As financial data is rarely available for SMES, we relied on the number of employees when selecting our interview partners
collected data broadly categorized according to our three research questions. Within the first round of content analysis, meaningful sections of the
the data searching for significant quotations, which are presented below. Another goal of the third step
the data as well as the most important findings Next, the results are presented with meaningful statements from the interviews
has been examined in a few studies based on large quantitative databases 5 These pioneering articles have explored why SMES engage in open innovation activities,
pressure differences into a convenient tool for recording weather data, the metal cells were brought into contact with a liquid that reacts to these small differences accurately
4. DATA AND METHODS 4. 1 Survey description To analyze trends, motives and management challenges related to open innovation
The population of firms was derived from a database of the Chambers of Commerce, containing data on all Dutch firms.
The data were collected in December 2005, over a period of three weeks, by means of computer assisted telephone
interviewing (CATI. All respondents were small business owners or managers and innovation decision-makers. Attempts to contact reference persons were made five
The survey data contained a summary variable indicating customer involvement, i e. a dummy coded 1
The survey data allowed distinguishing between employees that belong to the R&d department and those that are coming from other
To explore patterns of open innovation among SMES we relied on cluster analysis techniques. These are sensitive to the selection of the variables used, since the
cluster analysis techniques to explore patterns of open innovation practices among SMES. Finally, we used oneway analysis of variance to validate the taxonomy
in the clustering. In general, PCA reduces the risk that single indicators dominate a cluster solution,
weighted more heavily in the clustering and thus dominate the cluster solution (Hair et al.,
We first tested if our data were suitable for a component analysis by calculating Measures of Sampling Adequacy (MSA) for the individual variables
In the cluster analysis we combined hierarchical and nonhierarchical techniques. This helps to obtain more stable and robust taxonomies (Milligan and
-hierarchical†cluster analysis, in which SMES were divided iteratively into clusters based on their distance to some initial starting points of dimension k. While some k
the data did not contain enough records to provide reliable insights about respondents†motives and challenges on this topic
The results of the cluster analysis furthermore show that there are different open innovation strategies and practices
Cluster analysis. Oxford university Press, London Fontana. R.,Geuna, A.,Matt M.,2006. Factors affecting university†industry R&d
Multivariate Data Analysis. 5th ed. Prentice hall, Englewood Cliffs, NJ Henkel, J.,2004. Open source software from commercial firms †Tools, complements
clustering methods Applied Psychological Measurement 11,329-354 Milligan, G. W.,Sokol, L. M.,1980. A two-stage clustering algorithm with robust
Cluster analysis in marketing research: review and suggestions for application, Journal of Marketing Research, 20: 134-148
Evidence from Global Entrepreneurship Monitor Data H200809 25-7-2008 The Entrepreneurial Adjustment Process in Disequilibrium
Drawing on a database collected from 605 innovative SMES in The netherlands, we explore the incidence of and apparent trend towards open innovation.
As we draw on a survey database of 605 SMES in The netherlands, the paper also accounts
describes our data, while Section 5 analyses the incidence and trend towards open innovation, and motives and
data set contains information on perceived barriers to adopt open innovation practices. The open innovation
use a survey database that was collected by EIM, a Dutch institute for business and policy research.
Data collection was done over a 3-week period in December 2005. To reliably identify trends only respondents with long tenure and representing
guidelines for the collection of innovation data, see OECD 2005). ) Secondly, the survey asked if respondents†enter
Dutch Chambers of Commerce database. Interviewers explicitly asked for those who were responsible for innovation, i e. small business owners, general managers
the data did not contain enough records to provide reliable insights about respondents†e class
5. 3. Cluster analysis To explore the incidence of open innovation in more detail, we decided to cluster the respondents in groups of
dimensions in our data and applied cluster analytic techniques to ï nd homogeneous groups of enterprises
ï rst exploratory run demonstrated that our data were suitable for PCA (i e. MSA values all 40.57, KMO
In the cluster analysis we combined hierarchical and nonhierarchical techniques. This helps to obtain more stable and robust taxonomies (Milligan and Sokol, 1980
survey database of 605 innovative SMES in the Nether -lands, we conclude that SMES are practicing extensively
Drawing on an existing database, open innovation was operationalized along two dimensions, i e. technology exploitation (reï ecting innovation practices to organize
exploitation, our data suggests that many SMES attempt to beneï t from the initiatives and knowledge of their
Cluster analysis revealed three groups of SMES, clustering ï rms into groups with similar open innovation practices
relatively more medium-sized companies, the clustering implicitly suggests a sequence in the adoption of open
that our survey data capture the full domain of external technology exploitation and exploration Although our sample of SMES is extensive,
Cluster analysis revealed three homogeneous groups of SMES with similar application of open innovation practices. The clusters implicitly suggest a
Innovation Data, third ed. OECD, Paris Prencipe, A.,2000. Breadth and depth of technological capabilities in
Cluster analysis in marketing research review and suggestions for application. Journal of Marketing Research 20, 134†148
Cluster analysis Motives and challenges Discussion Conclusions Limitations Suggestions for further research Principal component analysis References
Technovation 29 (2009) 423 m P. J R e L y Re s, H Tech
Drawing on a database collected from 605 innovative SMES in The netherlands, we explore the incidence of and apparent trend towards open innovation.
As we draw on a survey database of 605 SMES in The netherlands, the paper also accounts
describes our data, while Section 5 analyses the incidence and trend towards open innovation, and motives and
data set contains information on perceived barriers to adopt open innovation practices. The open innovation
use a survey database that was collected by EIM, a Dutch institute for business and policy research.
Data collection was done over a 3-week period in December 2005. To reliably identify trends only respondents with long tenure and representing
guidelines for the collection of innovation data, see OECD 2005). ) Secondly, the survey asked if respondents†enter
Dutch Chambers of Commerce database. Interviewers explicitly asked for those who were responsible for innovation, i e. small business owners, general managers
the data did not contain enough records to provide reliable insights about respondents†e class
5. 3. Cluster analysis To explore the incidence of open innovation in more detail, we decided to cluster the respondents in groups of
dimensions in our data and applied cluster analytic techniques to ï nd homogeneous groups of enterprises
ï rst exploratory run demonstrated that our data were suitable for PCA (i e. MSA values all 40.57, KMO
In the cluster analysis we combined hierarchical and nonhierarchical techniques. This helps to obtain more stable and robust taxonomies (Milligan and Sokol, 1980
survey database of 605 innovative SMES in the Nether -lands, we conclude that SMES are practicing extensively
Drawing on an existing database, open innovation was operationalized along two dimensions, i e. technology exploitation (reï ecting innovation practices to organize
exploitation, our data suggests that many SMES attempt to beneï t from the initiatives and knowledge of their
Cluster analysis revealed three groups of SMES, clustering ï rms into groups with similar open innovation practices
relatively more medium-sized companies, the clustering implicitly suggests a sequence in the adoption of open
that our survey data capture the full domain of external technology exploitation and exploration Although our sample of SMES is extensive,
Cluster analysis revealed three homogeneous groups of SMES with similar application of open innovation practices. The clusters implicitly suggest a
Innovation Data, third ed. OECD, Paris Prencipe, A.,2000. Breadth and depth of technological capabilities in
Cluster analysis in marketing research review and suggestions for application. Journal of Marketing Research 20, 134†148
Cluster analysis Motives and challenges Discussion Conclusions Limitations Suggestions for further research Principal component analysis References
WARN-Count in xref table is 0 at offset 11669896 Knowledge Partner Research Report OPEN INNOVATION IN SMES
has been examined in a few studies based on large quantitative databases 5 These pioneering articles have explored why SMES engage in open innovation activities,
pressure differences into a convenient tool for recording weather data, the metal cells were brought into contact with a liquid that reacts to these small differences accurately
-verging data on some innovation inputs (R&d ex -penditure of firms), intermediate outputs (patents and final outputs (international trade), although on
R&d investment, with data related to 2004 (European Commission, 2005) and to 2009 (European Commis -sion, 2010.
Second, data on patents may be criticized as less relevant for some subsectors of IT, such as software
data from the European Patent office, stated that †the US is ahead of the EU in four out of six
Looking at patent data, it appears that in the patent class computer and automated business equipment
to 2005 on data from the Patent Cooperation Treaty PCT), and using the larger definition of information
-sequent analyses, based on sector-level data, showed that a large part of the gap is due to large gains in
of industry-level productivity data in the KLEMS project, supported by the European commission O†Mahony and Timmer, 2009;
data structure (Knuth and Tarjan 10. Artificial intelligence Source: our elaboration from expert opinion European competitiveness: IT and long-term scientific performance
procedures, or data) at many levels, preserving its fundamental properties. This makes it possible to
govern how data flows through the Internet what happens when packets get lost, and so on NRC, 2004: 18;
The abstract nature of computer objects (e g. data procedures) allowed a process of progressive trans
-tion of regions of reality (not only data but images sound, movement, all sorts of physical parameters
Several items of data are still missing, so the analysis must be done on different samples, variable by variable.
-ment of data, with limited comment Patterns of educational mobility We identified the location of the universities at
The data do not al -low a full-scale analysis, because we do not have control samples of scientists in related fields.
Our data seem to suggest that com -puter science has been a gateway for cross-discipline
We find the data illuminating. It is not surprising that top universities try to attract top sci
An easy way to comment these data is to remem -ber that these are star scientists,
Second, we are observing average data Standard deviation informs us that even faster careers are observable.
-ble to normalize these data by age or seniority, given several missing items of data.
A crude approxima -tion is offered in Table 9, suggesting that on average they may change country for each 30 years of age
Our data seem to suggest that in the com -puter sciences the pattern of geographic mobility has
external control on the data self-declared in the CVS would require a long and dedicated investigation
our data, top scientists move from the university that awarded their Bachelor degree to the USA, fight to
Company Data. Brussels: Directorate-General Joint Research Centre European commission 2007. Towards a European Research Ar
the EU KLEMS database Economic Journal, 119 (June), F374†F403 O†Mahony, M and M Vecchi 2005.
Diversification and clustering of SMES for future growth...74 5. 5 Israel: Envisaged targeted support for high-growth sectors and SMES...
Consistent statistical data is missing Comparable international data about high-growth SMES are missing, so that a consistent picture of
their prevalence cannot yet be drawn. The OECD -Eurostat Entrepreneurship Indicators Programme found that in 2006,
European countries for which data were available A Eurobarometer study found that in several Euro -pean countries the share of high-growth firms in
clustering of SME business activities. Diversifica -tion policy initiatives focus on supporting SMES to move (1) up the technology ladder,(2) between
data, theoretical ideas and empirical results. Chapter 4 analyses current policy developments, focusing on European and national policy approaches as well as specific issues related to entrepreneurship, access to
Matrix of main data sources for INNO-Grips Policy Brief 2 Quantitative focus Qualitative focus
Primary data collection ï Representative enterprise survey (CATI ï INNO-Grips case studies and case briefs
ï OECD and Eurostat databases ï Data from industry associations ï Existing case studies from various
sources ï Literature evaluation (desk research 12 Research and development do not necessarily have to take place, considering for example user entrepreneurs who
Data from various secondary sources is used here not only for exhibiting numbers of high-growth enterprises but also for other indicators such as venture capital provision
Primary data collection The description of examples of successful support of high-growth innovative companies is a key element of
issues of human capital, access to specialised technology and business consulting, R&d clustering technology scouting to identify R&d projects with commercial potential, technology transfer, and an
However, the study concludes that â€oefrom the nature of the data collected and the limited number of examples
unsatisfactory statistical data From a scientific point of view data availability is always unsatisfactory, but measurement of entrepreneurial
activity, including high-growth SMES, apparently remains particularly difficult. Internationally comparable data are scarce. The most notable initiative to make international data on entrepreneurship available may be the
joint OECD-Eurostat Entrepreneurship Indicators Programme (EIP) launched in 2006. Some key findings from the EIP are presented in the following,
supplemented by data from other sources OECD The EIP provides data about high-growth enterprises
which may be taken as a proxy for data about high -growth innovative SMES. Data are available for 15 countries,
divided by manufacturing and services. 19 The most recent data available at the time of authoring this Policy Brief were for 2006.
For this year, Bulgaria was on top for both manufacturing (8. 6%high-growth enterprises) and services (8. 2%)†see Exhibit 4. The fol
-lowing countries were Italy (8%/7. 9%),Estonia (7. 1%/5. 6%),Brazil (6. 9%/5%)and the USA (5. 9
%/19 See OECD (2009), pp. 28-31 Policies for high-growth innovative SMES v1. 6
high-growth enterprises, the USA were nevertheless ahead of most other European countries for which data
Among the countries for which data are performed available, Bulgaria best (2. 3%gazelles in manufacturing, 1. 9%in services.
A Eurobarometer survey in 2009 of more than 9, 000 companies provided data for all EU-27 countries. 20 As
the denominator and the data source is different from the OECD data, both datasets cannot be compared.
the related data 21 See Veugelers (2009), p. 2. The largest US companies were taken from the Financial times Global 500 of 2007, the
Firm-level data was provided by the Zentrum fã r Europã¤ische Wirtschaftsforschung (ZEW), Mannheim, Germany
and contraction in Europe and the US, drawing from a purpose-built database of business growth in the period from 2002-2005 with individual records for six million businesses.
A Kauffmann Institute study of the US economy in 2010 with data for 2007 contained 5. 5 million firms.
longitudinal data sets found that â€oethe profitable low growth firms are both more likely to reach the desirable
and 26 by GIF2 at an average cost of 600,000 euro. 61 No valid data for
It also tracks baseline data for its per -formance, such as employees, revenue growth and number of customers.
of growth finance can hardly be based on solid data. â€oeaccess to finance†for entrepreneurs and young busi
-nesses, both debt and equity capital, is one area where there is scarce availability of comparable data
often reliable data are not even available at the country level. 96 92 Definition of the European Venture capital Association, see http://www. evca. eu/toolbox/glossary. aspx?
The Eurobarometer survey quoted in the following provides insightful data and it is based on almost 10,000 interviews,
-cal data for EU companies, the report shows that â€oeinnovative companies are more likely to exportâ€, that â€oethey
The relationship between internationalisation and clustering may be of particular interest, since local clusters are seen often as breeding grounds for innovation.
-pean and national level. 126 One could assume that clustering and internationalisation mutually reinforce each
clusters and the relationship between clustering and internationalisation cannot be dealt with in depth in this Policy Brief, this finding strongly points to the importance of internationalisation for innovation
-tween clustering and internationalisation; see http://www. proinno-europe. eu/tactics 128 Dahl Fitjar/Rodrã guez-Pose (2011), p. 5
and clustering of SME business activities. Diversification policy initiatives focus, among other items, on sup -porting SMES to move between industries (new business activities in related industries) â€
Micro United Network Pte Ltd (http://www. microunited. com. sg) provides voice, video and data
which data are available †when a combination of venture capital 150 See Cooper (2009 Policies for high-growth innovative SMES v1. 6
funds but there is no data available to measure the investment performance of this group of funds
though they had the data, the review did not assess the presence of high growth firms or gazelle aspects. 160
Awards-U s. Small Business Administration Tech-Net Database; Responses-NRC Phase II Survey and NIH Phase II Survey and updates. http://www. ncbi. nlm. nih. gov/bookshelf/br. fcgi?
Diversification and clustering of SMES for future growth Summary Although the fall out from the 2008 Lehman brothers collapse continues to skew the Japa
diversification and clustering of SME business activities. Diversification policy initiatives focus on supporting SMES to move (1) up the technology ladder (product development),
Clustering policy initiatives focus on promoting (1) local clusters, such as regional linkages among small and medium manufacturers, and (2) network
and clustering policy initiatives, the government†s 2009 New Growth Strategy (Basic Policies Toward a Radiant Japan identifies SMES as an engine for future high economic growth
According to the OCS data most of the grants are provided to high growth SMES, though the OCS makes great efforts to increase the
Other items with outstandingly high percentages may confirm this interpretation of the data. 83%of the high
Improving the data base for company finance A further issue is the data base on which policies to enhance finance can build.
Access to finance for entre -preneurs is an area with scarce comparable data across countries (see section 4. 2. 2). In order to ensure
evidence-based policies for high-growth SMES, the European commission could seek to further improve the development of related databases
180 See European commission (2010), p. 14-15; see also EVCA (2010), p. 13 181 The EC launched a â€oeconsultation on a new European regime for venture capital†in June 2011
Here again SMES have to scan the EEN technology database or to subscribe for the EEN
open call topics †not just open calls †and technologies from the EEN database ranked by relevance.
The access to meta-data would be straightforward. It would empower the user to discover new knowledge and open opportunities
without having to process extensive data and information from various sources. In this way, the EEN could
and interpreting innovation data. The Measurement of Scientific and Technological Activities. Third edition. A joint publication of OECD and Eurostat
Diversification and clustering of SMES for future growth...74 5. 5 Israel: Envisaged targeted support for high-growth sectors and SMES...
Consistent statistical data is missing Comparable international data about high-growth SMES are missing, so that a consistent picture of
their prevalence cannot yet be drawn. The OECD -Eurostat Entrepreneurship Indicators Programme found that in 2006,
European countries for which data were available A Eurobarometer study found that in several Euro -pean countries the share of high-growth firms in
clustering of SME business activities. Diversifica -tion policy initiatives focus on supporting SMES to move (1) up the technology ladder,(2) between
data, theoretical ideas and empirical results. Chapter 4 analyses current policy developments, focusing on European and national policy approaches as well as specific issues related to entrepreneurship, access to
Matrix of main data sources for INNO-Grips Policy Brief 2 Quantitative focus Qualitative focus
Primary data collection ï Representative enterprise survey (CATI ï INNO-Grips case studies and case briefs
ï OECD and Eurostat databases ï Data from industry associations ï Existing case studies from various
sources ï Literature evaluation (desk research 12 Research and development do not necessarily have to take place, considering for example user entrepreneurs who
Data from various secondary sources is used here not only for exhibiting numbers of high-growth enterprises but also for other indicators such as venture capital provision
Primary data collection The description of examples of successful support of high-growth innovative companies is a key element of
issues of human capital, access to specialised technology and business consulting, R&d clustering technology scouting to identify R&d projects with commercial potential, technology transfer, and an
However, the study concludes that â€oefrom the nature of the data collected and the limited number of examples
unsatisfactory statistical data From a scientific point of view data availability is always unsatisfactory, but measurement of entrepreneurial
activity, including high-growth SMES, apparently remains particularly difficult. Internationally comparable data are scarce. The most notable initiative to make international data on entrepreneurship available may be the
joint OECD-Eurostat Entrepreneurship Indicators Programme (EIP) launched in 2006. Some key findings from the EIP are presented in the following,
supplemented by data from other sources OECD The EIP provides data about high-growth enterprises
which may be taken as a proxy for data about high -growth innovative SMES. Data are available for 15 countries,
divided by manufacturing and services. 19 The most recent data available at the time of authoring this Policy Brief were for 2006.
For this year, Bulgaria was on top for both manufacturing (8. 6%high-growth enterprises) and services (8. 2%)†see Exhibit 4. The fol
-lowing countries were Italy (8%/7. 9%),Estonia (7. 1%/5. 6%),Brazil (6. 9%/5%)and the USA (5. 9
%/19 See OECD (2009), pp. 28-31 Policies for high-growth innovative SMES v1. 6
high-growth enterprises, the USA were nevertheless ahead of most other European countries for which data
Among the countries for which data are performed available, Bulgaria best (2. 3%gazelles in manufacturing, 1. 9%in services.
A Eurobarometer survey in 2009 of more than 9, 000 companies provided data for all EU-27 countries. 20 As
the denominator and the data source is different from the OECD data, both datasets cannot be compared.
the related data 21 See Veugelers (2009), p. 2. The largest US companies were taken from the Financial times Global 500 of 2007, the
Firm-level data was provided by the Zentrum fã r Europã¤ische Wirtschaftsforschung (ZEW), Mannheim, Germany
and contraction in Europe and the US, drawing from a purpose-built database of business growth in the period from 2002-2005 with individual records for six million businesses.
A Kauffmann Institute study of the US economy in 2010 with data for 2007 contained 5. 5 million firms.
longitudinal data sets found that â€oethe profitable low growth firms are both more likely to reach the desirable
and 26 by GIF2 at an average cost of 600,000 euro. 61 No valid data for
It also tracks baseline data for its per -formance, such as employees, revenue growth and number of customers.
of growth finance can hardly be based on solid data. â€oeaccess to finance†for entrepreneurs and young busi
-nesses, both debt and equity capital, is one area where there is scarce availability of comparable data
often reliable data are not even available at the country level. 96 92 Definition of the European Venture capital Association, see http://www. evca. eu/toolbox/glossary. aspx?
The Eurobarometer survey quoted in the following provides insightful data and it is based on almost 10,000 interviews,
-cal data for EU companies, the report shows that â€oeinnovative companies are more likely to exportâ€, that â€oethey
The relationship between internationalisation and clustering may be of particular interest, since local clusters are seen often as breeding grounds for innovation.
-pean and national level. 126 One could assume that clustering and internationalisation mutually reinforce each
clusters and the relationship between clustering and internationalisation cannot be dealt with in depth in this Policy Brief, this finding strongly points to the importance of internationalisation for innovation
-tween clustering and internationalisation; see http://www. proinno-europe. eu/tactics 128 Dahl Fitjar/Rodrã guez-Pose (2011), p. 5
and clustering of SME business activities. Diversification policy initiatives focus, among other items, on sup -porting SMES to move between industries (new business activities in related industries) â€
Micro United Network Pte Ltd (http://www. microunited. com. sg) provides voice, video and data
which data are available †when a combination of venture capital 150 See Cooper (2009 Policies for high-growth innovative SMES v1. 6
funds but there is no data available to measure the investment performance of this group of funds
though they had the data, the review did not assess the presence of high growth firms or gazelle aspects. 160
Awards-U s. Small Business Administration Tech-Net Database; Responses-NRC Phase II Survey and NIH Phase II Survey and updates. http://www. ncbi. nlm. nih. gov/bookshelf/br. fcgi?
Diversification and clustering of SMES for future growth Summary Although the fall out from the 2008 Lehman brothers collapse continues to skew the Japa
diversification and clustering of SME business activities. Diversification policy initiatives focus on supporting SMES to move (1) up the technology ladder (product development),
Clustering policy initiatives focus on promoting (1) local clusters, such as regional linkages among small and medium manufacturers, and (2) network
and clustering policy initiatives, the government†s 2009 New Growth Strategy (Basic Policies Toward a Radiant Japan identifies SMES as an engine for future high economic growth
According to the OCS data most of the grants are provided to high growth SMES, though the OCS makes great efforts to increase the
Other items with outstandingly high percentages may confirm this interpretation of the data. 83%of the high
Improving the data base for company finance A further issue is the data base on which policies to enhance finance can build.
Access to finance for entre -preneurs is an area with scarce comparable data across countries (see section 4. 2. 2). In order to ensure
evidence-based policies for high-growth SMES, the European commission could seek to further improve the development of related databases
180 See European commission (2010), p. 14-15; see also EVCA (2010), p. 13 181 The EC launched a â€oeconsultation on a new European regime for venture capital†in June 2011
Here again SMES have to scan the EEN technology database or to subscribe for the EEN
open call topics †not just open calls †and technologies from the EEN database ranked by relevance.
The access to meta-data would be straightforward. It would empower the user to discover new knowledge and open opportunities
without having to process extensive data and information from various sources. In this way, the EEN could
and interpreting innovation data. The Measurement of Scientific and Technological Activities. Third edition. A joint publication of OECD and Eurostat
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