Clustering is particularly important to gain access to new ideas and tacit knowledge especially in young industries
the Gellman (1976,1982) data base identified SMES as contributing 2. 45 times more innovations per employee than do large firms.
â'Clustering. Groups of enterprises working in the same product are seedbeds for the exchange of new ideas.
In a clustering strategy, firms take advantage of linkages with other enterprises afforded by geographic proximity,
Data constraints can be overcome to study the extent of knowledge spillovers and their link to the geography of innovative activity using proxies like patenting activity, patent citations
comparability of the data in this table is guaranteed not fully 21 Year founded 22 Not included:
and Han Zhang, 1999, â€oesmall Business in the Digital economy: Digital Company of the Future, †paper presented at the conference, Understanding the Digital economy
Data, Tools, and Research, Washington, D c.,25-26,may 1999 Berman, Eli, John Bound and Stephen Machin, 1997, †Implications of Skill-Biased Technological Change
International Evidence, †working paper 6166, National Bureau of Economic Research (NBER Cambridge, MA Bessant, J.,1999, â€oethe Rise and Fall of Supernet:
OECD, 1999, Cluster analysis and Cluster-based Policy in OECD countries, Paris: OECD Porter, M. 1990), The Comparative Advantage of Nations, New york:
Prevenzer, Martha, 1997, †The Dynamics of Industrial Clustering in Biotechnology, †Small Business Economics, 9 (3), 255-271
n Number of points of data making the SIC on Data point number pn Number of periods of SIV analysis
produced data, have different requirements and limitations than in other disciplines. For 25 example, the subject in natural sciences can be manipulated and altered, freely,
textual/statistical analysis method to existing basic information from a Swedish database Affã¤rsdata), but a case study approach was necessary to confirm the ability of the model to
existing data without pre-structuring. Although I relied on existing accounting data for the financial parameters, there were no predetermined requirements on how the data would be
displayed. Finally, although the major outcome was an empirical model, verbal descriptions and explanations (i e. narrative-textual analyses) were used in a number of papers that
addressed the issue of performance in relation to the external environment of the firm, rather than quantification and statistical analysis.
and noisy data sets (Jain and Nag 1997). Furthermore decisions based on financial failure prediction, which is driven statistically, may actually
available financial data for larger firms (Chen and Shimerda 1981. Compared to that provided by larger firms,
works on analyzed data collected by Roethlisberger and Dickson (1939. Social psychologists such as Likert (1961) and Katz et al.
The clustering I chose for the parameters in the intended model is based on the understanding that the parameters in each subset are interconnected closely.
context of justification, where data are analyzed and interpreted (Brannen 2005. Traditionally quantitative methods are concentrated more on input issues.
was to compile data into review articles and conceptual papers There are some areas of debate in respect to qualitative research.
data and to satisfy both forms of logic. In quantitative research, observation is not generally
considered a very important method of data collection for two reasons. The first is that it is
study methods with textual analyses and analyses of accounting data Qualitative methods such as case studies allow for multiple data-collection
methods under the same study, unlike quantitative research studies (Chetty 1996. They are able to produce usable theories.
One of the best methods of collecting data is in -depth interviews (Welch and Comer 1988.
Data can be analyzed using different techniques (Chetty 1996. The writer recommended using a single case study method in SME
the data was taken directly from the accounting reports of the firm and the analysis was performed while I was
intake data can be taken directly from the financial records or deducted from this information. The
the data was delivered from the firm management for the period of the analysis; and I have good
In paper 3, the validity of the data used in the analysis of the firm stems from two
and the data was taken directly from the accounting reports of the firm for the period of the
In paper 7, the data used in the analysis of the firm is valid for three reasons:
the data was delivered from the firm†s management for the period of the analysis; and the owner of the firm is a close friend of mine
and have defined its limits within a specific context determined by the data input. In the case
should utilize the existing data and complete it with more new data reflecting the additional
years of analysis incorporated. It is important to highlight that reliability should be understood in relation to the research method used†in this case, qualitative.
The technology intake data can be taken directly from the financial records or deducted from the accountancy information,
technology intake data can be taken directly from the firm†s financial records. In this particular case, the management of Autoadapt AB was very generous
necessary input data. This secured the reliability of the analysis in paper 7 There are problems related to granting reliability of measurement in the papers of
That implies the need for detailed data, which is something that SMES generally lack The desired model requires a reasonably moderate data input to counter the
issue of SMES€ accounting and reporting techniques, which provide less intensive information input than those of large firms.
basic accountancy data, without advanced statistical methods of variable elimination Due to the flexible nature of the SIV model, one could run the analysis at
which are accumulated data -points, were positive. This indicates that, on average, the change of the survival index was
sophisticated statistical methods to eliminate input data. Rather, it uses limited accountancy information in an efficient way.
basic accountancy data and does need not advanced statistical methods. The fishery firm had no innovation or development activities,
desired model should have a reasonably moderate data input to counter the issue of SMES
In that sense, graphical statistics play an important role in the interpretation of the data output
Development and Public Policy in the Emerging Digital economy, University of Trollhã¤ttan/Uddevalla, Uddevalla, Sweden, 6†8 june, 7†19
Regression for longitudinal even data. Beverly Hills, California: Sage Publications Altman, E. I. 1968. Financial ratios, discriminant analysis and the prediction of corporate
Data mining with neural networks: Solving business problems from application development to decision support. Mcgraw-hill, Inc. Hightstown, New
Accounting data and the prediction of business failure, the setting of priors and age of data.
Journal of Accounting Research 22 (1), 361†368 Houghton, K. A. and Sengupta, R. 1984.
data effects on the classification accuracy of probit, ID3 and neural networks Contemporary Accounting Research 9 (1), 306†328
and a realistically proportioned data set. Journal of Forecasting 19 (3), 219†230 Mcpherson, M. A. 1995.
Interpreting qualitative data: Methods for analysing talk, text and interaction. London, UK: SGAE Publications Ltd
with establishment data for Lower saxony, 1978†1989. Small Business Economics 4 (2 125†131 Wamsley, G. L. and Zald, M. N. 1973.
He may use retrospective data, but these bring little certainty since nobody is using them the way he suggests.
data presented. †1 Foreword: Capitalising on achievements Over the last seven years, with the goal of improving regional policies, more than 2 000 public institutions
•Project fact-sheets drafted with data based on interviews and desk research (one per project
analytical studies and EU-wide data and statistics. The overall objective of the programme is to foster a
The students are selected annually via a database of at least 350 students from all over the world (mostly Swedes.
forms across Europe to assist SMES with the transition towards a digital economy, many small and micro-companies do not have the resources to access
Clustering physical infrastructure requirements to facilitate growth and internationalisation (§3. 2. 5 Medium Cluster policies SMART+SMESGONET Clustering management activities supporting the
internationalisation and R&d cooperation (§3. 2. 5 Medium Cluster internationalisation DISTRICT+Cluster and Foreign
The INTERREG IVC website has a GP database, which is useful for an initial benchmark, but
database and personnel to provide professional advice to EU countries and regions, similar to the S3
INTERREG database and the URBACT database, he would search one global database ï¿ESPON65: The European Observation Network for Territorial Development and Cohesion aims to
and represent the demand for data to support policy development. Therefore, these projects are not about GPS,
but about data and case studies Specific knowledge available from ESPON can help managing authorities including regional
INTERREG IVC project partners could include these data when defining their work programme, identifying GPS and analysing their conditions of transferability
These three networking programme have a wealth of data relevant to regional policy improvement especially for URBACT II and ESPON;
data would be beneficial to the future INTERREG EUROPE project partners. As mentioned in section
3. 3, a capitalisation tool including an up-to-date database and personnel to provide professional advice
could include data from these networks. Another way to improve synergies would be for the programme
different innovation voucher schemes in the database 3. 4. 3 Synergies with other European Funds and Programmes
further new paths for the provision of new services, including those based on massive volumes of data
towards concrete transfer of identified good practices (already available in the ERIK database) into mainstream Structural Funds programmes in regions wishing to improve policies
The students are selected annually via a database of at least 350 students from all over the world (mostly
the transition towards a digital economy, many small and micro companies do not have the resources
The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli
The use of such data by the OECD is without prejudice to the status of the Golan heights, East
Figure 2. 7. Finish Innovations, the VTT SFINNO Database...125 Figure 2. 8. National (Lead Market) Themes and Practices in 2008-2010.126
Box 3. 1. Advantages and limitations of patent data as a proxy indicator for technological innovation...
In addition to quantitative indicators, qualitative data such as SWOT analyses, surveys workshops and interviews with regional stakeholders are also important in the priority setting and
spatial clustering of innovation activities) makes regional responses to R&d globalisation naturally appropriate. Indeed there has been a trend over the past decades to devolve competences for innovation
Designing a specialisation strategy at the regional level requires an intelligent use of data in order
of quantitative and qualitative data to situate the region, country or emerging †activities†in a larger picture
what data and tools are needed †and available †to support policy makers to assess the
Thus, data and indicators are necessary to track progress, assess structural transformations and compare strategies.
while the latter can use a variety of data types, including number of employees, number of newly established enterprises, Gross domestic product,
and export data per economic sector For countries, sufficiently detailed, internationally comparable economic data is available from OECD (www. oecd
-ilibrary. org/industry. Unfortunately, on a regional level, it is difficult to find sufficiently detailed, internationally
comparable economic data. The most appropriate data appear to be OECD€ s regional labour market statistics
By comparing specialisation indicators over time, changes in scientific, technological or economic specialisations can be analysed.
In addition to publications, patents and economic performance indicators, other data are relevant for assessing a country†s or a region†s STIE potential.
Unfortunately, it is very difficult to find regional data that are sufficiently detailed in terms of relevant
Additional limitations to data analyses arise when considering that regional internationally comparable data †especially on economic specialisation †are underdeveloped.
A number of indicators for innovation, research and development commitments, complementary investments in related industries early stage market transactions as well as for interregional and international collaboration deserve more
indicators based on the new data available to measure trade in value-added terms: the OECD ICIO model
and ORBIS firm-level data. It could be interesting to explore if regional indicators can be developed to
In addition to quantitative data, diagnostic tools can be particularly useful to identify these promising †activities††not captured by existing empirical material â€
Innovation database: In Finland, the Technical Research Centre (VTT) has made a path-breaking research on the
identified nearly 5 000 innovations and collected data on them. This database makes it possible to make versatile
studies of the renewal of the Finnish economy and innovation environment. The study represents pioneering work in
and other regions utilizing the SFINNO database Source: OECD-TIP case-studies on smart specialisation. The RIS3 KEY for self-assessment at www. era. gv. at
Data and indicators to measure specialisation in science, technology and employment may help policy-makers in diagnosing apparent strengths, weaknesses, fits and misfits in terms of scientific
strategy formation process, prospective data and analysis •Selecting and engaging key actors, necessary for their expertise and knowledge, is an
It includes performance data such as publications, critical size, collaborative projects etc The Monitoring helps to â€oefine-tune†the Strategy...
Collection of data is undertaken based on a random sample of approximately 9 000 businesses. The sample was
Data from the survey is used to monitor trends in farm innovation, evaluate impacts on agricultural productivity
Furthermore, the data allows the grain sector to develop its own benchmarks for innovation to ensure agriculture does not lag behind the national push to develop
http://adl. brs. gov. au/data warehouse/agcomd9abcc004/agcomd9abcc004201203/AC2012. V2. 1 agcommoditiesv1. 1. 0 pdf 25 All figures are in Australian dollars.
digital information appliances, automotive and advanced parts and design •Multilevel coordination and mobilisation of stakeholders:
evaluated against qualitative data gathered from interactions with industry and by gauging community and industry support for the organisation through event attendance and brand recognition.
Qualitative data, in the form of â€oereal life†success stories help demonstrate to SMES, the value of networks, like SEMIP in
Data from the Australian Bureau of Statistics demonstrates the concentration of advanced manufacturing and high tech
•Use of data and diagnostic tools: The region†s innovation strategy is based on both qualitative
and quantitative data and takes into account local and external conditions. Lower Austria has gone through extensive prioritisation processes thanks to several strategic exercises since the
•Impact of data and diagnostic tools: Lower Austria made positive learning experiences with the
Empirical data-on how to articulate regional choices in terms of the national strategy-were collected in a series of 18 regional workshops,
identified nearly 5 000 innovations and collected data on them. This database makes it possible to make
versatile studies of the renewal of the Finnish economy and innovation environment. The study at hand
the SFINNO database is significantly richer in content and wider in scope compared to other ones abroad
The SFINNO database consists of about 4 900 innovations developed by Finnish companies, dating back
and other regions utilizing the SFINNO database  OECD 2013 125 Figure 2. 7. Finish Innovations, the VTT SFINNO Database
Source: Tekes. Futher information www. vt. fi/proj/shinno Future development for smart specialisation Current status of the specialisation and prioritisation in the region
collects the data from the area of social policy; and iv) Labour market and Education Observatory of
This type of tools is mostly based on data from the past and present. The analysis can be used by
makers were provided with this data-set for the OECD exercise, it is too early to comment on how it is
While good progress has been made to collect data and develop indicators to monitor the innovation performance of regions and countries, there is still a challenge to develop appropriate evaluation
The current state of the art for baseline data profiling for policy prioritisation is developed much more than that for ongoing monitoring.
Data and indicators about smart specialisation are necessary to make those processes and their impact
metrics, indicators and regular data collections, smart specialisation strategic opportunities will not be discernible and policy makers will be unable to track progress,
covered in a specific bibliographic database. Since the coverage and the profiles of most bibliographic
databases are subject to yearly modifications, the measurement of regional or national scientific output always needs to be considered in relationship to the development of the database as a whole.
In order to obtain insights in a nation†s or region†s publication trends, and hence its science base, over time, one thus
Databases One of the most accepted and widely used data sources for the analysis of scientific specialisations is
the Science Citation Index Expanded (SCIE), which is part of the Web of Science database of Thomson
Reuters. Whereas critiques can be formulated regarding coverage and data handling by Thomson Reuters the multidisciplinarity of the database, its selectivity based on quantitative criteria, the completeness of the
INNOVATION-DRIVEN GROWTH IN REGIONS: THE ROLE OF SMART SPECIALISATION Â OECD 2013 155 address information for all authors,
the inclusion of all references and the electronic availability make it one of the most appropriate data sources for bibliometric analyses.
In addition to SCIE, the Web of Science also contains the Social science Citation Index (SSCI), the Arts and Humanities Citation index (A&hci
Another multidisciplinary bibliographic database is Scopus. Officially named Sciverse Scopus, it is owned by Elsevier and available online by subscription.
scientific specialisations of countries, regionalisation of publication data has made it possible to develop the same indicators on a regional basis. They then represent the scientific specialisations of a specific
As the SCIE data do not contain regional identifiers such as NUTS2 or NUTS3 codes, regionalisation of publication data currently requires
text mining and programming procedures. Regionalization of the Scopus data is even more cumbersome given the lower quality of the address information in this database
The successful application of the Activity Index and of RCR by scientific field strongly depends on
the underlying subject classification system, and notably on its granularity. If a multilevel hierarchical scheme is used, one can look at specialisations on an aggregated level,
and also zoom in on more detailed fields. One such hierarchical scheme is the Budapest†Leuven classification scheme (Glã¤nzel and Schubert
The data indicate that the country has a persistent relative specialisation in geosciences and space sciences (G), mathematics (H), and biology
publication data for this country can shed more light on these dynamics INNOVATION-DRIVEN GROWTH IN REGIONS:
The most widely used indicators for technological activities make use of patent data. Despite several
Box 3. 1. Advantages and limitations of patent data as a proxy indicator for technological innovation
The advantages of patent data as a proxy indicator for technological innovation •Patents cover virtually every field of technology useful for the analysis of the diffusion of key
•The statistical processing of data is largely free of errors, because patent documents are legal
•Accessibility and electronic availability of patent data has eased greatly their use The limitations of patent data as a proxy indicator for technological innovation
•Firms differ in their propensities to patent(#patents per unit of expenditure on R&d or just#of patent
Just as with publication data, one needs to be careful in interpreting low count data. Regions with very
Databases For the calculation of the RTA and RTAN-indices, different patent databases representing different
patent systems can and may be used. Besides national patent systems from individual countries, several supranational databases are available.
In Europe, a European patent system has been established in parallel to these national systems. Data on these European patents are available from the European Patent office
EPO). ) Data from the U s. patent system is available from the United states Patent and Trademark Office
USPTO). ) One important way in which patent systems differ is in their publishing and granting procedures
Twice per year, the European Patent office publishes the PATSTAT database, covering large patent systems like EPO, USPTO, PCT, JPO,
as well as about national patent systems data for about 100 countries worldwide. Access to the PATSTAT database is obtained through a license agreement with EPO
When calculating the relative specialisation of a country or region, a benchmark group of countries needs to be chosen.
The choice of this benchmark group will often be determined by the patent data source used. When using USPTO,
use EPO patent data to compare the specialisation profile of Sweden with that of all Scandinavian
noted that the regionalization of patent data, based on inventor and applicant addresses, is not available in
patent databases. However, ECOOM38 and OECD39 have invested substantial efforts to regionalize patents NUTS2 and NUTS3 level), based on inventor and applicant addresses
The data show a relatively stable specialisation profile, with relative INNOVATION-DRIVEN GROWTH IN REGIONS:
This indicator is calculated typically with export data (Balassa 1965), but other economic indicators such as employment, Gross domestic product (GDP), number of
Databases The successful application of the RCA and RCAN indices by economic sector strongly depends on
However, international databases on sectoral economic activity often aggregate many NACE or SIC codes into broad overarching sectors, such as †manufacturingâ€.
internationally comparable data is necessary on a relatively fine-grained classification level For countries, sufficiently detailed, internationally comparable economic data is available from OECD
www. oecd-ilibrary. org/industry. The OECD Statistics on Measuring Globalisation database, the OECD databases on Structural and Demographic Business Statistics and the OECD Structural Analysis Statistics
database contain many different sector-specific indicators for economic activity, including international trade, R&d expenditures, birth and death rates, High-Growth enterprises rates, turnover, value-added
production, operating surplus, employment, labour costs and investment. Benchmark data can be obtained by summing up sectoral data over all countries in these OECD database (or over a smaller group of
benchmark countries if desired. Also Eurostat publishes ample economic data on a sufficiently detailed sectoral level.
The limitation of Eurostat data compared to OECD data is that the benchmarking group pertains to the whole (or a selection) of European countries, making worldwide comparisons impossible
Unfortunately, on a regional level, it is difficult to find sufficiently detailed, internationally comparable economic data.
The most appropriate data appear to be OECD€ s regional labour market statistics (e g. number of establishments or number of employees per TL2 region),
which are available for a selection of countries and regions and are aggregated in 37 industries.
Due to limited data availability for some sectors in multiple regions and countries, only 32 industries can be used in comparative analyses.
A limitation of these data is that not all industries represented. In a case a region would like to use other
indicators for its regional economic specialisation indicator, it can collect its own data and compare this to
worldwide indicators (e g. the sum of nationally available statistics over all OECD countries. However, in
this case, special care needs to be taken regarding data collection methodology in order to obtain internationally comparable statistics.
In Flanders for example, export data are calculated without quasi -transits, while OECD data include quasi-transits,
making international benchmarking difficult An example Figure 3 below shows the RCANS for an anonymous region in 32 industries according to OECD€ s
regional labour market statistics. The data show a relative specialisation in Manufacture of Coke and Refined Petroleum Products, Manufacture of Chemicals and Chemical Products, and Manufacture of
Equipment for Radio, TV and Communication. We see that the relative employment in Air Transport and
publication and patent data can point to opportunities in technology development. In particular, the use of
hand, and term frequencies originated from text mining, on the other hand, are usually not sufficient at this level if applied alone.
patent data. These promising scientific and technological domains can and should then be discussed with economic actors in order to assess potential economic use and impact
Data on co-applications need to be interpreted with caution. The location (and hence the region or country) of the application can differ
In addition to publications, patents and economic performance indicators, other data are relevant for assessing a country†s or a region†s potential.
For example, sectoral data from the European Innovation Survey and R&d Survey can be used to construct relative specialisation indices
benchmark group. Unfortunately, it is very difficult to find reliable regional data on these topics. Most
regional innovation and R&d data does not contain sector specific information needed for the construction
For the mapping of human capital, educational data, such as the number of students enrolled in
However, this data should be rather detailed in order to provide insights in potential future specialisations or strengths.
economic development have been developed, regional internationally comparable data †especially on economic specialisations †is underdeveloped. In addition, a number of indicators for innovation and
Burnham, J. 2006), â€oescopus database: A reviewâ€, Biomedical Digital Libraries, Vol. 3, No. 1 Callaert, J.,B. Van Looy and C. Vereyen (2011), Descriptive Report:
database description, overview of indicators and first applications†Scientometrics, Vol. 33, pp. 381-422 Glã¤nzel, W. and A. Schubert,(2003), â€oea new classification scheme of science fields and subfields designed
•â€oeoften hard data give surprising results and are useful for policy-making†•â€oethere are no mechanisms to assess technological/economic SWOT on a regular basis, further
It includes performance data such as publications, critical size, collaborative projects etc. The Monitoring helps to â€oefine-tune†the
existing data or stakeholder action. Actually, the responses to the enquiry suggest that the approach followed puts much more weight on reinforcing existing strengths than on directing
and prospective data and analysis will be particularly important to mobilise The generic arguments for the necessity of good, robust and policy-oriented monitoring and
and licensing data. †41 Extract from Polish questionnaire: â€oethe Ministry of Economy does not want to prioritise sectors.
Databases An example Baseline indicators for technological specialisations Indicators Databases An example Baseline indicators for economic specialisations
Indicators Databases An example Combining baseline indicators for specialisations in science, innovation, and economic development
Additional, sophisticated indicators Mapping interactions between science and technology Detection of emerging scientific and technological fields
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