(I) SWOT Analysis R&d&i 14 Strengths Availability of broadband coverage throughout the territory (universal service).
Open Data, demand for contents, more usable technologies closer to citizens, etc. Growing possibilities for use of ICT in the public sector (energy saving, education, health, social care.
and services to ensure digital connectivity. 2. To develop the digital economy for the growth and competitiveness of enterprises. 3. To improve the effectiveness,
sensors, embedded systems, data mining, etc. Robotics Intelligent infrastructures (roads, logistics: sensors, monitoring, etc. Bio--fuels: sunalower, bio--forest waste, etc.
crowdsourcing, utilising big data; but much of this is still in experimental mode13 and European cities tend to lag those in the US14.
and gather information, through open data gathering techniques, crowdsourcing and so on. The potential importance of digital dialogue, including for Smart Specialisation for Regional Innovation:
and you can include excerpts from OECD publications, databases and multimedia products in your own documents, presentations, blogs,
and Antonella Noya (Chapter 5). Further written inputs were provided by Stefano Menghinello, National Statistical Institute, Italy (the spatial clustering analysis and annexes in Chapter 3) and Andrea
This book presents the relevant data and policy information from 40 economies around the world,
In addition to presenting the data, the report also explores the policy imperatives in three major yet insufficiently recognised action areas that are new to much of the policy world.
providing up-to-date data and highlighting current policy issues of greatest concern. It draws in particular on the expertise and analysis of the OECD's Working Party on SMES and Entrepreneurship and the Directing Committee of the Local Economic and Employment Development Programme.
Notes on the country data...128 Chapter 3. Knowledge Flows...131 Introduction...132 How knowledge affects entrepreneurship...
134 The geographical clustering of knowledge-intensive activities...136 The role of local knowledge flows for spatial agglomerations and local innovation systems...
The ORBIS Database...158 Annex 3. A2. The LISA Methodology...161 Chapter 4. Entrepreneurship Skills...
Presents a set of country-level data on SMES, entrepreneurship and innovation performance, and a review of major policies and new policy developments in the field.
The data also show substantial EXECUTIVE SUMMARY SMES, ENTREPRENEURSHIP AND INNOVATION OECD 2010 17 shares of total activity accounted for by each of the sub-categories of micro,
The data suggest that SMES innovate less than large firms across a range of categories including product innovation, process innovation, non-technological innovation, new-tomarket product innovations and collaboration in innovation activities.
There is strong spatial clustering in knowledge-driven sectors, i e. those where R&d intensity, basic university research and highly-skilled workers are most important.
and data is not commonly available for non-technological innovation as a proportion of firm employment or turnover.
Chapter 2 provides data on SME innovation performance and constraints across 40 economies and examines the major and new policies that have been introduced.
Definitions Supporting Frameworks for Data Collection, OECD Statistics Working papers, 2008/1, OECD Publishing, Paris, doi: 10.1787/243164686763.1.
New Evidence from Micro Data, Ch. 1, pp. 15-82, University of chicago Press, Chicago. Baumol, W. 2002), The Free-Market Innovation Machine:
Each Country Note is accompanied by structural data on the SME sector and selected indicators showing SME innovation performance, perceived barriers to innovative activities
When available, data are presented also for accession countries (Chile, Estonia, Israel, Russia and Slovenia) and enhanced engagement countries (Brazil, China, India, Indonesia and South africa.
Box 2. 1. Basic methodological references Data presented in the chapter come from three main sources:
OECD Structural and Demographic Business Statistics Database; Innovation Surveys (e g. the Community Innovation Surveys; national innovation surveys;
Structural indicators of the enterprise population Data are drawn from the OECD dataset Business Statistics by Size Class,
which is part of the OECD Structural and Demographic Business Statistics Database. The dataset comprises five dimensions:
The PMR database comprises three broad sets of indicators on: state control, barriers to entrepreneurship,
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/812706506652 B. Innovation performance of SMES and large firms, 2007-081
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/812710434422 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/812733856326 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/812772716231 B. Innovation performance of SMES and large firms, 2002-041
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/812823002327 B. Innovation performance of SMES and large firms, 2004-061
and customer relationship management system (CRM) have been established for all five existing centres. Basic funding for the Regional Centres of Growth amounts to DKK 92.8 million a year (including administrative costs.
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/812824001404 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/812888716138 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813015740451 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813110872302 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813116208667 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813126285000 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813138862470 B. Innovation performance of SMES and large firms, 2002-041
1. Data only reflect enterprises with 3 or more persons engaged. 2. As%of all firms within size class. 3. 2002-04.4.
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813224252704 B. Innovation performance of SMES and large firms, 2004-062
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813326326305 B. Innovation performance of SMES and large firms, 2004-061
1. For manufacturing, data only reflect enterprises with 4 or more persons engaged. 2. As%of SMES with new product sales. 3. Index scale of 0-6 from least to most restrictive.
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813327663628 B. Innovation performance of SMES and large firms, 2002-04
1. For manufacturing, data only reflect enterprises with 5 or more persons engaged. 2. As%of all firms within size class. 3. As%of total turnover. 4. Index scale of 0-6 from least to most restrictive.
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813331118285 B. Innovation performance of SMES and large firms, 2002-042
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813367102180 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813468327202 B. Source of finance of SMES and large firms, 2002-04
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813475131677 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813487217851 B. Innovation performance of SMES and large firms, 2005-071
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813517714027 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813521857686 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813540502857 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813544660727 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813612045268 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813624112502 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813727458672 B. Type of innovation by SMES, 20071 C. SMES'reasons
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813835033115 B. Innovation performance of SMES and large firms, 2004-061
OECD, Product Market Regulation Database. statlink2 http://dx. doi. org/10.1787/813847876385 B. Innovation performance of SMES and large firms, 20071 C
combined, they provided legal recognition to transactions carried out by electronic data interchange. Several other actions, including the establishment of Cyber Laws, the setting up of the Cyber Regulations Appellate Tribunal,
which include data relating to the Golan heights, East Jerusalem and Israeli settlements in the West bank. 1. Czech republic, Hungary, Korea, Mexico, Poland, Slovak Republic, Turkey. 2. Austria, Belgium, Finland
OECD, PMR Database; OECD Economic Surveys: Israel 2009. statlink2 http://dx. doi. org/10.1787/813271727207 A. Barriers to entrepreneurship, 2008 Index scale of 0-6 from least to most restrictive B. Administrative
AN OVERVIEW BY COUNTRY SMES, E 128 NTREPRENEURSHIP AND INNOVATION OECD 2010 ANNEX 2. A1 Notes on the Country Data The structural data on businesses presented in the chapter
Most data presented refer to the nonfinancial business economy, i e. ISIC Rev. 3/NACE Sections C to I and K and is subdivided into Industry (Sections C, D,
The following text gives details on the completeness of the data for each country. Australia:
The geographical clustering of knowledge-intensive activities Activities can cluster for different reasons, such as availability of intermediate suppliers and skilled labour
These findings underline the importance of knowledge-driven clustering in knowledge-intensive industries. They are reflected also in the results of a recent OECD study of seven internationally-reputed clusters including Grenoble in France and Medicon Valley in Scandinavia.
Given the limits of official data sources for local-level analysis, we turn to firm-level information from the commercial ORBIS database on the location, nature and performance of local innovation clusters.
The ORBIS database provides insights about the spatial pattern of business demography and performance and is based on a highly disaggregated territorial breakdown that is not easily available from national statistical offices.
The use of ORBIS location information (based on company zip code or municipality of residence) and business demographic information (based on the company year of incorporation and persistence in time as an active company) has enabled development
Selected clusters are compared then using data on business demography and business performance. These findings represent some of the first outputs of an OECD project to map clusters
structural and selection biases in the original data source. 3 3. KNOWLEDGE FLOWS SMES, ENTREPRENEURSHIP AND INNOVATION OECD 2010 137 European union The first two maps show the agglomeration of HTM
and United kingdom. Given problems of comparability between the NUTS classification and the location information included in the original data source, countries such as Denmark, Luxembourg, The netherlands,
OECD elaboration based on ORBIS database available from Bureau Van dijk. Low LQ Medium-Low LQ 3. KNOWLEDGE FLOWS SMES, E 138 NTREPRENEURSHIP
which might also infer a bias in the original data sources. Secondly, KISA firms often tend to Figure 3. 2. Distribution of KISA firms in the European union (Quantiles based on LQS) Source:
OECD elaboration based on ORBIS database available from Bureau Van dijk. Low LQ Medium-Low LQ Medium-High LQ High LQ 3. KNOWLEDGE
Figure 3. 5 points out the location of KISA firms in the United states using the same data source,
OECD elaboration based on ORBIS database available from Bureau Van dijk (Geoda software. 1st range (0) 2nd range (891) 3rd range (516) 4th range (493) 5th range (488) Figure 3. 4. Agglomerations of HTM firms in the United states LISA methodology Source:
OECD elaboration based on ORBIS database available from Bureau Van dijk (Geoda software. High-High low-Low Low-High High-low 3. KNOWLEDGE FLOWS SMES, ENTREPRENEURSHIP AND INNOVATION OECD 2010 141 Figure 3. 6, based on the same methodology
OECD elaboration based on ORBIS database available from Bureau Van dijk (Geoda software. 1st range (0) 2nd range (790) 3rd range (400) 4th range (401) 5th range (399) 6th range (398) Figure 3. 6. Agglomerations of KISA firms in the United states
OECD elaboration based on ORBIS database available from Bureau Van dijk (Geoda software. High-High low-Low Low-High High-low 3. KNOWLEDGE FLOWS SMES, E 142 NTREPRENEURSHIP AND INNOVATION OECD 2010 Cluster rankings:
A preliminary exploration The combination of different indicators of business demography and business performance at the local level calculated experimentally from the ORBIS database can lead to an international analysis of the strength of clusters based on a composite indicator.
In the case of the US clusters, given data source constraints for this country, the composite indicator is limited to the first three variables in the list.
OECD elaboration based on ORBIS database available from Bureau Van dijk. statlink2 http://dx. doi. org/10.1787/814040554856 Name of business cluster Country New
OECD elaboration based on ORBIS database available from Bureau Van dijk. Ranking Name of business cluster Country of residence 1 Boston (Route 128) United states 2
OECD elaboration based on ORBIS database available from Bureau Van dijk. statlink 2 http://dx. doi. org/10.1787/814047837382 Name of business cluster Country
OECD elaboration based on ORBIS database available from Bureau Van dijk. Ranking Name of business cluster Country of residence 1 Silicon valley United states 2 Austin ITC
E 144 NTREPRENEURSHIP AND INNOVATION OECD 2010 The role of local knowledge flows for spatial agglomerations and local innovation systems The above section illustrated the phenomenon of spatial clustering of economic activity
briefly summarised here, suggests that local knowledge transfers are important to this clustering process. This literature stresses the fact that knowledge does not spill over long distance
%A relationship therefore exists between knowledge spillovers, spatial clustering and innovative output (Giuliani, 2005. This is especially true for knowledge-driven sectors,
Despite the increasing importance of university-industry knowledge transfers and of public schemes that try to bolster such transfers, it is difficult to produce reliable and comparable data on this phenomenon.
In addition, data collection is regular in some countries but sporadic in others. 3. KNOWLEDGE FLOWS SMES,
Data show that KTOS have a much longer tradition in the United states than in Europe.
Finally, data on university spin-offs in the two different contexts diverge much more slightly, with nearly 2 spin-offs per KTO a year in Europe and nearly 3 in the United states. University-industry knowledge transfers are also of increasing importance in Asia;
Data, however, show that knowledge transfer is still incipient in China. Universities have a great number of patents (126 per KTO),
research and development (73). 3. An overview on the ORBIS database is given in Annex 3. A1. 4. Patent protection can be sought abroad
Evidence from European Patent Data, European Economic Review, Vol. 47, pp. 687-710. Cantner U.,M. Goethner and A. Meder (2007), Prior Knowledge and Entrepreneurial Innovative Success, Jena Economic Research Paper, No. 2007-052.
KNOWLEDGE FLOWS SMES, E 158 NTREPRENEURSHIP AND INNOVATION OECD 2010 ANNEX 3. A1 The ORBIS Database The scope of ORBIS for territorial analysis The ORBIS database, developed
is a source of business micro data. The database includes around 40 million companies, has a geographical coverage of up to 200 countries,
and can consider all sectors of economic activity. There are no exclusion thresholds in terms of enterprise size, unless national limitations reduce the coverage of administrative data sources.
Given national data source constraints there is plenty of information at the firm level about sector, legal status, ownership and an array of financial and economic variables.
The target population consists of firms with a corporate legal status, which means that micro firms with less than ten employees may be excluded largely from this database.
The value of the ORBIS database for territorial analysis rests on the possibility to rearrange firm-level data according to detailed company location.
The information on company location relates to the complete address, which includes street, city and postal code.
A wide range of entrepreneurship, economic performance and financial indicators can be calculated at the local level from the ORBIS database:
Business demographic indicators, e g. business birth rate; survival rate; distribution of firms by age, etc. Economic performance indicators, e g. labour productivity;
While economic performance and profitability indicators can be calculated from the ORBIS database at different levels of industry
Limited information available in the ORBIS database on complex business demography events, such as mergers and acquisitions, makes the definition of a company profile over time incomplete, with spurious effects on the calculation
Moreover, ORBIS naturally tends to overestimate real entry rates compared to real exit rates, as it is a continuously expanding database in terms of both international and national coverage.
information regarding the company's incorporation year is essential to disentangling real entry from increasing coverage effects.
In synthesis, the two different types of demographic information included in the ORBIS database i e. the date of company incorporation and the entry of a new company in the open panel dataset tend respectively to anticipate
Potential biases of territorial data calculated from commercial databases The key territorial information included in a commercial database with firm-level data is the company's complete postal address.
Two different sources of location bias can be associated with the use of this information to produce territorial data.
As already outlined in the previous section, the attribution of all company activities to a single location leads to incoherent territorial data in the case of multi-plant companies.
other characteristics of commercial databases can indirectly alter the consistency of territorial data calculated from this sort of source.
Coverage restrictions concern the under-coverage of the set of companies extracted from the database with respect to the relevant target population,
Under-coverage is induced generally by threshold effects in which firms under a certain size are included not in the original data sets
or by additional restrictions on revealing information on small areas imposed by the database provider.
A structural bias is a systematic deviation from the target population in the sample distribution of key economic variables (number of firms
Structural biases may be induced by a number of factors such as restrictions in the database and poor data quality consistency across industries, regions,
A selection bias can be generated by the presence of selectivity criteria in the database, for instance the exclusion of all companies with some specific legal status.
E 160 NTREPRENEURSHIP AND INNOVATION OECD 2010 The extent to which statistical biases affect indicators Territorial data in the form of absolute values are affected by coverage restrictions, structural bias and selection bias.
This is because simple aggregations of micro data by relevant territorial units completely mirror the characteristics of input data.
may mitigate the negative impact of coverage restrictions and structural bias. If coverage restrictions or a structural bias homogeneously affect the spatial distribution of the sample e g.
SMES are underrepresented in the same direction and with the same magnitude in all territorial areas the standard location quotient (LQ) tends to neutralise these sources of bias in the input data Dynamic territorial indicators, such as employment or labour productivity
growth rates in a given period, present particular characteristics in terms of potential sources of bias as compared to static territorial indicators.
While the consistency of static territorial indicators is altered by an uneven spatial distribution of the sample of micro data as compared to the target population,
since commercial databases are upgraded sometimes in terms of coverage and data quality. This database upgrading may induce a structural break that can alter the spatial distribution of companies by increasing
or decreasing the magnitude of a static territorial bias. Spatial) econometric modelling may also represent a possible way to deal with different types of territorial bias in the data.
In particular, if model regressors absorb some sources of bias, this econometric approach may provide interesting and unbiased results.
This discussion balances the unquestionable informative relevance of commercial databases to carry out nonstandard territorial analysis with an insight into the methodological problems that may affect the consistency of these territorial data.
In the case of uneven spatial clustering global spatial indicators, such as Moran'S i, are found to be less useful and local indicators of spatial association (LISA) have been developed (Anselin, 1995.
Moran'S i and LISA differ in data employed and analytical scope. Moran'S i is a global measure of spatial autocorrelation.
and the overall pattern in the data is summarised in a single statistic. In contrast, LISA calculates a local version of Moran'S i for each areal unit in the data.
In particular, LISA shows statistically significant groupings of neighbouring areas with high and low values around each region in the study area.
However, the scope of these indicators should be limited to exploratory data analysis. In the absence of a well-defined spatial modelling framework,
data confirm the existence of a skills and training problem holding back innovation. Across OECD countries, employees of SMES participate in formal training activities to only half the extent that staff in large firms do (OECD, 2010b, forthcoming.
Across the EU-15 countries, data from the Eurostat Continuing vocational training Survey show that employees in enterprises with less than 50 employees receive significantly less in-company training than employees in larger firms.
The Social Entrepreneurship Monitor is a special report of the Global Entrepreneurship Monitor (GEM) UK project to estimate the percentage of social entrepreneurs in UK society using population survey data.
recent UK data released by the Third Sector in July 2009 (www. cabinetoffice. gov. uk/media/231495/factoids. pdf) refer to an estimated average (2005-07) of 61
8 in July 2009) out of 515 organisations that applied (data elaborated for OECD by the Korea Labor Institute and the Research Institute of Social Enterprise.
In the United states, the Johns Hopkins Nonprofit Economic Data Project (NED) is generating information on the dynamics of the nonprofit sector by analysing diverse datasets on nonprofit organisations,
including data on nonprofit finances, employment and wages, and volunteering. The website of the project (www. ccss. jhu. edu/index. php?
In addition, country notes present statistics and policy data on SMES, entrepreneurship and innovation for 40 economies, including OECD countries, Brazil, China, Estonia, Indonesia, Israel, the Russian Federation
www. sourceoecd. org/9789264080317 Sourceoecd is the OECD online library of books, periodicals and statistical databases.
Open Data movements and innovative/transparent forms of governance go hand in hand (http://data. gov. uk) with these new forms of coproduction.
The Open Data movement lobbies government institutions, international organizations and the private sector to make private and public databases available to application developers.
Data is an important resource and output of these social media innovations. Opening up government data silos to developers and communities is
therefore potentially one way to support this growing social-digital economy. Yet to be of any use,
the new superfluity of data needs to be structured, analyzed and interpreted (Wilson et al. 2013). This is an increasingly pressing challenge,
deeply imbued by often overlooked issues of provenance and trust Cornford et al. 2013). One of the defining features of social innovation is that it provides insights
Data Is the Solution! What was the Question Again? Public Money & Management 33 (3): 163 166. doi:
collecting and correlating social network data (e g. degree, density, etc.)for innovation success Providing a framework for timely communication and distribution of experiences, contextual information,
and displaying data and is used as the front-end tool for the application in conjunction with Oracle 9i database as the backend.
Oracle 9i Reports Builder (DS Release 9. 0. 2. 0. 3) was considered in the implementation for generating reports.
Through data-driven tables of contents, Oracle Forms provides users with an easy interface to the information that is required.
The Entity Relationship diagram (ERD) in Figure 6 represents the data modeling of I3. The figure represents the organization of data into entities and the relationships between the entities in I3.
Although the actual system includes both user modules and administrator modules only the administrator modules that focus on the management of a social network based digital city is discussed in this study.
as the administrator is responsible for updating the database to ensure that the data in the system is updated and true at all times,
and manage all meta-data and other pre-conditional data required for the system. These include metadata pertaining to user, company, resource or request management like Education level, Occupation list, Ethnic group, Expertise areas, NAICS code, Nonprofit type, Occupation type
, Session type, Milestone, Counties, zipcodes, regions, contact method etc. The subsequent sections will discuss how the ERD for the various management tasks has been designed
) User registration and profiles are created through an initial registration process and data (e g.,, occupation, education information, college teaching, highest education level, privacy level, etc.
The survey statement entity manages the who knows who'data collated from a survey deployed during the registration process
which could be patent, database, software, copyright materials or literature. The equipment entity manages equipment information like the type of equipment resource,
Similar data is collected and managed for all resource types. Each resource is tagged to its owner (a registered user)
all data entry fields are enabled with smart tips to provide information (e g.,, type, length, restrictions,
or meanings of data) or helpful tips when a user moves mouse over a field,
Smart tips are enabled for all data fields Figure 12: Resource Management Smart tips enables. Figure 13:
In I3 several graphical representations are provided with many reports to give the administrator a different direction for analyzing data.
data mining techniques can be employed as part of future enhancements to provide credibility and integration of information as suggested by Lo and Hsieh (2003).
but analyzing the data and building a knowledge base would help build a stronger digital community.
Who Knows Who Data Presents the user's social connectedness in the network and can help identify important nodes in the network Social network Analysis ACKNOWLEDGMENTS This project is funded by National Science Foundation Award#0332378, Partnerships for Innovation Program, Dr. John hurt Program Director
Characteristics and Processes, The DATA BASE for Advances in Information systems, Winter, 35,1, 65-79. Bucolo, S.,Ginn, S.,Gilbert, D,
and Business Application of Software Intelligent agents. Dr Lea has published in numerous journals including International Journal of Production Research, International Journal of Production Economics, Industrial Management and Data systems, Technovation,
His research interests are in the fields of data/text mining, business process simulation, software agent applications,
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