Europe and uploading them on Youtube
machinery, equipment, and software (iv; the acquisition of external knowledge through licenses or other types of contracts (v). A company†s external knowledge acquisition is captured by calculating the average
Association for Information systems, Vol. 16,1-25-25; Shafer, M. S.,Smith, H. J. and Linder, J. C. 2005), The
The core ideas of this book are summarised in the following HBR article: Other definitions of open innovation have been provided by Johnson
as if the user is at sea level. The effective air pressure is measured not, but the air pressure in relation to 0 metres is measured.
NASCAR is one of the most-viewed professional sports in terms of television ratings in the United states. In
for Information systems Chapter 4 98 29 Chesbrough, H. 2007), Why companies should have open business models, MIT Sloan Management
three core process archetypes, R&d Management Conference RADMA, Lisbon, Portugal 30 Larsen P. and Lewis, A. 2007), How award-winning SMES manage the barriers to innovation, Creativity and
In 2006, Netflix, a major movie rental company, organized a crowdsourcing contest on the Internet. The idea
was to build a better way to recommend movies to its users than its own software.
The contest was a huge success. Three years later, the Web-based movie rental service company awarded a team of mathematicians
and computer engineers called Bellkor's Pragmatic Chaos. The group developed software that is at least 10%more accurate than Netflix's current software
Cinematch) at predicting which movies customers will like based on their past preferences. Crowdsourcing contests are also possible for smaller companies †although most likely in smaller, more focused
communities. Moreover, small contests can be held among employees, suppliers, and local communities of designers, engineers, and so on
The reasons behind the poor competitiveness of the European information technology (IT) industry vis -Ã-vis the US one have been discussed many times.
and strong institutional complementarity with user industries. The paper compares the history of IT in
scientists in computer science, it shows that these conditions were met only in the US academic system
-verging data on some innovation inputs (R&d ex -penditure of firms), intermediate outputs (patents and final outputs (international trade), although on
R&d investment, with data related to 2004 (European Commission, 2005) and to 2009 (European Commis -sion, 2010.
In the two categories of IT hardware and software, there were a few European companies that spent more than â 1 billion on R&d in the year 2004
In the IT hardware category, just four companies from Finland (Nokia), Sweden (Ericcson), France Alcatel) and Germany (Infineon Technologies) are
recorded against six in the USA (Intel, HP, Cisco Motorola, Texas instruments and Sun) and four in
Japan (Hitachi, Toshiba, NEC and Fujitsu. 1 The situ -ation is even worse in software and computer ser
-vices. SAP was the only European company spending more than â 1 billion for R&d, while Microsoft, IBM
and Oracle combined spent ten times that amount. In addition, there were 26 companies from the USA and
three from Japan spending more than â 100 million against only six in Europe. The 2010 Scoreboard
European commission, 2010) has a different sectoral classification, but confirms the overall picture. In the semiconductors sector, within the top 20 R&d inves
In the software sector there are 14 US companies and six from Europe (SAP UBISOFT Entertainment, Dassault systemes, Sage
Amdocs and Invensys). There are few European companies who are not only in the top list of software
producers, but also in the wave of internet-related in -novators, or in the small group of successful startups
such as Google, e-Bay or Amazon, surviving the new economy bubble, or in the top list of companies offer
-ing IT-related services on a global scale. On a longer historical scale, companies which used to be national
Email: a. bonaccorsi @gmail. com; Tel:++39 050 22 17 378; Fax:++39 050 22 17 333
Various earlier versions of this paper have been presented at the Atlanta Conference on Science and Technology Policy
champions, such as Bull in France, Olivetti in Italy Siemens nixdorf in Germany, or ICL in the UK, are
Second, data on patents may be criticized as less relevant for some subsectors of IT, such as software
but are clearly a crucial indicator for hardware -related sectors. The Key Figures 2007 Report, using
data from the European Patent office, stated that †the US is ahead of the EU in four out of six
high-tech areas:(1) computers and automated business equipment,(2) microorganisms and genetic engineering,(3) lasers, and (4) semi
-conductors. On the other hand, EU leads in aviation and in communication technology European commission, 2007: 54 Looking at patent data,
it appears that in the patent class computer and automated business equipment the share of the EU-27 (the current 27 members of
the EU) increases from 20.2%in 1995 to 25.8%in 2003, while in the same period the share of the USA
to 2005 on data from the Patent Cooperation Treaty PCT), and using the larger definition of information
fields such as †pharmaceuticalsâ€, †computers office machineryâ€, †telecommunications†and †electronics†than in medium technology fields
long-run market shares in international trade for core ICT hardware, including computers and peripherals semiconductors, and telecommunications equipment
Europe declined from 63%in 1961 to 41%in 1994 while in the same period Japan rose from 4%to
30%and the USA defended its share, from 27%to 25 %Thus different indicators, although with different
in telecommunications, where Nokia dominates sev -eral segments of the market and Ericcson is a large
The EU has some excellent software companies with strong positions in their subsectors or niches †there are just too few compared to the
management software for smaller businesses European commission, 2010: 37 In addition, Europe is relatively strong in embedded
software, particularly in real-time applications for industrial automation, thanks to its leadership in the fields of mechanical and electrical engineering
However, this software is sold not typically sepa -rately from the equipment. Again, the reasons be
-sequent analyses, based on sector-level data, showed that a large part of the gap is due to large gains in
is a heavy user of ICT. Of particular importance is the stream of research originated by the construction
of industry-level productivity data in the KLEMS project, supported by the European commission O†Mahony and Timmer, 2009;
largest effect were computer and office equipment and electronic components. In addition, these sectors showed the largest spillover effects to other indus
of the internet, originate from this source (Flamm 1988; Lowen, 1997. Since the USA devoted a large
The case of Microsoft in operating systems is an ob -vious example. Not surprisingly, in mobile phone
technology Europe gained a leadership position also because of a first mover advantage in defining the
global system for mobile communications (GSM standard. Thus market size may be considered a nat -ural advantage for US industry, one that cannot be
products in software. According to this interpreta -tion, European software companies would be global -ly competitive,
but they specialize in customised software products, which require adaptation to the customer and the use of national languages.
In addi -tion, European markets are fragmented still in terms of regulation (particularly in services), standardiza -tion and professional practices, creating obstacles to
-search in the underlying fields, particularly computer science, and industrial competitiveness. We will use original evidence, admittedly of preliminary type, to
1, 010 scientists in computer science worldwide Finally, we illustrate some policy implications of these findings and draw conclusions
has established its own markets, end users, perfor -mance criteria, and learning curves What is the relationship between technological
small panel of scientific authorities in computer sci -ence, in both European and US universities, to list
invention of the internet at CERN, all the major breakthroughs originated from academic research carried out by US scientists and/or in US universi
of computer science were conceived by European thinkers (Alan Turing and John Von neumann) the evolution of the field in the half-century after WWII
Table 1. Origins of most important ideas in computer science and technology Top ten ideas in computer science
1. Turing machine (Goldstine and Von neumann; Turing 2. Programming languages; formal description of syntax and semantics;
LISP (Mccarthy 3. Memory hierarchy; cache memory 4. User interface; graphic user interface (GUI; concept of window (Xerox Palo alto Research center;
Apple 5. Internet (UCLA/DARPA; packet switched multinetworks; http and html protocols; WWW (Berners-Lee
6. Computational complexity; computational intractability pseudocausality 7. Relational database 8. Fourier fast transform (FFT)( Cooley and Tuckey
9. Efficient algorithms; data structure (Knuth and Tarjan 10. Artificial intelligence Source: our elaboration from expert opinion
European competitiveness: IT and long-term scientific performance Science and Public Policy August 2011 525 the mountains of pure theory down to the sea of
market competitiveness. We know that the path is not linear, but then we ignore how to trace commer
-cial success back to the pioneering ideas. The incu -bation cycle of truly innovative ideas may be very
long Luckily, computer science and the computer in -dustry have been the object of a massive historical
literature, that has highlighted several key factors We draw from this literature to answer the following
companies such as IBM (Flamm, 1988; Chandler 1990; Langlois, 1992; Mowery, 1996; Langlois and Steinmuller, 1999.
growth of the US computer industry. Universi -ties were important sites for applied, as well as basic, research in hardware and software and
contributed to the development of new hard -ware. (â€) By virtue of their relatively †openâ€
research and operating environment that em -phasized publication, relatively high levels of turnover among research staff, and the produc
elsewhere, universities served as sites for the dissemination and diffusion of innovation throughout the industry
history of computing in which this contribution is more evident. Evidence on the USA is offered first
The era of digital computing in the USA was inau -gurated by the ENIAC electronic calculator (Ceruzzi
that, IBM had developed the automatic sequence -controlled calculator (ASCC), known as Mark I which was still an electromechanical machine.
-tween IBM and the University of Harvard, which was established in 1939 (Moreau, 1984 Interestingly, as early as in 1946 the Moore
IBM hired Von neumann as a consultant in January 1952 and started a collaboration with his organiza
-15 computer, based on Harry Huskey†s 1953 design at Wayne State university, Detroit, MI. Thus in the ear
-ly days of the computer industry we witness many universities building their own machines, based on Von neumann or Turing architectures
commercial move by IBM. In 1954 IBM delivered the 650, a machine that was installed mainly for
business purposes in a thousand companies. Thomas Watson Jr decided that a university could benefit from a discount up to 60%on the price of the 650 if
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 underly -ing the computer.
Soon after WWII, the University European competitiveness: IT and long-term scientific performance Science and Public Policy August 2011 526
Technology (MIT) worked on magnetic core memo -ries (Pugh, 1984; Wildes and Lindgren, 1985. Bas
the field of high-level programming languages, for both the USA and Europe. While the single most
important language, FORTRAN, was invented by John Backus at IBM in 1954 (Pugh, 1995), the APT
language for the control of machine tools was devel -oped by the Servomechanisms Laboratory of MIT in
1955, the ALGOL 60 was created by a committee convened by F L Bauer from the University of Mu
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 devel
-oped by Niklaus Wirth at ETH in Zurich (Switzer -land) 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
C++ ,it was developed in 1979 at Bell laboratories by Bjarne Stroustrup, on the basis of the work he
-ment of the software industry. In December 1968 IBM was forced by the US authorities to unbundle
the commercialization of software from sales of hardware products, giving origin to a separate indus
-try, which then propagated in several application ar -eas (Mowery, 1996. In many cases the development
of software was the product of a large-scale entre -preneurial effort, carried out by thousands of indi
-vidual programmers. As Campbell-Kelly (2003 209) puts it In the late 1970s, a typical software development
firm consisted of one or two programmers with strong technical skills but no manufacturing marketing or distribution capabilities
This trend was reinforced after the emergence of the personal computer (PC) in the 1980s, but also in the
huge growth of the videogame industry and of soft -ware applications after the internet revolution.
The creative skills of small firms were commercially ex -ploited by larger firms, or the former were acquired
or disappeared. Universities did not play a direct sci -entific role in this massive bottom-up effort, but
were a crucial element for the mass culture that fostered entrepreneurial activities In the software industry, most of the R&d is
done by youthful programmers, 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 postgrad -uate studies at universities,
but benefit from an envi -ronment in which new ideas are generated and debated on a continuous basis. Without such an aca
to investigate new computing techniques Throughout its entire life, IPTO followed the rules prescribed by its early director, Joseph C R Licklider
-tion of computers, while after the emergence of a dedicated computer industry they were rather com
-mitted to fundamental research, education, scientific advice and consultancy Historical evidence on the role of the
promising start with the computer industry and built up foundations that could evolve into industrial competitiveness.
Indeed, the origins of the computer technology are to be found in 20th century European science, particularly in the work of two intellectual
computer, the universal Turing machine (Davis 2000). ) He had visited Princeton in 1936, where he met the great logician Alonzo Church and von
the new Mathematics Division of the National Phys -ical Laboratory, where he contributed to the devel
-opment of the automatic computing engine (ACE which was realized in 1950 and was the basis of a
-program computer, conformed to the Von neumann architecture, was completed and labelled the Man -chester automatic digital machine (MADM)( Lav
automatic computer (EDSAC) was realized at Cambridge. Here Maurice Wilkes developed ideas that prepared for high-level programming languages
such as symbolic labels, macros, and subroutine libraries (Books LLC, 2010a. Thus in the early years of the computer era the UK was head-to-head
with the USA. Ironically, as Moreau notes †it was the Europeans rather the Americans
A commercial computer, known as LEO was installed at a company in 1951, well before ENIAC (Campbell-Kelly, 1989;
In France the theoretical roots of computer sci -ence were laid down as early as the 1930s.
use ferrite-core memories. According to Mounier -Kuhn, 1994: 214 †in 1960 Compagnie des Machines Bull was
data processing machinery. It had a base of One of the main reasons why the UK
IBM. Unfortunately, although highly innovative, the machine had several problems that would have re -quired substantive development,
computers started with the pioneering work of Konrad Zuse well before WWII. Zuse started his
software were established by academic groups in the 1940s and early 1950s. These included: the Plankalkã l of Zuse in 1945, the work of Rutishauer
programming languages and developed the notion of bracketed structures, a fundamental breakthrough in computer science, while Bauer was the first to
propose the stack method of expression evaluation Jointly, they developed fundamental works on com -pilers (Books LLC, 2010b
-ty, IBM and the Bell laboratories in the USA Williams, 2000. In the early history of computing
technology Europe and the USA were equally competitive These short summaries also make it clear that
and large computer manufacturers emerged. How -ever, a sharp difference seems to emerge between the evolution of the technology in the USA and
-tioned in the studies of history of computing after the 1970s. This is an interesting puzzle.
that the institutionalization of computer science as an academic discipline took place earlier in the USA, approximately in the 1950s, than in Europe
the search regime of computer science In a stream of recent papers (Bonaccorsi, 2007 2008; 2010;
characterize the history of computer science from the point of view of the underlying abstract dynam -ics of knowledge
computer science, with a collective introduction NRC, 2004. The opening description sets the stage for our discussion
Computer science embraces questions ranging from the properties of electronic devices to the character of human understanding, from indi
Computer science encompasses basic research that seeks fundamental under -standing of computational phenomena, as well as applied research.
from experimental physics, in computer science there is a significant overlap. Great theorists also en -gage in developing
software code in order to test their results. This is fa -cilitated by the fact that the test of theories can be
computer science research (NRC, 2004: 15 †involves symbols and their manipulation and the creation and manipulation of abstractions
†creates and studies algorithms and artificial constructs, notably unlimited by physical laws †exploits and addresses exponential growth
procedures, or data) at many levels, preserving its fundamental properties. This makes it possible to
-tion on a hardware without losing the relevant aspects of the description. For example, it is possible to de
-couple the program from the underlying hardware representation (Shaw, 2004. This is sharply different from what happens in most areas of engineering (as
theoretical computer scientist summarized The computer originated in the academic envi -ronment. Zuse and IBM are special cases.
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. Neither the car nor the air
-craft have come up this way. And there are very good reasons. One certainly is that the
computer has an essential abstract side, most visible in programming, and abstract automati -zation is at least not a usual industrial subject
Zemanek, 1997: 16 To illustrate the power of abstraction, the introductory essay in the NRC€ s volume notes that
†the Internet works today because of abstrac -tions that were products of the human imagina
Computer scientists imagined †packets†of information flowing through pipes, and they symbolically) worked out the consequences of
govern how data flows through the Internet what happens when packets get lost, and so on NRC, 2004: 18;
computer science has been characterized by turbulent We conclude that the search regime of computer science has been characterized by a turbulent rate of
growth, proliferation dynamics, and strong cognitive and institutional complementarity European competitiveness: IT and long-term scientific performance
-tude increases in computing power over time relaxing year after year the constraints on computa -tion. At the same time, the symbolic representational
nature of computer programs made it possible to ex -plore hundreds of different directions at relatively low
Programming languages added further diversity to the search regime, by allowing computing results to be obtained in many different programming ways
The abstract nature of computer objects (e g. data procedures) allowed a process of progressive trans
-formation of many fields of reality, previously repre -sented in analogical ways, in the form of bits.
This has triggered a proliferation dynamics, whereby, at any point in time, there have been several diverse research
-tion of regions of reality (not only data but images sound, movement, all sorts of physical parameters
into computer science, creating powerful forms of cognitive complementarity. Not only mathematics logics, and electric and electronic engineering have
been involved into computer science since the be -ginning, but also biology and chemistry (bioinfor -matics), earth sciences (geographic information
systems), psychology (artificial intelligence), visual art (computer graphics), operations management enterprise resource planning), and many other cog -nitive fields.
All have been transformed deeply from the relationship with computer science. In all cases there was not just †applicationâ€,
but, as noted above †fundamental insights†to be gained from this complementarity Another form of complementarity is defined insti
In computer science this complementarity comes from the constitutive in -terplay between theoretical work and pragmatic
computer science An analysis of the CVS of top computer scientists An interesting perspective is to look at the large
community of computer scientists and at their own self-validation processes. Citations to papers in computer science are recorded automatically by
Citeseer, 2 a highly structured indexing system estab -lished in 1997 and endorsed by most scientific socie
-ties and departments in computer science worldwide The Citeseer service ranks scientists by the total number of citations, without checking for homo
-nyms and controlling for the age of scientists. There -fore it may be considered a crude approximation for
We downloaded from the internet all CVS of all top 1, 000 scientists in the Citeseer service, as of end
computer science, irrespective of their age. Their av -erage age is 56 years, with a minimum of 30 years
CV downloading and data processing was done manually by a team of research assistants Information from CVS is well known to be highly
Several items of data are still missing, so the analysis must be done on different samples, variable by variable.
-ment of data, with limited comment Patterns of educational mobility We identified the location of the universities at
rank high in the computer science field without a Phd from either the USA or Europe, with the USA
computer science and information technology, US Table 2. Distribution of degrees of top computer scientists by geographical area Area Phd degree Master degree Bachelor degree
Number%Number%Number %USA 654 76.5 332 72.6 363 56.6 Europe 142 16.6 58 12.7 112 17.5
the computer science field without a Phd from either the USA or Europe with the USA leading by a large
Table 3. Distribution of year and place of Phd degree of top scientists in computer science
Table 4. Ranking of top 15 universities granting Phd, Master and Bachelor degrees to top scientists in computer science
-dents considering a career in computer science. In this list the Indian Institute of technology ranks se -cond, contributing with 34 undergraduate students to
for a career in computer science is worldwide. En -try points are good universities offering strong
computer science is not globally competitive. If it were competitive we would see more students mi
Where do top computer scientists come from, in terms of disciplinary affiliations? The data do not al
-low a full-scale analysis, because we do not have control samples of scientists in related fields.
-ated either in mathematics or engineering, not com -puter science (see Table 5). The entry point of a
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 The large number of missing observations may con
Phd is in computer science At the same time an interesting tentative interpre -tation can be offered. Computer science is a relative
-ly young discipline. It has not the long scientific history of physics, mathematics, or chemistry
Furthermore, it has an intrinsically dual nature: a Table 5. Distribution of Phd, Master and Bachelor degrees by discipline
%Computer science 327 38.2 156 34.1 102 15.9 Engineering 116 13.6 113 24.7 165 25.7 Mathematics 90 10.5 75 16.4 165 25.7
Physics 25 2. 9 14 3. 1 45 7. 0 Statistics 9 1. 1 6 1. 3 3 0. 5
mathematics, logics, computation, probability, and is also an application-oriented discipline, with a face towards the industrial and commercial feasibility of
Our data seem to suggest that com -puter science has been a gateway for cross-discipline
-ciplines (mathematics, physics) and find this new discipline as attractive as old fields for a brilliant
expertise with advanced computer science This interpretation is confirmed by Table 6, which shows the transition matrix between the Bachelor
This is roughly confirmed for computer science (79.4%on the diagonal cell) but not for mathematics and engineering
We therefore conclude that computer science is a field characterized by a high degree of disciplinary mobility, attracting competences from related fields
In terms of the search regime framework, this amounts to saying that cognitive complementarity is a key element of the epistemic dynamics
Mathematics Engineering Computer science Other disciplines No Phd Total Number%Number%Number%Number%Number%Number
%Mathematics 47 8. 5 14 49.7 82 11.5 19 11.5 3 1. 8 165 100.0
Computer science-2. 0 2 79.4 81 15.7 16 15.7 3 2. 9 102 100.0 Total 51 19.7 85 50.9 220 14.8 64 14.8 12 2. 8 432 100.0
The search regime of computer science has been characterized by a turbulent rate of growth, proliferation dynamics and strong cognitive and institutional
We find the data illuminating. It is not surprising that top universities try to attract top sci
An easy way to comment these data is to remem -ber that these are star scientists,
Second, we are observing average data Standard deviation informs us that even faster careers are observable.
-ble to normalize these data by age or seniority, given several missing items of data.
A crude approxima -tion is offered in Table 9, suggesting that on average they may change country for each 30 years of age
Our data seem to suggest that in the com -puter sciences the pattern of geographic mobility has
external control on the data self-declared in the CVS would require a long and dedicated investigation
Web of Science count of publications at the end of 2005. These cover only a subset of journals consid
-ered important in the computer science community and do not include many top conferences, that are
produce complete software and mention it in their CVS. In this case, on average four programs are
of the search regime in computer science Discussion of findings and policy implications The hidden dimension of industrial competitiveness
Software 204 1 56 4. 14 6. 081 Patents 137 1 47 6. 57 8. 342
For a large industry such as the computer industry an overall ecology of abstract ideas, engineering capabilities, technical skills, and entrepreneurial vi
our data, top scientists move from the university that awarded their Bachelor degree to the USA, fight to
-ent disciplines around computer science, enjoy a rapid career, have extensive industry involvement as witnessed by research collaborations, as well as
software development and patents 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
computer scientists and increasingly have also be -come less attractive for students. Among well -reputed old European universities, just a few have
-covered in the computer science is becoming widespread (Wildavsky, 2010. This will continue to put pressure on European higher education systems
-lic, aimed at establishing an informatics indus -try that would guarantee France independence from the American manufacturers.
The search regime in computer science is based on a massive and fast effort of exploration of
-vations of the PC (in the 1980s) and the internet (in the 1990s) were introduced, the US system already
challenging requirements in the user sector generate a feedback on the creation of new ideas, and new
waves of IT associated to the PC and the internet ex -actly because they had experienced already the early
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