Synopsis: Ict: Data:

were used to solicit for relevant data. Collected data was presented and analysed using tables, bar charts and pie charts as extracted from Statistical Packages for Social sciences (SPSS). The

hypothesis test was conducted using the SPSS package. On the findings, innovation was found as one of the major attributes which aid SMES to remain competitive.

to asses the true nature of SME failure due to lack of accurate data on this phenomena.

Data was collected mainly using structured interviews and questionnaires and analysed using Statistical Packages for Social Studies (SPSS

bring opportunities for integrating data for journey planning and electronic ticketing, and smart cards to facilitate interoperability between public transport

has funded various projects on data collection monitoring and analysis of modal effects and ON ITS for integrated traffic management

transport data from all over Europe by means of common mechanisms, standard rules, and protocols. This easy to install

database, January 2012, Brussels INNOVATION IN URBAN MOBILITY-POLICY MAKING AND PLANNING 15 SU CC

The portal uses Europe†s largest vehicle database and provides energy consumption and emission data on vehicles as well as an online calculator

for lifetime costs of vehicles, as required by the Directive 2009/33/EC. An internet forum enables

and integrate freight data in urban mobility statistics 22 INNOVATION IN URBAN MOBILITY-POLICY MAKING AND PLANNING

CARE-EU road accidents database, January 2012, Brussels â European Green Cars Initiative (2012: Public-Private Partnership European Green Cars Initiative

CARE European road accidents data base CIVITAS Clean and Better Transport in Cities CNG Compressed Natural gas

data were not fully available for each case. From 1980 to 1987, ANVAR changed its administrative

the SPRU innovations database revisited. Research Policy 26 (3), 19†32 Von Hippel, E.,1987.

error due to the confidential nature of the data and the variance among participating firms (Dess & Robinson, 1983.

•Further Investigation of Clustering •Part-time Innovation drivers •Development of more Innovation Centres

Annex 3. Statistical data...44 Figures Figure 1: main concepts explaining innovation...2 Figure 2:

Data is triangulated from three sources: semi-structured interviews, survey and secondary data Data collection was based on a detailed case study protocol.

I was supported by a research assistant for translation, transcription, administration and logistics. 42 semi-structured

interviews were conducted, comprising 27 firms, 3 experts, 11 major players of the local innovation system and one global buyer.

The traditional clustering of firms per type of material has been replaced by clustering around market demand

Table 1: survey sample per subsector and firm size (turn over Firm-size W ood Pottery

became part of a larger clustering of firms and the speed of innovation became even faster (Resp

stock as well as a large database of potential buyers. The interplay between market demands for innovation and externalities thus push the sector towards middle to high end product markets

as the data received proved unreliable Innovation creates economic rents, especially relational and product rents. They can be

database of potential buyers, a large pool of actual buyers and a large product range4. The practice of subcontracting creates flexibility

own data. Firms in Yogyakarta didn†t fit the bill, as subcontracting makes it hard to control

The importance of clustering in economic development has changed radically the past decades for various reasons.

In this study clustering correlates significantly and negatively with innovation. The relationship is linear (Î=0. 017, Î=-0. 476, r2=0. 0489.

controlled for absorptive capacity and position in value chain, then poverty-driven clustering no longer has a significant impact on innovation et al (see next section.

other studies in Indonesia where clustering has no significant impact either IHS Working Paper 27.2013.

The analysis is based on qualitative data Capacity of non-firm actors and research incentives Interaction with non-firm actors plays a secondary role in innovation.

adjusted, but as a result the data on the bottom-end subcontractors must be treated with

and interpreting innovation data. Paris: OECD publications OECD 2006. Comnmunity Innovation Statistics. From today†s community innovation surveys to better surveys tomorrow

Promoting small and medium entreprises with a clustering approach: A policy experience from indonesia. Journal of Small Busines Management 43 (2): 138-54

data collection IHS Working Paper 27.2013. Innovation in SMES. The case of home accessories in Yogyakarta, Indonesia 42

Annex 3. Statistical data Table 1: association of acquisition indicators (V-Cramer/significance Travel Language Access to

Annex 3. Statistical data

In the natural world there is n o w aste D etritu s fro m

Using data from Germany, BASF factored together energy costs and consumption, purchasing costs, and other environmental and

human genes, the status of the data bases built up in functional genomics and the scope of patent claims on

resources (health data, family histories blood samples, etc. legitimately be obtained? is informed consent of the

companies and stored in private data bases? Is it legitimate to reserve exclusive access to data bases for just

one company •Frequently, private companies collect genetic materials by appealing to altruism, conveying the message that

•With respect to gene sequence data there is a growing consensus that these data be disclosed and made

freely available to all scientists. Are there reasons to apply that policy to data bases in functional genomics

Can one learn from the model case of the SNP (Single Nucleotide Polymorphisms) consortium •What is the proper scope for patent

Empirical data suggests that small firms file for less patents abroad than do large firms (e g.

use of the information contained in patent databases Various studies have shown that the use of patent

patent databases, copyright and other IP rights. Poor IP management skills within SMES reduce their

databases may be limited of use. This is why a number of IP offices provide value-added technological information services, turning the raw

information provided by patent databases into more workable knowledge that can be of practical use to

exploitation of IP rights, the use of patent databases the valuation of IP assets and the enforcement of IP

Cataloguing data can be found at the end of this publication Luxembourg: Publications Office of the European union, 2011

and travel data...pp. 8†12 Action area 2: Continuity of traffic and freight management ITS services

integration †by linking all sources of data to produce valuable information for transport users and operators

traffic and travel data Many ITS applications rely on an accurate knowledge of the road network and of traffic regulations like one-way streets

Optimal use of data will also facilitate multimodal journey planning pages 13†16 >Action area 2:

The handling of data †notably personal and financial †in ITS applications raises a number of issues as citizens†data

-protection rights are at stake. Data integrity and confidentiality must be ensured for all parties involved, especially citizens

The provision and use of ITS applications also create additional requirements in terms of liability. These issues could be a major

Given advances in data-collection technology and with growing demand for more precise and real-time information

the need for more †and better †data is increasing all the time. Yet differences between national policies on traveller

sectors as well as rules for cooperation on data exchange content and service provision >AIMS >make private, especially safety-related, traffic

-and travel-related data >promote public†private cooperation to improve traffic and travel information >increase data quality and improve multimodal

cooperation >encourage (cross-border) data exchange >TASKS AND ACHIE VEMENTS The European commission in 2011 completed a study on

traffic and travel data access, with a view to analysing the status quo in the EU and producing draft policy options

Specifications and procedures should be established for the use of public data; data availability, formats, exchange

and (cross-border) procedures; and legal issues (contracts agreements, licences, liability. Harmonisation should make it easier to develop Europe-wide traffic

and travel information services Definition of procedures for the provision of EU-wide real-time traffic and travel

•provision of traffic regulation data by the transport authorities •guaranteed access by public authorities to safety-related information collected

•guaranteed access by private companies to relevant public data DGMOVE brochure ITS A4 indd 8 11/05/11 15:

Accurate road data is needed for in-car navigation devices as well as for travel planners and all kinds of traffic-management

However, data shortcomings are restricting the ability of in-car systems to consider traffic-management plans

countries on the collection of road and traffic-regulation data have been uneven and often completely lacking.

and data formats for the collection of road data and traffic-regulation data in all EU Member States

>establish common minimum requirements and standards regarding the timely and coordinated updating of this data in all EU Member States

>establish common minimum requirements attributes and data formats for recommended routes, in particular for heavy goods vehicles

>TASKS AND ACHIE VEMENTS Building on the results of the actions on real-time traffic and travel

data for digital maps (Action 1. 3), the European commission will launch a study to analyse the status quo concerning road-data

collection and the provision and reuse of traffic circulation plans traffic regulations and recommended routes in the EU. Looking

road data. road data Optimisation of the collection and provision of road data and traffic circulation plans

traffic regulations and recommended routes (in particular for heavy goods vehicles Optimised collection and provision of road

traffic and travel data DGMOVE brochure ITS A4 indd 9 11/05/11 15: 15t105146 cee. pdf 11t105146 cee. pdf 11 20/06/11 13: 5020/06/11 13:50

The problem has been that the road data needed to produce them is not always available, accurate or

databases maintained by thousands of European road authorities in a standardised, non-discriminatory and transparent way

data for use in digital maps in the EU >define procedures for ensuring fair, simple and

transparent access to this road data for digital map providers >identify common minimum requirements regarding

road-data collection for digital maps, and of the technical and standardisation needs, is ongoing.

and existing or planned national and European spatial data infrastructures, the ongoing study will try to provide

designed to ensure timely data dissemination >>For further information on the topi ht p://ec. europ. eu/tran port/its/road

of availability of accurate public data forof availability of accurate public data forof availability of accurate public data for

Definition of procedures for ensuring the availability of accurate public data for digital maps and their timely updating through cooperation between the relevant public

Availability of accurate public data for digital maps DGMOVE brochure ITS A4 indd 10 11/05/11 15:

Definition of specifications for data and procedures for the free provision of minimum universal traffic information services (including definition of the repository of

>address issues of data availability, 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 ACHIE VEMENTS The ITS Directive foresees the development of functional

exchange of traffic data and information across borders, regions and urban/interurban interfaces enabling door-to-door and truly multimodal travel

data exchange for traffic management and travel information) specifications >finalise the adoption of required specifications for

and travel data exchangeand developed as a traffic and travel data exchange mechanism by a European task force set up to mechanism by a European task force set up to mechanism by a European task force set up to

confidentiality and secure handling of data, including personal and financial details, and show that citizens†rights are fully

of data in ITS applications and services and propose measures in full compliance with EU legislation

Cataloguing data can be found at the end of this publication Luxembourg: Publications Office of the European union, 2010

CCTV) security systems to more advanced applications integrating live data and feedback from aâ variety of information sources (e g. parking guidance, weather information

•Various forms of wireless communication for both short-range and long-range data exchange UHF, VHF, Wimax, GSM, etc

data (from devices such as radar, RFID readers, infrared-and visible-band cameras) and infrastructure-based data (from similar devices,

as well as inductive or pressure sensors installed or embedded in and around the road To meet the challenges of achieving virtually accident-free, clean and efficient mobility

and travel data •continuity of traffic and freight management ITS services in European transport corridors and

detailed GPS navigation and road/traffic data, including local roadworks. The in-vehicle technologies needed 3g telecom

computerised data, to enable vehicles to †understand†the environment around them. They facilitate control, accident

•Speed alert †using satellite navigation data to signal thatâ aâ vehicle is travelling too quickly when approaching

and growing range of available data sources and types, the impact of potential information overload on the primary task

Centralised processing of data on the natural and infrastructure conditions of aâ road network makes it possible to generate alerts,

be combined with data from moving vehicles to provide operators, maintenance authorities and road users with

real-time data to aâ central server, where it can be analysed byâ sophisticated prediction and decision-making models

†Closing the loop by using the vehicles themselves to send data back to traffic control centres

so that they can exchange data with roadside infrastructure, display information to the drivers (or passengers on public transport) and communicate

which the data can be managed, will greatly increase the quality and reliability of personalised information available to

The same data can also be used to extend the functionality of in-vehicle safety systems †for example, by constructing

and context-specific data, trusted travel assistants will be able to plan each journey and guide travellers throughout

accessibility, based on data provided via RTTI services Forâ passenger transport, the envisaged systems embrace all

-time data for pre-trip planning and on-the spot response to changing needs or conditions

data for individual bus stops or rail stations, so that DRT could be fully coordinated with the fixed line services †which

personalised data Existing systems for journey planning and route guidance tend to be limited to single forms of transport or even single

freight transport, including digital mapping, the monitoring of dangerous goods and live animals, and interoperability of

open platform which will facilitate data sharing and exchange from different sources and provide data processing and

systems will integrate data from vehicles, to provide dynamic, predictive and adaptive control of traffic flows

data collection and information exchange via mushrooming social networking websites C H A p T E R 8

and transport data from various sources with an emphasis on quality, standardisation and cost-efficiency

s and the most recent data was released by the CSO on 19 february 2013. This survey examines R&d activities performed

s survey data is represented in the following charts by a perforated line If you require further information about this survey please contact

Where data for 2011 was unavailable the next closest year was used 0. 16 %0. 16 %0. 17

Comparing 2011 data from Figure 12 and 13 shows the occupations spending most of their time on R&d (as determined

In this section data gathered on the number of R&d-active companies and the levels of R&d

of doing business for a wide range of data providers, and restrictions on cloud provider

â€oeinternet of Things, †data analytics and big data, IT-powered robotics, intelligent agents mobile commerce, improved self-serve kiosks, 3d printing, location awareness, and

OECD data show that from 1985 to 2010, ICT capital contributed 0. 53 percentage points to the average annual GDP growth rate in the United states and 0. 56 percentage points in

Internet access and standardized data exchange with trading partners contributed to significant increases in labor productivity. 60 Similarly, Koellinger finds that firms in the EU

ICT investment shows up in survey data on ICT use as well. The 2013 and 2014 World

area of big data analytics Regulations don†t just increase costs†poorly-designed or unresponsive regulations can

proposed data mining and data collection taxes, directed specifically at large internet companies such as Google and Facebook. 125 Higher taxes on ICT-producing companies

data collection would tax companies based on the number of users they collect data on apparently with no regard to the actual market value of the data

Another important channel through which tax policies influence investment is depreciation rates†the rates at which corporations can write off capital investments for tax purposes. 126

countries have focused recently on building their own domestic data centers, rather than ensuring that European ICT users have access to the cheapest and highest quality cloud

data providers This focus on the ICT-producing sector appears to be misplaced. Rohman finds that the

it still leads to efforts to get a cloud data center in rural France, instead of helping French

-border data flows) will be detrimental to the latter Yet, even if raising tariffs might lead to some offsetting production of the good or service in

usage, and data. They should allow companies to more rapidly depreciate ICT investments for tax purposes, including allowing firms to expense them in the first year

due to emerging â€oedata nationalism††the idea that data must be stored domestically in order to keep it secure.

Data nationalism is a â€oefalse promise†because it is unlikely to deliver the expected benefits of privacy and security,

down ICT-related growth. 182 Unfortunately, data nationalist policies are already a reality in some countries:

communication so that data never physically crosses the Atlantic. 184 By definition, the result of these kinds of policies will be to raise the costs of ICT services for firms in these

The responsible use of data can lead to productivity gains and innovation. However, overly stringent privacy rules limit the ability

1. The Conference Board, Total Economy Database: January 2014 (total GDP EKS, labor productivity per hour worked EKS;

-board. org/data/economydatabase/;/author calculations following Marcel P. Timmer et al. â€oeproductivity and Economic growth in Europe:

2. The Conference Board, Total Economy Database 3. Ibid 4. Ibid 5. Ibid 6. Mary O†Mahony and Bart van Ark, eds.

The Conference Board, Total Economy Database: January 2014 (Table 5 accessed April 2, 2014), http://www. conference-board. org/data/economydatabase

/10. Ibid 11. Ibid 12. Ibid. Note that EU-28 productivity actually decreases due to the less-productive EU-13 increasing their

Data unavailable for Croatia, Estonia, Latvia, and Slovenia 16. Ibid 17. Ibid. Data unavailable for Croatia, Estonia, Latvia and Slovenia;

Romania excluded because its extremely low initial productivity makes it an outlier 18. Robert D. Atkinson, â€oecompetitiveness, Innovation and Productivity:

The Conference Board, Total Economy Database: January 2014 (total GDP EKS, labor productivity per hour worked EKS;

-board. org/data/economydatabase/;/Timmer et al. â€oeproductivity and Economic growth in Europe. †Assuming 2. 8 percent productivity growth

The Conference Board, Total Economy Database. Assuming 1. 6 percent productivity growth 21. Ibid. Assuming yearly productivity growth for EU-15 after 1995 was the actual rate for the United states

Guidelines for Collecting and Interpreting Innovation Data (OECD 2005 29. Robert D. Atkinson, â€oecompetitiveness Innovation and Productivity:

A Survey of the Literature, †OECD Digital economy Papers, no 195 (2012), http://dx. doi. org/10.1787/5k9bh3jllgs7-en;

for the United states, 1947-2010†(presentation to the Final World Input-Output Database Conference Groningen, The netherlands, April 2013), 24

http://www. worldklems. net/data/notes/jorgenson ho samuels. USPRODUCTIONACCOUNT. pdf 42. Ibid. 30; David M. Byrne, Stephen D. Oliner,

-Level Data on Developed and Developing Countries†(working paper, Center for Research on Information technology and Organizations, 2001;

Evidence from Firm-Level Data, †Electronic commerce Research 9, no. 3 (2009): 173-81 61.

-level evidence using data envelopment analysis and econometric estimations, †OECD Science, Technology and Industry Working papers, no. 2002/13 (September 2002), http://dx. doi. org/10.1787/101101136045

OECD, Country Statistical Profile 2012 (Investment Data and Shares of ICT Investment in Total Nonresidential GFCF;

) The Conference Board, Total Economy Database: January 2014 (Table 5; accessed April 2 2014), http://www. conference-board. org/data/economydatabase

/85. National Science Foundation, Science and Engineering Indicators 2014 (Figure 6-7, ICT business and

OECD Statextracts, Productivity Database By Industry 2012. Growth of labour productivity, in per cent, Business Services Sector;

Transmitting Data, Moving Commerce†(European Centre for International Political economy/U s Chamber of commerce, March 2013 https://www. uschamber. com/sites/default/files/legacy/reports/020508 economicimportance final revi

Jacob Albert, â€oefrance Wants to Tax Data mining, and It†s Not a Bad Idea, †Quartz

Data for the EU Member States, Iceland and Norway Luxembourg: European commission-eurostat, 2013 130. Lorin M. Hitt, D. J. Wu,

Daniel Castro, â€oethe False Promise of Data Nationalism†(Information technology and Innovation Foundation, December 2013), http://www2. itif. org/2013-false-promise-data-nationalism. pdf

183. See for example: â€oeprocessing of sensitive personal data in a cloud solution, †Datatilsynet, February 3 2011, http://www. datatilsynet. dk/english/processing-of-sensitive-personal data-in-a-cloud-solution/,and

David Jolly, â€oeeuropean Union Takes Steps Toward Protecting Data, †New york times, March 12, 2014 http://www. nytimes. com/2014/03/13/business/international/european-union-takes-steps-toward

-protecting-data. html 187. David Streitfeld, â€oeeuropean Court Lets Users Erase Records on Web, †New york times, May 13, 2014

17 Department of Broadband, Communications and the Digital economy. Aus -tralia†s digital economy: future directions. Final report.

Canberra: Common -wealth of Australia, 2009. http://www. dbcde. gov. au/data/assets/pdf file/0006

/117681/DIGITAL ECONOMY FUTURE DIRECTIONS FINAL REPORT. pdf accessed Aug 2010 18 Coye M, Kell J. How hospitals confront new technology.

17 Department of Broadband, Communications and the Digital economy. Australia†s digital economy: future directions. Final report

18 Coye M, Kell J. How hospitals confront new technology. Health Aff (Millwood) 2006; 25: 163-173

working paper presents original data on innovation strategies for smart specialisation (RIS3) in European union (EU) regions and Member States, obtained from the Eye@RIS3 open data tool for

sharing information on the areas identified as priority areas by 198 innovation strategies. It also

Finally, we compare the main areas of planned investment with sectoral data on firms, employment and patents, with the conclusion that the connection between priorities and the

smart specialisation, prioritisation, innovation policy, open data, structural funds Acknowledgements The authors would like to express their gratitude to a number of colleagues for their kind

the development of the Eye@RIS3 tool and the database 2 1. Introduction This working paper presents the first comprehensive mapping of innovation priorities and

developed Eye@RIS3, an open data tool for gathering and diffusing information on the envisaged

use these data to give an overview of the most common priority areas and to explore the extent to

One of the main challenges when collecting data on these domains or prioritised areas is

3. Developing an open data tool for mapping innovation priorities Eye@RIS3 is an interactive open data tool that gives an overview of the envisaged RIS3 priorities of

regions and countries in Europe. The tool gives regional and national innovation communities visibility and an opportunity to be recognised by potential counterparts looking for collaboration in

open data tool to help strategy development and to facilitate interregional and transnational cooperation, rather than as a source of statistical data.

The majority of data have been added by S3 Platform staff and a minority by policy makers themselves

To have listed priorities in the Eye@RIS3 database does not mean that the particular strategy or

priorities have been approved by the Commission as meeting the RIS3 ex ante conditionality criteria. Furthermore, the listed priorities have not been verified as being areas of strength.

Currently, the data consist of 1 307 priorities from 20 EU countries, 174 EU regions, 6 non-EU

countries without regional RIS3, national data have been added. In total, the sample covers almost all of the EU-28 territory, with the exception of three Italian regions

The database contains data at NUTS1, 2 and 3 levels, since there are large variations in our sample

5) The data used in this paper were retrieved on 5 december 2014, at which time there was almost full coverage across

Since then, additional data have been added 6 Regional and national innovation priorities are at the heart of the database.

For each priority, we have information on four main categories, as follows:(1) a free-text description of the priority,(2

The database also contains information on the source of each entry With regard to data quality, there are a number of caveats.

First of all, the data are not yet suitable for econometric analyses, since all entries must be confirmed and double-checked against the final

versions of strategies. However, the database is continuously being updated with the aim of having up-to-date information.

When the negotiations of Operational Programmes and the implementation of Action Plans are finalised, the data can be validated fully.

It must be kept in mind that, originally the main rationale for developing the tool was to increase transparency

category names in the Eye@RIS3 database; we have merged these in a umbrella terms. The most

This figure is based on data from 218 regions and countries from the Eye@RIS3 database.

of all regions and countries in the database (n=198. The x-axis depicts the degree of correspondence of regional and

data entry among the sub-categories. The most common combinations of the subcategories are listed in Table 9

This figure is based on data from 218 regions and countries from the Eye@RIS3 database.

Looking at sub-category data, we found that, grosso modo, regions and countries have not chosen

data. In total, there were 231 combinations of 1 307 encoded priorities. The by far most common

This figure is based data from 218 regions and countries from the Eye@RIS3 database. The y-axis is the share of all

regions and countries in the database (n=198. The x-axis depicts the degree of correspondence of regional and national

small extent, be an outcome of our coding and interpretation of data. However, in general, we do

will now examine data on their actual economic structure. This helps us to better understand the

For this, we have used Eurostat data on the number of organisations, employment data and patent applications in absolute terms,

as well as growth figures in absolute and relative terms. We have compared these data with the most common RIS3 priorities to determine how the

priorities relate to the economic structure. This analytical exercise does not allow regional matching but looks at EU totals

are reflected not strongly in the data on local units in absolute numbers We also looked at the sectors that,

SBS data by NUTS 2 regions and NACE Rev. 2 (from 2008 onwards), number of local units

Eurostat employment data for 2010, SBS data by NACE Rev. 2 for the EU-28 (and Norway) with missing data for

No data were available for the wholesale and retail sectors Finally, we examine Eurostat patent data covering patent applications to the European Patent

Office (EPO), in terms of both absolute numbers and growth in absolute and relative numbers There were relatively few connections between regional priorities and the growth of the number of

compare or due to lacking patent data categories and lack of easily assignable NACE codes for

This working paper has presented data from the Eye@RIS3 database, an open data tool which

priorities, we explored combinations of both main category and sub-category priority data. We found that very few regions and countries have developed similar combinations.

Finally, we compared Eye@RIS3 data with Eurostat data on numbers of local units in different

relevant data or it might simply indicate that priorities are geared towards future potential rather than existing areas of activity.

performance indicated by regional data on labour, organisations, publications and patents 21 References Aho, E.,Cornu, J.,Georghiou, L,

Open data and sharing of public sector information KETS Advanced manufacturing systems Advanced materials Industrial biotechnology

This working paper presents original data on innovation strategies for smart specialisation (RIS3) in European union (EU) regions and Member States, obtained from the Eye@RIS3 open data tool

for sharing information on the areas identified as priority areas by 198 innovation strategies. It also contextualises these

data on firms, employment and patents, with the conclusion that the connection between priorities and the economic and

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