The sector thus upgrades, as knowledge is shared within value chains and spills over across chains.
mostly in central Java and Yogyakarta (540 in Yogyakarta, Tambunan 2006b. These are traditional activities of local communities where production has long been advancing.
339 421.400 Medium silver Salim silver Exporter 45 0 45 105.350 High silver KWAS Exporter 79 79 High wood AS Java
and multimodal mobility assisted by smart management and information systems. A transport system can be considered as smart
only limited attempts have been made to build a door-to-door multimodal travel information system for the cross-border European traveller.>
data sharing (formats) and data quality>move from national systems to a true European door-to-door information system and multimodal journey planner>TASKS AND ACHIEVEMENTS The ITS Directive foresees the development of functional, technical,
>equip all new type-approved vehicles with ecall devices>upgrade Public Safety Answering Points (PSAPS) to enable them to handle ecalls>achieve the full-scale roll out of ecall throughout Europe>TASKS
European Sky ATM Research Programme), RIS for inland waterways (River Information system) and VTMIS for maritime transport (Vessel Traffic Management Information system).
systems PRT Personal rapid transit RFID Radio frequency identification device RIS River information system RTTI Real-time traffic information SESAR Single European
trunked radio UHF Ultra-high frequency UMTS Universal mobile telecommunications system V2i Vehicle to infrastructure V2v Vehicle to vehicle VHF Very high frequency VTMIS Vessel traffic management information system WIM
Hungary also intends to spend nearly one-tenth of the resources coming from the Structural Funds and the Cohesion Fund in the 2014-2020 programming period directly on research & development & innovation,
The spread of information technologies may be the best example of this: the development of these technologies has been limited to only a few small regions in the world,
and spread information technologies have also been able to achieve rapid economic growth. Consequently, the research & development & innovation strategy does
this amount will increase to EUR 81 billion in the next programming period3. So if Hungary would like to maximise its use of these resources,
The coordination of the instruments of Cohesion Policy and innovation policy has been set as a specific objective by the European commission for the 2014-2020 programming period.
Similarly there may be more resources available for developing the R&d infrastructure at the Union level in the next programming period.
Besides the coordination of and increase in development resources, new types of governmental interventions are to be expected in the next programming period both at the Community level and at national level:
from the demand-side programmes to the uniform patent and standardization package, from establishing a Union-level institutional system for venture capital to supporting the Joint Programming Initiatives. 10 The size of the research and development sector and its main
and at least for the seven years of the next programming period, So, if Hungary would like to use these funding resources in a more effective way then the country has to pay more attention to RDI than the EU average.
A new approach to coordination will feature in European union competitiveness policy in the next programming period,
The support for and encouragement of participation in the European Innovation Partnerships (EIP) and Joint Programming Initiatives (JPI) in the strong national RDI areas. 3) The securing of a more efficient access to the EU programmes
way in the 2014-2020 programming period. 7) The better inclusion of the Hungarian processes of the Enterprise Europe Network into the strategy.
this region is eligible to use EU resources only in a very limited way in the programming perdiod 2014-2020,
Since the primary goal of the Structural and Cohesion Funds in the new EU programming period is to mitigate the economic differences between regions,
The 2014-2020 EU programming period is a good opportunity to establish an efficient tax-side incentive mechanism.
According to current EU programming companies will be able to use a significant part according to preliminary analysis,
if an average annual funding of HUF 100 billion is available for RDI in the programming period. 44 The gradual enforcement of competitive practices is desirable
and the demands of the 2014-2020 programming period to bring smart specialization into prominence.
o intersectoral (business-research organisation) cooperation, o establishment of an experimental development environment (living lab) based on nongovernmental initiatives, o support for open innovation models (in case of proper
information systems and services that are essential for scientific research activities and the dissemination of results. The related human resources form an integral part of RIS that enable the professional operation, use and services.
PAGE 1 Raising European Productivity Growth Through ICT BY BEN MILLER AND ROBERT D. ATKINSON JUNE 2014 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Most
PAGE 2 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Around two-thirds of U s. total factor productivity growth between 1995 and 2004 was due to ICT,
Moreover, Europe's much higher proportion of small firms makes it hard for firms to surmount the high fixed costs of many PAGE 3 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 ICT investments.
Trade policy can play a role, particularly through an expanded Information technology Agreement. Fifth, European firms would be better able to take advantage of ICT
and grow PAGE 4 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 quickly, but many other types of small firms are simply inefficient organizations that have been protected from competition.
PAGE 5 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 The diverging productivity trends also reflect important industry-level differences.
S. convergence divergence PAGE 6 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Four countries Finland, Greece, Sweden,
S. PAGE 7 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Figure 4: EU-15 productivity percent growth rate relative to U s. area of circle is relative size of country GDP) 16 Figure 5:
PAGE 8 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 $16. 7 trillion. 20 Or from a different perspective,
PAGE 9 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 On the level of individual industries, productivity gains can occur in three different ways:
Indeed, new growth economics accounting suggests that the lion's share of productivity stems from the use of more and better tools. 30 PAGE 10 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 And in today's knowledge-based economy,
and in both goods-and services-producing industries. 38 Firm level studies have shown also that PAGE 11 THE INFORMATION TECHNOLOGY
PAGE 12 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Germany. Figure 6) Similarly, a 2011 report from Coe-Rexecode finds that
but France and The netherlands under 50 percent. 52 0. 00.10.20.30.40.50.6 PAGE 13 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Figure 7:
THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 of national growth. 63 Also in Italian firms Hall, Lotti,
PAGE 15 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 WHY HAS GAINED EUROPE NOT AS MUCH FROM ICT?
PAGE 16 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Amount of ICT Investment firms in Europe do not invest as much in ICT as firms in the United states. Higher levels of ICT investment drive higher
PAGE 17 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Figure 9: Gross fixed capital formation (investments) by type as a percentage of GDP (EUR-W is weighted average of major European countries) 89 Figure 10:
but quintupled in the United states. PAGE 18 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Figure 11:
S. EU-15eu-13 PAGE 19 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 EU private service sector productivity grew only one-third as fast
PAGE 20 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Privacy regulations not only limit business models they also increase the cost of doing business for firms,
PAGE 21 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Land use regulation is a third area of regulation that leads to reduced ICT benefits, particularly in the retail sector.
Taxes on general consumption in the United states and European union, 2009121 Because the EU signed onto the 1997 Information technology Agreement (ITA),
despite Europe's higher tax 051015202530hungarydenmarkswedenestoniafinlandsloveniaaustriagermanypolandczech Republicfranceportugalbelgiumnetherlandsslovak Republicgreeceluxembourgirelandunited Kingdomitalyspainunited States%GDP%total taxation PAGE 22 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 overall.
For example, it can cost the same to develop an ERP (enterprise resource planning) PAGE 23 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 system for a mid-size firm as a large one,
PAGE 24 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Europe's second challenge regarding scale is the issue of market size.
and PAGE 25 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 developing new analytical capabilities, whereas in Europe the primary concern is straightforward cost cutting. 144 Such differences are obviously harder to influence through public policy than factors like regulation and taxes,
PAGE 26 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 companies embrace the cloud and engage in disruptive productivity growth.
PAGE 27 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Focus on Raising Productivity Many European officials see increasing jobs
so that they were covered no longer by the WTO's Information technology Agreement that was supposed to eliminate tariffs ON IT products.
since the agreement was reached in 1996 meant that some products were now consumer goods rather than information technology goods. 160 In cases like this,
and PAGE 28 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 whether promotion of the former through higher tariffs or other restrictions (like on cross-border data flows) will be detrimental to the latter.
as well as in a host of technology industry areas such as high-speed broadband telecommunications, smart cards, radio frequency identification devices (RFID), geographic information systems, mobile commerce,
In these cases, EU governments should use a wide PAGE 29 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 array of policy levers, including tax, regulatory,
and Belgium, stand in need of reforms to more fully open their service sectors with the rest of Europe. 171 In PAGE 30 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 particular,
PAGE 31 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 due to emerging data nationalism the idea that data must be stored domestically
PAGE 32 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 States has taken already and proven successful,
Clearing up the Confusion (Information technology and Innovation Foundation, August 2013), http://www. itif. org/publications/competitiveness-innovation-and-productivity-clearing-confusion. 19.
and the Ugly of Innovation Policy (Information technology and Innovation Foundation, October 7, 2010), 27-30, http://www. itif. org/publications/good-bad-and-ugly-innovation-policy;
PAGE 33 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 27. Chad Syverson, What Determines Productivity?
Clearing up the Confusion (Information technology and Innovation Foundation, August 2013), http://www2. itif. org/2013-competitiveness-innovation-productivity-clearing-up-confusion. pdf. 30.
and Daniel Castro, The Internet Economy 25 Years After. com (Information technology and Innovation Foundation, March 2010), http://www. itif. org/publications/internet-economy-25
and Kenneth L. Kraemer, Information technology and Economic Performance: A Critical review of the Empirical Evidence, ACM Computing Surveys 35, no.
1 march 2003): 1. 37. For several of the numerous literature surveys, see: Dedrick et al. Information technology and Economic Performance, 12;
Mirko Draca, Raffaella Sadun, and John Van Reenen, Productivity and ICT: A Review of the Evidence (discussion paper no. 749, Centre for Economic Performance, August 2006), http://eprints. lse. ac. uk/4561/;
and Eric Shih, Information technology and Productivity in Developed and Developing Countries, Journal of Management Information systems 30, no.
Measurement, Evidence and Implications (OECD Publishing, 2004), 96, http://www. oecd-PAGE 34 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 ilibrary. org/docserver
Understanding the Economic Benefits of the Information technology Revolution (Information technology and Innovation Foundation, March 2007), http://archive. itif. org/index. php?
and Daniel E. Sichel, Is the Information technology Revolution Over?(SSRN Scholarly Paper, March 27, 2013), 22, http://papers. ssrn. com/abstract=2240961;
Comments on‘Is the Information technology Revolution Over?''International Productivity Monitor 25 (2013): 37 40.43. S. Gilchrist, V. Gurbaxani,
and R. Town, Productivity and the PC Revolution (working paper, Center for Research on Information technology and Organizations, 2001);
New Evidence from Sector-Level Data on Developed and Developing Countries (working paper, Center for Research on Information technology and Organizations, 2001;
Lorin M. Hitt and Prasanna Tambe, Measuring Spillovers from Information technology Investments (proceedings of the 27th International Conference on Information systems, Milwaukee, WI, 2006), 1793.46.
Information technology and Productivity in Developed and Developing Countries for a cross-country study, or for specific examples see Geoff Walsham, ICTS for the Broader Development of India:
An Analysis of the Literature, The Electronic Journal of Information systems in Developing Countries 41 (2010), http://www. ejisdc. org/Ojs2/index. php/ejisdc/article/view/665 and Jyoti
Vig, Information technology and the Indian Economy (Phd diss. University of Minnesota, March 2011), http://conservancy. umn. edu/bitstream/104630/1/Vig umn 0130e 11796. pdf. For public sector literature examples, see:
Luis Garicano and Paul Heaton, Information technology, Organization, and Productivity in the Public sector: Evidence from Police departments, Journal of Labor Economics 28, no.
PAGE 35 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 53. Van Welsum et al. Unlocking the ICT Growth Potential in Europe. 54.
and R. Sabater-Sánchez, Information technology and learning: Their relationship and impact on organisational performance in small businesses, International Journal of Information management 26, no. 1 (2005): 16-29.
PAGE 36 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 67. Paul-Antoine Chevalier, Rémy Lecat,
and Nicholas Oulton, Convergence of Firm-Level Productivity, Globalisation and Information technology: Evidence from France, Economics Letters 116, no.
Where America's Broadband Networks Really Stand (Information technology and Innovation Foundation, February 2013), http://www2. itif. org/2013-whole-picture-america-broadband-networks. pdf. 82.
PAGE 37 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 89. Ibid. Major European countries included in this chart are:
The Role of Information technology and Regulatory Practices, Labour Economics 11, no. 1 february 2004): 33 58, doi:
/Meg Leta Ambrose, The Law and the PAGE 38 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Loop (Proceedings of the International Symposium on Ethics in Engineering science,
Key Information technology and Management Issues 2011 2012: An International Study, Journal of Information technology 27, no. 3 (2012): 198 212;
Talking points, Information Services Group, February 2013, http://www. isg-one. com/web/research-insights/talking-points/archive/1302. asp. 116.
and Economic growth by Expanding the ITA (Information technology and Innovation Foundation, March 2012), http://www. itif. org/publications/boosting-exports-jobs-and-economic-growth-expanding-ita. 123.
Business Impact and Productivity Measures, J. of Management Information systems 19, no. 1 (2002): 71 98.131.
+OECD, Entrepreneurship at a Glance 2013, Table 2. 2. PAGE 39 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 133.
PAGE 40 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 157. Miller and Atkinson, Are Robots Taking Our Jobs, or Making Them?
A Policymaker's Guide to Crafting Effective Innovation Policy (Information technology and Innovation Foundation, October 2010), 70, http://www. itif. org/publications/good-bad-and-ugly-innovation
and Nirvikar Singh, Information technology and Broad-Based Development: Preliminary Lessons from North India, World Development 32, no. 4 (2004): 594.
Jason Dedrick and Kenneth L. Kraemer, India's Quest for Self reliance in Information technology: Costs and Benefits of Government Intervention,(University of California, Irvine:
Graduate school of Management and Center for Research on Information technology and Organizations, December 30, 1992), http://crito. uci. edu/papers/1993/pac-005. pdf. 163.
Kenneth L. Kraemer and Jason Dedrick, Payoffs From Investment in Information technology: Lessons from the Asia-Pacific Region (University of California, Irvine:
Graduate school of Management and Center for Research on Information technology and Organizations, April 13, 2001), http://www. crito. uci. edu/git/publications/pdf/pac-037d. pdf. 164.
Kenneth L. Kraemer and Jason Dedrick, Information technology and Productivity: Results and Policy Implications of Cross-country Studies (working paper, University of California, Irvine:
Center for Research on Information technology and Organizations, February 1999), 25, http://www. crito. uci. edu/itr/publications/pdf/it-productivity-2-99. pdf. 165.
Understanding the Benefits of the IT Revolution (Information technology & Innovation Foundation, October 1, 2008), http://www. itif. org/publications/digital-quality-life-understanding-benefits-it-revolution. 167.
Hitt and Tambe, Measuring Spillovers from Information technology Investments, 1793; Xavier Sala-i-Martin, 15 Years of New Growth Economics:
Stephen Ezell and Robert D. Atkinson, How ITA Expansion Benefits the Chinese and Global economies (Information technology and Innovation Foundation, April 2014), http://www. itif. org/publications
and Growth in the EU (Oxford Economics/AT&T), accessed October 3, 2013, http://www. corp. att. com/bemoreproductive/docs/capturing the ict dividend. pdf. PAGE 41 THE INFORMATION TECHNOLOGY & INNOVATION
Daniel Castro, The False Promise of Data Nationalism (Information technology and Innovation Foundation, December 2013), http://www2. itif. org/2013-false-promise-data-nationalism. pdf. 183.
ICT-Enabled Benefits for EU Society Digital Agenda for Europe, PAGE 42 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 Enterprise and Industry,
PAGE 43 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 ACKNOWLEDGEMENTS The authors wish to thank Stephen Ezell, ITIF,
ABOUT THE AUTHORS Robert Atkinson is the founder and president of the Information technology and Innovation Foundation.
Ben Miller is an economic growth policy analyst at the Information technology and Innovation Foundation. He has a Master's degree in International Development and Economics from Johns Hopkins School of Advanced International Studies.
ABOUT ITIF The Information technology and Innovation Foundation (ITIF) is a Washington, D c.-based think tank at the cutting edge of designing innovation strategies and technology policies to create economic opportunities
for purchasing, accounting, or computing. Denominator: Total number of SMES. Rationale: Technological innovation as measured by the introduction of new products (goods or services) and processes is key to innovation in manufacturing activities.
electronic and optical products (26) Air transport (51) Publishing activities (58) Motion picture, video and television programme production, sound recording and music publishing activities (59) Programming
and broadcasting activities (60) Telecommunications (61) Computer programming, consultancy and related activities (62) Information service activities (63) Financial service activities,
but it inevitably requires retraining of users in the next upgrade, and can increase scepticism. Risk of errors and inefficiencies increases when organisations are forced to run paper and computer systems in parallel. 8,
Nurses and information technology. Final report. Canberra: Commonwealth of australia, 2007. http://www. anf. org. au/it project/PDF/IT PROJECT. pdf (accessed Aug 2010). 7 Conn J. Failure,
76: 583-591.21 Fonkych K, Taylor R. The state and pattern of health information technology adoption.
Information technology and changes in organizational work proceedings of the IFIP WG82 working conference on information technology and changes in organizational work.
11: 100-103.26 Beynon-Davies P, Lloyd-Williams M. When health information systems fail. Top Health Inf Manage 1999;
the majority of countries in the core are developed countries with a relatively 3 This algorithm is implemented in UCINET software Borgatti, S. P.,Everett, M. G. & Freeman, L. C. 2002.'
and is computed through fractional counting of inventors in each priority patent application submitted in 2007 to one of 59 patent offices around the world. 8 Our methodology of computing patent statistics for the purpose of this paper follows (De Rassenfosse
'An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality.'
combined with the Pagerank search algorithm, powered their success. There is no doubt that, at Stanford, Google's founders benefited from a very supportive
its prime locations were Tallinn (for software engineering), Palo alto (for VC) and London (for general business).
This is the case in the Oracle plea against Google in April/May 2012 over the use of the Java language to write APIS for the Android operating system. 21 Patent pools may be used where a standard complex technology is being assembled
Patents outside the pool become 21 Java is owned by Oracle, and its open source status has always been in a grey,
The use of a programming language to create the APIS has been considered‘fair use'in the past. Moreover the knowledge of how APIS work is essential for the ICT industry to build compatible software modules for instance the PC industry could not have been created without it,
where the source code as well as any updates are openly available to its users and creators. Although most related to the software industry, in theory open source can be employed to share original IPR in any sector. 23 By using open source software, in accordance with the open source licence, the SME gains significant protection from infringements on proprietary
as the source code and thus its concepts are openly available to all. Note that the freedoms
(whose origins lie in Steve jobs'Next Nextstep operating system, based on the MACH kernel from Carnegie mellon University, with Freebsd source code extensions), Linux,
27 Of the top 50 departments in the world in different subject areas, the majority are found in the USA (39 of 50 in computer science, 33 in engineering, 37 in neuroscience)( Technopolis, 2011;
A Roadmap for US ROBOTICS From Internet to Robotics, Computing Community Consortium and Computing Research Association, http://www. us-robotics. us/reports/CCC%20report. pdf Chandy, R.,Tellis, G,
and Innovation, http://www. nap. edu/catalog. php? record id=11989 Westerberg, U.,(2009), VINNOVA Report VR 2009: 19, The Public sector-one of three collaborating parties.
Apple has changed radically, turning away from pure hardware and software for computing to combining bundles of web services.
and away from pure computing. Thus Apple's major business segments have advanced out of hardware and software products for personal computing and graphics into global retail services, with chains, of both online and physical shops.
Its websites for interactive and download services are aimed at a higher-end mass market. The net result is verticalization:
CA in 1998 by Larry page and Sergey Brin while they were Phd candidates in computer science at Stanford university.
(ex Netscape), David Cheriton (Stanford computer science Professor), and Jeff Bezos41 (Amazon). In June 1999, a $25 million round of funding was announced,
Consequently the algorithms behind Pagerank were guarded zealously. However, as Phd research students, they were expected to present their work
Stanford university seem to have extended considerable tolerance to Page and Brin, turning a blind eye to them acquiring computing resources.
Plastic Logic sees itself as leading a revolution in visual information technology. Founded in 2000 by researchers at the Cavendish Laboratory of Cambridge university in the UK,
In many areas, KUKA has benefited from the control algorithms, technologies and software developed by DLR,
-KUKA uses DLR's model-based minimum cycle time algorithms for high-speed spot welding for car assembly lines.
These include specific subsystem engineering, prototyping, complete robotics system development, studies and assessments and also robot reconfiguration and upgrades.
simplified robot programming; faster robot reconfiguration; mobile platforms. Industrial projects-for SME users able to exploit robotics with feasibility studies (using local university students and mentors),
36 5. 1. 2 Academic ranking of a Computer science Faculty...38 5. 1. 3 Employer Ranking of a Computer science Faculty...
40 5. 1. 4 Citations Ranking of a Computer science Faculty...42 5. 1. 5 R&d Expenditures by ICT Firms...
44 5. 1. 6 ICT FP7 Funding to Private Organisations...46 5. 1. 7 ICT FP7 Participations...
56 5. 1. 12 Scientific Publications in Computer science...58 5. 1. 13 Outward ICT R&d Internationalisation...
32 Academic ranking of a Computer science faculty Agrd 2 10 Employer ranking of a Computer science faculty Agrd 3 11 Citations ranking of a Computer science faculty Agrd 4 29 R&d
of ICT R&d centres Agrd 11 7 Scientific publications in Computer science Agrd 12 23 Internationalisation Outward ICT R&d internationalisation Intrd 1 5
18 Academic ranking of a Computer science faculty Agrd 2 7 Employer ranking of a Computer science faculty Agrd 3 3 Citations ranking of a Computer science faculty Agrd 4 6 R&d
of ICT R&d centres Agrd 11 16 Scientific publications in Computer science Agrd 12 4 Internationalisation Outward ICT R&d internationalisation Intrd 1 16
of a Computer science faculty Agrd 2 8 Employer ranking of a Computer science faculty Agrd 3 8 Citations ranking of a Computer science faculty Agrd 4 4 R&d expenditures by ICT
centres Agrd 11 4 Scientific publications in Computer science Agrd 12 13 Internationalisation Outward ICT R&d internationalisation Intrd 1 4 Inward ICT
the EIPE ID card Activity Characteristic Name of Indicator Indicator ID Nr R&d Agglomeration Universities ranked in the QS University ranking Agrd 1 1 Academic ranking of a Computer science
faculty Agrd 2 2 Employer ranking of a Computer science faculty Agrd 3 3 Citations ranking of a Computer science faculty Agrd 4 4 R&d expenditures by ICT firms
R&d centres Agrd 11 11 Scientific publications in Computer science Agrd 12 12 Internationalisation Outward ICT R&d internationalisation Intrd 1 13 Inward
Academic ranking of a Computer science Faculty Table 15: Top ranking regions according to the Academic Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKH12
Cambridgeshire CC 100 5 2 UKJ14 Oxfordshire 87 19 3 UKI22 Outer London-South 73 114 4 UKM25 Edinburgh, City
UKE21 York 27 63 29 ITD55 Bologna 27 76 Indicator description Indicator ID Agrd 2 Name of indicator Academic ranking of a Computer science faculty
Measures the performance of the Computer science faculty according to the academic ranking of QS Unit of measurement The highest rank of a Computer science faculty in the academic ranking Definition of ICT dimension Computer science faculty Unit of observation NUTS
Frequency of the Academic Computer science faculty QS Ranking indicator values 1244 1 5 5 12 11 8 9 2 2 1 1
1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Academic ranking of a Computer science faculty Table 16:
Descriptive statistics of the Academic Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 38 7. 25 52.59 40 5
. 1. 3 Employer Ranking of a Computer science Faculty Table 17: Top ranking regions according to the Employer Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKH12
Cambridgeshire CC 100 5 2 UKJ14 Oxfordshire 95 19 3 UKI12 Inner London-East 68 2 4 UKI22 Outer London
30 GR300 Attiki 28 49 Indicator description Indicator ID Agrd 3 Name of indicator Employer ranking of a Computer science faculty What does it measure?
Measures the performance of the Computer science faculty according to the employer ranking of QS Unit of measurement The highest rank of a Computer science faculty in the employer ranking Definition of ICT dimension Computer science faculty Unit
Frequency of the Employer ranking of a Computer science faculty indicator values 1244 1 3 3 12 12 11 6 6 1 1 1 2 0
500 1000 1500 Frequency 0 20 40 60 80 100 Employer ranking of a Computer science faculty Table 18:
Descriptive statistics of Employer Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 47 7. 63 58.27 42 5
. 1. 4 Citations Ranking of a Computer science Faculty Table 19: Top ranking regions according to the Citations Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKL12
Gwynedd 100 266 2 PL127 Miasto Warszawa 91 50 3 NL335 Groot-Rijnmond 77 72 4 FR101 Paris 75 3
Gent 37 94 Indicator description Indicator ID Agrd 4 Name of indicator Citations ranking of a Computer science faculty What does it measure?
Measures the performance of the Computer science faculty according to the citations ranking of QS Unit of measurement The highest rank of a Computer science faculty in the citations ranking Definition of ICT dimension Computer science faculty Unit
Frequency of the Citations Computer science faculty QS Ranking indicator values 1243 3 11 10 9 6 7 3 2 3 2 2
1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Citations ranking of a Computer science faculty Table 20:
Descriptive statistics of Citations Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 94 9. 57 91.58 44 5
. 12 Scientific Publications in Computer science Table 35: Top ranking regions according to scientific publications in Computer science indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 NL333 Delft en
Westland 100 17 2 DE138 Konstanz 93 53 3 DE711 Darmstadt, Kreisfreie Stadt 89 7 4 UKI12 Inner London-East
Port Talbot 32 272 Indicator description Indicator ID Agrd 12 Name of indicator Scientific publications in Computer science What does it measure?
in the Computer science area produced by organisations located in the observed region Unit of measurement Region's share in the total number of publications in Computer science to a region's share in the EU population Definition of ICT dimension Computer science as defined by Web
Frequency of the scientific publications in Computer science indicator values 1172 38 18 21 13 8 12 2 3 4 1 2 1 2
Descriptive statistics of scientific publications in Computer science indicator Number of observations Mean value Standard deviation Variance 1303 2. 32 9. 45 89.45 60 5. 1. 13
of indicator Universities ranked in the QS University ranking Academic ranking of a Computer science faculty Employer ranking of a Computer science faculty Citations ranking of a Computer science faculty R&d expenditures by ICT firms ICT FP7 funding
Measures the number of universities in QS university ranking Measures the performance of the Computer science faculty according to the academic ranking of QS Measures the performance of the Computer science faculty according to the employer ranking of QS Measures the performance
of the Computer science faculty according to the citations ranking of QS Measures the average annual amount spent on R&d in the ICT sector Measures the amount received for research in ICT R&d Unit of measurement Region's share in the total
number of EU ranked universities to a region's share in the EU population The highest rank of a Computer science faculty in the academic ranking The highest rank of a Computer science faculty in the employer ranking The highest rank of a Computer science
ICT firms in the EU to a region's share in the EU population Region's share in the total EU ICT FP7 funding to a region's share in the EU population Definition of ICT dimension None Computer science faculty Based on NACE
Scientific publications in Computer science What does it measure? It measures the total number of ICT R&d FP7 projects to which organisations,
in the Computer science area produced by organisations located in the observed region Unit of measurement Region's share in the total number of ICT FP7 participations to a region's share in the EU population Region's share in the total EU ICT
in Computer science to a region''s share in the EU population Definition of ICT dimension ICT areas of the FP7 programme Based on HIS isuppli classification of the major"semiconductors influencers"Computer science as defined by Web of Science classification of Research
Areas Unit of observation NUTS 3 Source ICT FP7 by EC DG CONNECT (see Section 8. 2) R&d Centre location by IHS isuppli (Section 8
Data Sources 8. 1 QS WORLD UNIVERSITY RANKINGS by QS The Computer science and Electronic Faculties rankings originate from the QS WORLD UNIVERSITY RANKINGS,
which there is the Computer science subject considered appropriate for the EIPE study. To construct measures of faculty performance,
For the purpose of the EIPE exercise, journals classified in the Computer science research area are considered. 8. 4 R&d Centre Location by IHS isuppli The data used for the purpose of identification of R&d centre locations
activities, 612-Wireless telecommunications activities, 613-Satellite telecommunications activities, 619-Other telecommunications activities, 6201-Computer programming activities, 6202-Computer consultancy activities
, 6209-Other information technology and computer service activities, 6311-Data processing, hosting and related activities, 6312-Web portals, 9511-Repair of computers and peripheral equipment,
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