and the potential of open innovation based on Web 2. 0 technologies. Keywords: small and micro firms, open innovation, Web 2. 0. JEL Classification:
O32. Introduction2 In the age of globalization, complex technologies (Nonaka, 2007), shortened product life cycles, and increasing interconnectedness of customers (Tidd and Bessant, 2005), companies depend on their ability to innovate
and application of a variety of Web 2. 0-based information and communication technologies that simplify the relationship with stakeholder groups
Many large firms such as IBM, Procter & gamble or Eli lilly have adopted already successfully the open innovation approach.
established companies were already using Web 2. 0 technologies to integrate customers and external experts into their innovation processes (Mckinsey, 2008).
especially supported through new Web-based technologies, might also offer benefits and advantages for SMES to Katja Hutter, Julia Hautz, Karina Repke, Kurt Matzler, 2013.
Furthermore, these previous studies on open innovation in SMES have neglected the potential of Web 2. 0 technologies and platforms and related concepts such as crowdsourcing (Howe, 2008), cocreation (Winsor, 2005),
and their peculiarities as well as considering the potential opportunities provided through online Web 2. 0 technologies.
this study will assess how Web 2. 0 technologies can serve as an intermediary in supporting small and micro firms in their open innovation activities.
A large share of research on the opening of the innovation process also highlights the important role of online information and communication technologies (ICTS) and Web 2. 0-based applications.
companies can make strategic use of social networks (Marandi et al.,2010), online communities (Spaulding, 2010; Dahlander et al.
2008), virtual worlds (Kaplan and Haenlein, 2010; Kohler et al. 2011), or idea and design contest and tournaments (Morgan and Wang, 2010) to support
and thereby aim to leverage the creativity, skills, insight and intelligence of billions of individuals on the Web (Terwiesch and Xu, 2008).
In addition, social technologies on the Web can support later stages of the innovation processes, help to identify new application opportunities
the creation of adequate collaboration structures, consulting services and targeted marketing support. One recent study focused on the application of social technologies of the Web 2. 0 as tools to open up the innovation process of SMES (Piva et al.
2012). ) They investigate how collaborating with Problems and Perspectives in Management, Volume 11, Issue 1,
2013 16 open source software communities on the Web can help SMES overcome financial constraints and access external competencies and valuable complementary assets (e g.,
However, potential of Web 2. 0 innovation platforms and communities to support the open innovation activities of SMES outside the special case of the software industry. 2. Empirical study 2. 1
As financial data is rarely available for SMES, we relied on the number of employees
The interviewees were notified via phone and an appointment was made in advance in order to guarantee that the interviews could be conducted over the full length of 30 to 40 minutes.
and the collected data broadly categorized according to our three research questions. Within the first round of content analysis
and the coders reworked the data searching for significant quotations, which are presented below. Another goal of the third step was to filter out the most essential and topic-related findings among a variety of interesting ideas.
and try to comprehend the data as well as the most important findings. Next, the results are presented with meaningful statements from the interviews. 3. Results 3. 1. Current sources of innovation in SMES.
and core customer segments and are confronted suddenly with new market conditions. With regard to the innovation process, the search for suitable markets, adequate determination of the appropriated pricing strategies and selection of marketing tools for effective communication have been identified as obstacles.
Problems and Perspectives in Management, Volume 11, Issue 1, 2013 19 3. 3. The potential of open innovation practices based on Web 2. 0 and social technologies.
and supported through the adoption of open innovation practices based on Web 2. 0 and social technologies.
It is obvious that the Internet already plays an important role in the small and micro firms investigated in the study
The Internet is essential for us, because it offers a huge amount of specific information that is needed to offer even better solutions for our clients
We are trying to use the Internet in the best possible way. If necessary we are also open for new ideas
to follow latest technology and Internet trends. Furthermore, the prerequisites of the surveyed firms are very good for the strategic application of new open innovation tools
Through using the support of Web 2. 0 technologies in applying open innovation practices information generation can be simplified
or establishing and managing Web 2. 0 based initiatives. Thereby, strong competitive thinking as well as a focus on selfinterests can be an obstacle for collaborations and partnerships.
Finally, research should investigate in more detail how Web 2. 0 based technology like online idea and design contests, innovation platforms and other open innovation tools,
Harnessing the Power of the Oh-So-Social Web, MIT Sloan Management Review, 49 (3), pp. 36-42.4.
Internet Marketing in the Internationalisation of UK SMES, Journal of Marketing Management, 13 (1-3), pp. 9-28.31.
Crowdsourcing How the Power of the Crowd is Driving the Future of Business, Crown Business. 35.
Crowdsourcing: How the Power of the Crowd is Driving the Future of Business, New york, The Crown Publishing Group. 36.
The challenges and opportunities of Social media, Business Horizons, 53 (1), pp. 59-68.38. Kaufmann A. and Tödtling F. 2002.
Co-creation in virtual worlds: the design of the user experience, MIS Quarterly, 35 (3), pp. 773-788.41.
Effects of the Internet on the marketing communication of service companies, Journal of Services Marketing, 19 (2), pp. 63-69.44.
Facebook and value cocreation, Marketing Review, 10 (2), pp. 169-183.49. Mckinsey (2008. Building the Web 2. 0 Enterprise. 50.
Morgan J. and Wang R. 2010. Tournaments for Ideas, California Management Review, 52 (2), pp. 77-97.51.
Open Innovation in Global networks. Organization for Economic Co-operation and Development. 57. Parida V.,Westerberg M. and Frishammar J. 2012.
The Internet and Foreign Market Expansion by Firms, Management International Review, 42 (2), pp. 207-221.59.
Is Open source Software about Innovation? Collaborations with the Open source Community and Innovation Performance of Software Entrepreneurial Ventures, Journal of Small Business Management, 50 (2), pp. 340-364.61.
Plehn-Dujowich J. M. 2009. Firm size and types of innovation, Economics of Innovation & New Technology, 18 (3), pp. 205-223.62.
May 2009, published in English Het gebruik van Web 2. 0 ter ondersteuning van open innovatie en collectieve creativiteit.
December 2009, published in Dutch The international expansion path of Bekaert, AB-Inbev and Belgacom, Priscilla Boiardi and Leo Sleuwaegen,
online tool Web 2. 0 Readiness Scan HR Toolbox 7 Table of contents 1 Why does open innovation in SMES deserve more attention?..
These stories about applying open innovation in small firms successfully can barely be compared with the open innovation ventures of large manufacturing companies, such as Xerox, P&g, Philips, Lego, and IBM.
because the open innovation network is at the core of the business model. The existing business model (innovation) frameworks do not pay attention to strategic partners
and sent an additional email with detailed information about the study. In total we contacted 18 companies that have been mentioned as having been involved in open innovation activities.
this trend is an outcome of the growing impact of the Internet, television, and other distractions at night.
and largely untapped approach to increasing value for the customer and enabling medical staff to deliver value by making their jobs more convenient using, for instance, smart and integrated information systems.
Based on a combination of two technologies (displays and three-dimensional scanning the company wants to change both the physical shop and the shopping experience.
After scanning, customers see themselves on large screens as a virtual, three-dimensional model dressed in clothes from various collections that the shop offers.
but the software also can make choices for the customer depending in the skeleton, weight, age,
To convert these minimal 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
It is remarkable that the firms we interviewed did not diversify over time into new businesses that were not or only weakly related to their core business.
innovative company should stick to its core products. First, new product markets have their own specific challenges.
awards, lectures at conferences, press coverage, and other inexpensive means. 54 4 How SMES build new business models through open innovation?
It had to team up with different parties to develop the two basic technologies (displays and 3d scanning) to make virtual shopping possible.
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.
Based on an idea from Product Days, a small pop-up device was installed now to remove the panel easily without using tools.
and make patients less dependent on nurses using intelligent monitoring and communication systems. The small consortium used the keywords to develop a new concept of the patient room that was translated subsequently into several products
and blogs where recipes could be added and where people could learn inspirational ways to fry food.
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.
and uploading them on Youtube, Slideshare, and so on. For a good example, see the videos on the Web site of the Belgian Design Forum.
the acquisition of innovative, externally developed 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 score of the five questionnaire items registering a firm's use of these external
and future of the concept, Communications of the Association for Information systems, Vol. 16,1-25-25; Shafer, M. S.,Smith, H. J. and Linder, J. C. 2005), The power of business models, Business Horizons, 48 (3), 199-207.
The core ideas of this book are summarised in the following HBR article: Other definitions of open innovation have been provided by Johnson.
origins, present and future of the concept, Communications of the Association for Information systems. Chapter 4 98 29 Chesbrough, H. 2007), Why companies should have open business models, MIT Sloan Management Review, Winter 2007,48, 2, 22-28;
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
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.
The idea was to build a better way to recommend movies to its users than its own software.
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. 33 These value networks have been described by different authors.
As forthechallengesofopeninnovationinsmes, our data setcontainsinformationonperceivedbarriersto adoptopeninnovationpractices. Theopeninnovation literaturehassofarwitnessedfewattemptstoexplorethis subject. Chesbroughandcrowther (2006) for example identifiedthenot-invented-here (NIH) syndromeandlack of internalcommitmentasmainhamperingfactors.
Clusteranalysisrevealedthreegroupsofsmes, clustering firms intogroupswithsimilaropeninnovationpractices. Theirfeaturesconfirm Lichtenthaler's (2008) conclusionthat companies seldomfocusoneithertechnologyexploitationor technologyexploration. Rather, openinnovatingcompanies tend tocombinethesetwoaspectsofopeninnovation.
EIM bv does not accept responsibility for printing errors and/or other imperfections. 2 Open innovation in SMES:
but focuses only on companies that develop open source software. Lecocq & Demil (2006) study the U s. tabletop role-playing game industry,
such as in the open source software (Henkel, 2004; Hienerth, 2006. This practice is also becoming fashionable in other industries such as car design, electronic games,
Henkel (2004) argues that firms (adopting open source strategies) may make their technology available to the public
including cultural and organizational problems as the most important items. 4. DATA AND METHODS 4. 1 Survey description To analyze trends,
The population of firms was derived from a database of the Chambers of Commerce, containing data on all Dutch firms.
The data were collected in December 2005 over a period of three weeks, by means of computer assisted telephone interviewing (CATI.
All respondents were small business owners or managers and innovation decision-makers. Attempts to contact reference persons were made five times before considering persons as non-respondents.
In total 2, 230 respondents were contacted, of which 1, 206 (54%)were willing to participate in our survey.
The survey data contained a summary variable indicating customer involvement i e. a dummy coded 1
The survey data allowed distinguishing between employees that belong to the R&d department and those that are coming from other organizational parts of the company.
and they experience stronger growth in adapting open innovation practices than their smaller counterparts 5. 3 Clusters To explore patterns of open innovation among SMES we relied on cluster analysis techniques.
since the addition of irrelevant variables can have a serious effect on the results of the 25 clustering (Milligan and Cooper, 1987).
Next, we applied cluster analysis techniques to explore patterns of open innovation practices among SMES. Finally, we used oneway analysis of variance to validate the taxonomy.
as a way to reduce the number of dimensions to be used in the clustering. In general
and therefore no variable would implicitly be weighted more heavily in the clustering and thus dominate the cluster solution (Hair et al.,
We first tested if our data were suitable for a component analysis, by calculating Measures of Sampling Adequacy (MSA) for the individual variables (Hair et al.
In the cluster analysis we combined hierarchical and nonhierarchical techniques. This helps to obtain more stable and robust taxonomies (Milligan and Sokol, 1980;
For each number of clusters (k), we perform a k-means‘nonhierarchical'cluster analysis, in which SMES were divided iteratively into clusters based on their distance to some initial starting points of dimension k
the data did not contain enough records to provide reliable insights about respondents'motives and challenges on this topic.
Table 7. Classification of open innovation motives Category Description Control Increased control over activities, better organization of complex processes Focus Fit with core competencies,
The results of the cluster analysis furthermore show that there are different open innovation strategies and practices among SMES.
Cluster analysis. Oxford university Press, London. Fontana. R.,Geuna, A.,Matt M.,2006. Factors affecting university industry R&d projects:
Multivariate Data analysis. 5th ed. Prentice hall, Englewood Cliffs, NJ. Henkel, J.,2004. Open source software from commercial firms Tools, complements,
and collective invention. Zfb-Ergänzungsheft. Henkel. J.,2006. Selective revealing in open innovation processes: The case of embedded Linux, Research Policy 35,953 969.
Herstatt, C.,Von Hippel, E.,1992. From experience: Developing new product concepts via the lead user method:
Creating a Strategic Center to Manage a Web of Partners. California Management Review 37,146-162.
clustering methods. Applied Psychological Measurement 11,329-354. Milligan, G. W.,Sokol, L. M.,1980. A two-stage clustering algorithm with robust recovery characteristics.
Educational and Psychological Measurement 40,755-759. Morgan, G.,1993. Imaginization. Sage California 43 National Science Foundation, 2006.
A case study. Information systems Research 1, 89-113. Nalebuff, B. J.,Brandenburger A m.,1996. Co-opetition.
Cluster analysis in marketing research: review and suggestions for application, Journal of Marketing Research, 20: 134-148.
Determinants of innovation capability in small electronics and software firms in southeast England. Research Policy 31,1053 1067.
Evidence from the telecommunications equipment manufacturing industry. Academy of Management Journal 49,819 835. West, J.,2003.
Melding proprietary and open source platform strategies. Research Policy 32,1259 1285. West, J.,Callagher, S.,2006.
the paradox of firm investment in open-source software. R&d Management 36,319-331.45 The results of EIM's Research Programme on SMES and Entrepreneurship are published in the following series:
Evidence from Global Entrepreneurship Monitor Data H200809 25-7-2008 The Entrepreneurial Adjustment Process in Disequilibrium:
As forthechallengesofopeninnovationinsmes, our data setcontainsinformationonperceivedbarriersto adoptopeninnovationpractices. Theopeninnovation literaturehassofarwitnessedfewattemptstoexplorethis subject. Chesbroughandcrowther (2006) for example identifiedthenot-invented-here (NIH) syndromeandlack of internalcommitmentasmainhamperingfactors.
Clusteranalysisrevealedthreegroupsofsmes, clustering firms intogroupswithsimilaropeninnovationpractices. Theirfeaturesconfirm Lichtenthaler's (2008) conclusionthat companies seldomfocusoneithertechnologyexploitationor technologyexploration. Rather, openinnovatingcompanies tend tocombinethesetwoaspectsofopeninnovation.
May 2009, published in English Het gebruik van Web 2. 0 ter ondersteuning van open innovatie en collectieve creativiteit.
December 2009, published in Dutch The international expansion path of Bekaert, AB-Inbev and Belgacom, Priscilla Boiardi and Leo Sleuwaegen,
online tool Web 2. 0 Readiness Scan HR Toolbox 7 Table of contents 1 Why does open innovation in SMES deserve more attention?..
These stories about applying open innovation in small firms successfully can barely be compared with the open innovation ventures of large manufacturing companies, such as Xerox, P&g, Philips, Lego, and IBM.
because the open innovation network is at the core of the business model. The existing business model (innovation) frameworks do not pay attention to strategic partners
and sent an additional email with detailed information about the study. In total we contacted 18 companies that have been mentioned as having been involved in open innovation activities.
this trend is an outcome of the growing impact of the Internet, television, and other distractions at night.
and largely untapped approach to increasing value for the customer and enabling medical staff to deliver value by making their jobs more convenient using, for instance, smart and integrated information systems.
Based on a combination of two technologies (displays and three-dimensional scanning the company wants to change both the physical shop and the shopping experience.
After scanning, customers see themselves on large screens as a virtual, three-dimensional model dressed in clothes from various collections that the shop offers.
but the software also can make choices for the customer depending in the skeleton, weight, age,
To convert these minimal 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
It is remarkable that the firms we interviewed did not diversify over time into new businesses that were not or only weakly related to their core business.
innovative company should stick to its core products. First, new product markets have their own specific challenges.
awards, lectures at conferences, press coverage, and other inexpensive means. 54 4 How SMES build new business models through open innovation?
It had to team up with different parties to develop the two basic technologies (displays and 3d scanning) to make virtual shopping possible.
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.
Based on an idea from Product Days, a small pop-up device was installed now to remove the panel easily without using tools.
and make patients less dependent on nurses using intelligent monitoring and communication systems. The small consortium used the keywords to develop a new concept of the patient room that was translated subsequently into several products
and blogs where recipes could be added and where people could learn inspirational ways to fry food.
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.
and uploading them on Youtube, Slideshare, and so on. For a good example, see the videos on the Web site of the Belgian Design Forum.
the acquisition of innovative, externally developed 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 score of the five questionnaire items registering a firm's use of these external
and future of the concept, Communications of the Association for Information systems, Vol. 16,1-25-25; Shafer, M. S.,Smith, H. J. and Linder, J. C. 2005), The power of business models, Business Horizons, 48 (3), 199-207.
The core ideas of this book are summarised in the following HBR article: Other definitions of open innovation have been provided by Johnson.
origins, present and future of the concept, Communications of the Association for Information systems. Chapter 4 98 29 Chesbrough, H. 2007), Why companies should have open business models, MIT Sloan Management Review, Winter 2007,48, 2, 22-28;
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
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.
The idea was to build a better way to recommend movies to its users than its own software.
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. 33 These value networks have been described by different authors.
http://www. ingentaconnect. com/content/beech/spp European competitiveness in information technology and long-term scientific performance Andrea Bonaccorsi The reasons behind the poor competitiveness of the European information technology
Based on the analysis of the curriculum vitae of the top 1, 000 scientists in computer science, it shows that these conditions were met only in the US academic system.
This assessment is based on converging data on some innovation inputs (R&d expenditure of firms), intermediate outputs (patents) and final outputs (international trade),
with data related to 2004 (European commission, 2005) and to 2009 (European commission, 2010. In the two categories of IT hardware and software, there were a few European companies that spent more than €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 situation is even worse in software and computer services.
SAP was the only European company spending more than €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 €100 million, against only six in Europe.
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 innovators, 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 offering IT-related services on a global scale.
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 (2007), at the IPTS Workshop on Sectoral Specialisation of R&d in Europe (2008),
IT and long-term scientific performance 522 Science and Public Policy August 2011 champions, such as Bull in France, Olivetti in Italy, Siemens nixdorf in Germany,
Second, data on patents may be criticized as less relevant for some subsectors of IT, such as software,
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) semiconductors.
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
Extending the analysis to 2005 on data from the Patent Cooperation Treaty (PCT), and using the larger definition of information and communication technologies (ICT),
EU-27 is less specialised in high technology fields such as‘pharmaceuticals',‘computers, office machinery, '‘telecommunications'and‘electronics'than in medium technology fields such as‘general machinery',‘machine tools',‘metal products'and‘transport.
European commission, 2008: 69) Over a longer period, Dalum et al. 1999) constructed the revealed technological advantage (RTA) indicator,
1999) calculated the 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%.
First, European competitiveness is much stronger in telecommunications, where Nokia dominates several segments of the market
and Ericcson is a large player (Santangelo, 1998; Hultén and Molleryd, 2003; Cantwell and Santangelo, 2003.
The EU has some excellent software companies with strong positions in their subsectors or niches there are just too few compared to the US.
Examples include SAP in enterprise software, Autonomy in unstructured search and Sage in accounting and customer relationship 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 separately from the equipment. Again, the reasons behind large differences in performance between large markets and niches are worth exploring.
Subsequent analyses, based on sector-level data, showed that a large part of the gap is due to large gains in productivity in the US market service sector,
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;
and discovered that the sectors accounting for the largest effect were computer and office equipment and electronic components.
Many technological breakthroughs, including the original idea of the internet, originate from this source (Flamm, 1988;
The case of Microsoft in operating systems is an obvious 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 natural advantage for US industry one that cannot be modified by will (Mowery, 1996;
Third, one might refer to the linguistic heterogeneity of European countries to explain the difficulty in producing standardised or packaged products in software.
According to this interpretation, European software companies would be globally competitive, but they specialize in customised software products,
which require adaptation to the customer and the use of national languages. In addition, European markets are fragmented still in terms of regulation (particularly in services),
particularly computer science, and industrial competitiveness. We will use original evidence, admittedly of preliminary type, to support this proposition.
In the fourth section we review descriptive evidence drawn from a large sample of CVS of the top 1, 010 scientists in computer science worldwide.
A few years ago we asked a small panel of scientific authorities in computer science, in both European and US universities,
There is another useful piece of information in Table 1. With the (partial) exceptions of the early pioneering ideas of John Von neumann and of the invention of the internet at CERN,
while the seminal theoretical contributions to the entire field of computer science were conceived by European thinkers (Alan Turing
it nurtures the ecology of ideas and visions that feed innovation Table 1. Origins of most important ideas in computer science and technology Top ten ideas in computer science 1. Turing machine
Turing) 2. Programming languages; formal description of syntax and semantics; LISP (Mccarthy) 3. Memory hierarchy; cache memory 4. User interface;
Apple) 5. Internet (UCLA/DARPA; packet switched multinetworks; http and html protocols; WWW (Berners-Lee) 6. Computational complexity;
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 commercial success back to the pioneering ideas.
Luckily, computer science and the computer industry have been the object of a massive historical literature, that has highlighted several key factors.
and marketing investment by large companies such as IBM (Flamm, 1988; Chandler, 1990; Langlois, 1992; Mowery, 1996;
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.()
By virtue of their relatively‘open'research and operating environment that emphasized publication, relatively high levels of turnover among research staff,
and the production of graduates who sought employment elsewhere, universities served as sites for the dissemination and diffusion of innovation throughout the industry.
We briefly review some of the turning points in the history of computing in which this contribution is more evident.
USA The era of digital computing in the USA was inaugurated by the ENIAC electronic calculator (Ceruzzi, 1998;
After this development, in 1945 the great mathematician John Von neumann described the abstract structure of a modern computing machine,
Before that, IBM had developed the automatic sequencecontrolled calculator (ASCC), known as Mark I, which was still an electromechanical machine.
resulting from on a joint effort between IBM and the University of Harvard, which was established in 1939 (Moreau, 1984.
Ceruzzi, 1998: 25) Eckert and Mauchly soon established a company that developed the UNIVAC, the first large-scale computer,
IBM hired Von neumann as a consultant in January 1952 and started a collaboration with his organization, the Institute for Advanced Study at Princeton (Pugh, 1995.
Another small company, Bendix, built the G-15 computer, based on Harry Huskey's 1953 design at Wayne State university, Detroit, MI.
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
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 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 role of academic research is also evident in 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 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).
As with C++ ,it was developed in 1979 at Bell laboratories by Bjarne Stroustrup, on the basis of the work he did for his Phd at Cambridge university (UK)( Stroustrup, 1996).
however, particularly after the development 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 industry,
which then propagated in several application areas (Mowery, 1996). In many cases the development of software was the product of a large-scale entrepreneurial effort,
carried out by thousands of individual 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 software applications after the internet revolution.
The creative skills of small firms were exploited commercially by larger firms, or the former were acquired,
or disappeared. 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:
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 postgraduate studies at universities,
but benefit from an environment in which new ideas are generated and debated on a continuous basis. Without such an academic background it would not be possible to explain the hacker movement,
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,
in the heroic period until 1959 they were involved directly in full-scale design and prototype production of computers,
while after the emergence of a dedicated computer industry they were committed rather to fundamental research, education, scientific advice and consultancy.
Europe During WWII all large European countries had a promising start with the computer industry and built up foundations that could evolve into industrial competitiveness.
In 1937 The english mathematician Alan Turing published the first theoretical model of a modern computer, the universal Turing machine (Davis, 2000.
where he contributed to the development of the automatic computing engine (ACE), which was realized in 1950
As early as 1948 a prototype of the first completely electronic storedprogram computer, conformed to the Von neumann architecture,
In the same year the electronic delay storage 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 who were the first in the world to make a computer as a commercial product.
Moreau, 1984: 53) It was the Ferranti MARK I, built in collaboration with the Manchester University Group
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 science were laid down as early as the 1930s. The french mathematician Louis Couffignal demonstrated how a programmable binary calculator could be constructed using electromechanical technology as early as 1938,
one of the first to use ferrite-core memories. According to Mounier-Kuhn, 1994: 214: in 1960 Compagnie des Machines Bull was one of the world's leading manufacturers of data processing machinery.
It had a base of One of the main reasons why the UK did not capitalize on its early achievements in digital computers was that these machines were considered military secrets
Bull with the Gamma 60 was one of the competitors for the tender issued by the US Atomic energy commission (AEC) for a machine 100 times faster than any then existing, alongside UNIVAC and IBM.
Here the construction of computers started with the pioneering work of Konrad Zuse well before WWII.
In addition, the scientific foundations for the modern notion of software were established by academic groups in the 1940s and early 1950s.
Semelson studied the structure of 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 compilers (Books LLC, 2010b.
Indeed, Zuse's work is considered by historians of computing technology to be the earliest pioneering work in the modern era.
In his reconstructions of major early computing events Williams places European pioneers such as Zuse, Turing at NPL, Williams and Kilburn at Manchester and Wilkes at Cambridge alongside Von neumann, Eckert and Mauchly, the Moore
School, Harvard university, 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 the early era of computer technology saw the deep involvement of the academic environment.
and large computer manufacturers emerged. However, a sharp difference seems to emerge between the evolution of the technology in the USA and Europe.
Not many scientific stars from Europe are mentioned in the studies of history of computing after the 1970s.
It is clear that the institutionalization of computer science as an academic discipline took place earlier in the USA, approximately in the 1950s,
characterizing the search regime of computer science In a stream of recent papers (Bonaccorsi, 2007; 2008;
It is therefore useful to try to characterize the history of computer science from the point of view of the underlying abstract dynamics of knowledge.
The National Research Council (NRC) of the US National Academies has edited a number of essays from leading scientists on the state of the art of computer science, with a collective introduction (NRC, 2004.
Computer science embraces questions ranging from the properties of electronic devices to the character of human understanding, from individual designer components to globally distributed systems,
Computer science encompasses basic research that seeks fundamental understanding of computational phenomena, as well as applied research. The two are coupled often;
in computer science there is a significant overlap. Great theorists also engage in developing (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
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. seeks the fundamental limits on
or data) at many levels, preserving its fundamental properties. This makes it possible to move increasingly far from the physical implementation on a hardware without losing the relevant aspects of the description.
For example, it is possible to decouple the program from the underlying hardware representation (Shaw, 2004.
This is sharply different from what happens in most areas of engineering (as well as in the human brain).
As a prominent theoretical computer scientist summarized: The computer originated in the academic environment. 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 aircraft 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 automatization is at least not a usual industrial subject. Zemanek, 1997: 16) To illustrate the power of abstraction,
the Internet works today because of abstractions that were products of the human imagination. Computer scientists imagined‘packets'of information flowing through pipes,
and they (symbolically) worked out the consequences of that idea to determine the new laws those flows of information must obey.
This conceptualization led to the development of protocols that govern how data flows through the Internet,
On the basis of an extensive historical reconstruction and of informed reports from scientists, we can conclude that the search regime of 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,
but a law of business), granted order of magnitude increases in computing power over time, relaxing year after year the constraints on computation.
At the same time, the symbolic representational nature of computer programs made it possible to explore hundreds of different directions at relatively low cost.
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 transformation of many fields of reality,
previously represented in analogical ways, in the form of bits. This has triggered a proliferation dynamics,
The progressive digitalization of regions of reality (not only data but images, sound, movement, all sorts of physical parameters etc.
has attracted a large number of other disciplines 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 beginning, but also biology and chemistry (bioinformatics), earth sciences (geographic information systems), psychology (artificial intelligence), visual art (computer graphics), operations management (enterprise resource planning),
and many other cognitive fields. All have been transformed deeply from the relationship with computer science. In all cases, there was not just‘application,
'but, as noted above,‘fundamental insights'to be gained from this complementarity. Another form of complementarity is defined institutionally
In computer science, this complementarity comes from the constitutive interplay between theoretical work and pragmatic goals (Bonaccorsi, 2010.
New evidence on scientific excellence in 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 established in 1997 and endorsed by most scientific societies and departments in computer science worldwide.
The Citeseer service ranks scientists by the total number of citations without checking for homonyms and controlling for the age of scientists.
We downloaded from the internet all CVS of all top 1, 000 scientists in the Citeseer service,
These scientists have the largest cumulative number of citations in papers from a list of journals and conferences in computer science, irrespective of their age.
CV downloading and data processing was done manually by a team of research assistants. Information from CVS is well known to be highly informative and rich,
Self-declaration cannot be checked with any accuracy. The updating of information is totally arbitrary. The format is free and practical experience shows many instances of arbitrariness and bizarre attitudes.
Several items of data are still missing, so the analysis must be done on different samples, variable by variable.
What follows is a purely descriptive treatment of data, with limited comment. Patterns of educational mobility We identified the location of the universities at
it is almost impossible to rank high in the computer science field without a Phd from either the USA or Europe,
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
457 100.0 641 100.0 It is almost impossible to rank high in the computer science field without a Phd from either the USA or Europe, with the USA leading by a large margin European competitiveness:
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.
and place of Phd degree of top scientists in computer science Year USA Europe Asia Other Not available Total<1950 4 4 0 0 0 8
Master and Bachelor degrees to top scientists in computer science Phd degree Master degree Bachelor degree Number%Number%Number%MIT 82 9. 6 47
when we move to the Bachelor degree, the entry point for students considering a career in computer science.
The talent pool for a career in computer science is worldwide. Entry points are good universities offering strong basic scientific knowledge
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,
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.
not computer science (see Table 5). The entry point of a scientific career is not in the specialised field,
Also interesting is the group of graduate students in physics who are recognized as key leaders in computer science.
computer science is number one at the level of Master degrees, a stage in which some focusing is required.
Finally, at the Phd stage the disciplinary affiliation of computer science dominates with 38.2%of cases.
because it is considered obvious that their Phd is in computer science?).At the same time an interesting tentative interpretation can be offered.
Computer science is a relatively young discipline. It has not the long scientific history of physics, mathematics, or chemistry.
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
IT and long-term scientific performance 534 Science and Public Policy August 2011 theoretical discipline, based on advanced research in mathematics, logics, computation, probability,
Our data seem to suggest that computer science has been a gateway for cross-discipline mobility and cognitive recombination.
students with a background in human sciences (literature, linguistics, psychology) and social sciences (economics) may combine their domain expertise with advanced computer science.
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 characterized a field by a high degree of disciplinary mobility attracting competences from related fields.
which is Table 6. Transition matrix between disciplinary distribution of Bachelor and Phd degrees Bachelor degree Phd degree Mathematics Engineering Computer science Other disciplines No Phd Total
100.0 Engineering 4 41.8 69 34.5 57 17.6 29 17.6 6 3. 6 165 100.0 Computer science-2. 0 2 79.4
. 8 432 100.0 The search regime of computer science has been characterized by a turbulent rate of growth, proliferation dynamics,
We find the data illuminating. It is not surprising that top universities try to attract top scientists,
An easy way to comment these data is to remember that these are star scientists,
we are observing average data. Standard deviation informs us that even faster careers are observable. Indeed, for some people promotion to a higher level may occur within a year of the initial promotion!
It was not possible to normalize these data by age or seniority, given several missing items of data.
A crude approximation is offered in Table 9, suggesting that on average they may change country for each 30 years of age and each 15 years of professional seniority.
Our data seem to suggest that in the computer sciences the pattern of geographic mobility has been an ingredient of long-term success. Scientific productivity We offer a very rough descriptive analysis of the scientific production of top scientists.
so that any external control on the data self-declared in the CVS would require a long and dedicated investigation.
and carried out a crude comparison with ISI (now Thomson Reuters) sources, by building up a Web of Science count of publications at the end of 2005.
These cover only a subset of journals considered important in the computer science community, and do not include many top conferences,
One-fifth of the top scientists also actively produce complete software and mention it in their CVS.
This confirms the notion that institutional complementarity is an integral part of the search regime in computer science.
Number of ISI international papers 983 1 284 24.73 34.59 Other research output Software 204 1 56 4. 14 6. 081
For a large industry such as the computer industry, an overall ecology of abstract ideas, engineering capabilities, technical skills,
According to our data, top scientists move from the university that awarded their Bachelor degree to the USA,
combine different 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
But European universities have not been attractive for top computer scientists and increasingly have also become less attractive for students.
However, there is also very recent evidence that the type of brain race that we have discovered in the computer science is becoming widespread (Wildavsky
Governments considered the computer industry a sector that could be supported with the old model of industrial policy:
The search regime in computer science is based on a massive and fast effort of exploration of many competing directions,
When the two radical innovations of the PC (in the 1980s) and the internet (in the 1990s) were introduced
or because there is an intensification of effort in the front office. The former invariably requires skillful implementation of IT,
Our conjecture (admittedly, only that is that US service companies were ready to jump on the new waves of IT associated to the PC
and the internet exactly because they had experienced already the early benefits of the technology, while for European service companies the learning curve, in the same period, was much less favourable.
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