Science and Public policy February 2010 0302-3427/10/010051-11 US$12. 00 Beech tree Publishing 2010 51 Science and Public policy, 37 (1 february 2010, pages
51 61 DOI: 10.3152/030234210x484775; http://www. ingentaconnect. com/content/beech/spp User-driven innovation? Challenges of user involvement in future technology analysis Katrien De Moor, Katrien Berte, Lieven De Marez, Wout Joseph, Tom Deryckere and Luc Martens The shift
and content on the one hand (Yovanof and Hazapis, 2008) and hyper-competition and increased market liberalization on the other,
there has been an explosion of nondisruuptiv innovations that are not always clearly different from other products on the market (De Marez and Verleye, 2004;
Yovanof and Hazapis, 2008. In this changed context, many new products fail to‘cross the chasm'between the adoption segmeent that include innovators and some early adopteer on the one hand and the mass market on the other (Moore, 2002;
De Marez and Verleye, 2004. Furthermore, traditional product development strategies are said to have crucial shortcomings since they are no longer able to guarantee the successful adoption and diffusion of new ICT.
Although innovattio is considered traditionally to be a rather lineaar research -and price-driven process, this focus seems to have shifted over the years (Rosted,
2006), influenced by the altered role of the technology user as an important stakeholder. Confronted with almost unlimited choices,
users'demands have become more sophisticated. Today's users increasingly seek out those products and experiences that fit their persoona and situational needs.
2004; Veryzer and Borja de Mozota, 2005. T Katrien De Moor (corresponding author), Katrien Berte and Lieven De Marez are at MICT-IBBT, Department of Communicattio Sciences, University of Ghent, Korte Meer
7, 9000 Ghent, Belgium; Emails: Katrienr. Demoor@Ugent. be; Katrien. Berte@Ugent. be; Lieven. Demarez@Ugent. be;
Impacts and Implications for Policy and Decision-making, held 16 17 october 2008 at Seville, Spain. User involvement in future technology analysis Science and Public policy February 2010 52 Indeed,
although‘the consumer'has always been important, the rationale of involving the user has changed drastically.
the latter acknowledge the crucial role of users in the innovation process (Rickards, 2003; Trott, 2003; Von Hippel, 1986;
2005). ) In this context we can also refer to policy action that suppoort user-driven innovation, such as the rise of living labs, which are user-driven innovation environmments and the launch of the European Network of Living Labs (ENOLL) in 2006.
Although many other policy initiatives are embedded in this new innovaatio context, it remains difficult to create a meaningful synergy between users and technology in the field of ICT development.
This paper therefore aims to discuss the integratiio challenges still to be found in this user-centred context.
It is organized as follows: first, we expand on a number of theoretical perspectives on technoloog and society and the notion of user-driven innovattion We then explore the implications for traditional innovation and development processes.
2005). ) This theory of‘technological determinism'fits into the‘diffusion of innovations'framework (Rogers, 1995),
which is dedicated to the adoption and diffusion of new technologies in society. Technollog adoption is assumed to follow a predictable path
to the point where the adoptiio rate has become so high that the innovation can be considered successful (this is referred to as the‘critical mass')(Rogers, 1995).
After graduating in 2001, she worked for a commercial market research agency. She joined MICT in 2005.
Her research interests and publications lie in the field of quantitative survey analysis, new media and advertissing She is currently working on a Phd thesis on advertisiin in a digital media environment based on the IBBT research project ADME (website<http://projects. ibbt. be/adme>).
>Lieven De Marez obtained an MSC in communication sciennce (1999) and then an MSC in marketing (2000.
Wout Joseph holds a MSC in electrical engineering from Ghent University (2000. He started his career at the Departtmen of Information technology (INTEC),
University of Ghent and received his Phd degree in 2005. His research focused on the measurement
Since October 2007, he has worked for IBBT-INTEC as a postdoctoral fellow (FWO-V Research Foundation, Flanders.
Tom Deryckere received an MSC degree in electrical engineeerin (micro-and optoelectronics) from Ghent University in 2004.
In the same year, he started as a research enginnee at IBBT (Ghent University) in the field of interactive media.
Luc Martens received an MSC in electrical engineering and a Phd from Ghent University in July 1986 and December 1990, respectively.
From September 1986 to December 1990, he was a research assistant at INTEC, Ghent Universiity Since January 1991,
His research group joined the IBBT in 2004. User involvement in future technology analysis Science and Public policy February 2010 53 of the diffusion theory has to do with its proinnovvatio bias and the assumed linearity of the innovaatio and adoption process.
However, from the 1960s on this industry-push perspective was challenged by more human-centred paradigms that largely reject this notion of technologgica determinism
and which point to the deviation of adoption curves from Rogers'theory. One of them is the social shaping of technology framework,
which focuses on the daily use of technology and stresses the power of human actors and societal forces (Williams and Edge, 1996;
Lievrouw, 2006. This social constructivist vision aims to make technollog development more user-and human-centred.
Closely related to the social shaping perspective is the social construction of technology (SCOT) approoac (Bijker and Law, 1992), in
2005; Lievrouw, 2006. In the SCOT perspective, it is assumed that negotiatiio between certain social groups influences the construction and emergence of new technologies (Bijker and Law, 1992;
Haddon et al. 2005). ) Although both approaches emphasize the interactiio between technological and societal forces, they have been criticized for their rather linear social determiinism Other theories have a less linear view:
e g. the actor-network theory (Latour, 1993), which states that technology and people are part of sociotechhnica networks,
which influence the shaping, forms and uses of (new technologies. This and other approaches try to focus on technological developmeen from a mutual shaping or interactionism point of view (Lievrouw, 2006.
They provide us with a theoretical basis for uniting the technology-centred with the user-or human-centred vision,
since the successful adoption and diffusion of technology is ascribed to the continuous interaction between technoloogica and societal forces (Rickards, 2003;
Trott, 2003; Boczkowski, 2004. User-driven innovation In this new context, the notion of user-led or userdriive innovation has assumed a prominent role.
In current definitions,‘user-driven innovation'refers to the process of collecting a particular type of informattio about the user:
it deals with insights both at an observable and a more latent level that are quite difficult to grasp (Rosted, 2006).
As a result, userdriive innovation requires an interdisciplinary approach. Several approaches have been put forward for the collection of this type of knowledge.
Hansson (2006) distinguishes two types of user-driven innovattio methods: voice of the customer methods and lead-user methods.
Eric Von Hippel's work on‘lead users'(1986) can undoubtedly be regarded as pioneeerin in this respect.
2007). ) Følstad (2008) situates the rise of living labs in this context of user-driven innovation. Living labs are innovation environments that provide full-scale test-bed possibilities for inventing, prototyping,
interaactiv testing and marketing of (new) mobile technology applications (Schumacher and Niitamo, 2008; Følstad, 2008. They can be seen as humancenntri systemic innovation instruments,
encouragiin the interaction between all stakeholders in the innovation process and facilitating the involvement of users as co-creators (Ballon et al. 2007).
As discussse by Warnke and Heimeriks (2008: 74), systemmi innovation instruments are intended: to provide platforms for learning
and experimeenting facilitate the management of interfacces foster new alignment of elements and stimulate demand articulation, strategy and visiio building.
Contrary to other test platforms, living labs provide a more natural testing environment and strongly encouurag continuous and meaningful interaction betwwee developers/suppliers and users.
and at a more latent level that are quite difficult to grasp User involvement in future technology analysis Science and Public policy February 2010 54 narrow and technology-centric scope of many projects.
2008; Følstad, 2008. For example, in the early development stages it is often difficult to transcend users'limited powers of imagination:
without having a fully developed ICT device at their disposal, users do not have a clear-cut idea of
Limonnar and de Koning, 2005: 176) This challenge requires a consolidation of knowleddg and tools from various disciplines (e g. foresigght design,
Severra scholars have focused on the fact that there are still only a few companies that effectively involve the customer or user in the innovation process (Alam, 2002;
2004). ) Kristensson et al. 2004: 4 5) attribute this discrepancy betwwee theory and practice mainly to the lack of empirical evidence on the benefits of userinvollvemen and user-oriented strategies compared to traditional research and development.
Although research has indicated that if new product developmmen fails, it usually goes wrong from the beginniin (Khurana and Rosenthal, 1998),
user involvement is limited too often to just one or only the final stage (e g. usability testing, evaluation etc.)(
2005). ) However, the benefits of involving users continuously have already been investigated: users can for example generate unique and valuable ideas for future products (Kristensson et al.
2004). ) User-driven innovation should thus go beyond merely asking users for feedback after the piloting phase or launch.
The notion of translators is used also in this context (Veryzer and Borja De Mozota, 2005. In this respect it is relevant to mention the gap betwwee Qoe and Qos, two important concepts in the field of ICT development.
which refers to‘general application service performance'(Soldani, 2006: 1), received a lot of attention in the past, it seems that Qoe has driven now taken over
Experiience are seen as a new source of value (Pine and Gilmore, 1999: 2) and the nature of users'experiience with new products can determine their success or failure (Crisler et al.
2004; Jain, 2004) In this changed context, Corrie et al. 2003: 2) emphasiiz the importance of users'expectations and experiences:
Qoe is how the user feels about how an applicattio or service was delivered, relative to their expectations and requirements.
De Marez and De Moor (2007) looked into Qoe at a conceptual level and identified five main dimensions and over 70 subdimensions.
Given this diversity of factors influencing users'Qoe, its adequate measureemen and translation remains a challenge:
Microsoft, Concentra and i-City) and the IBBT, founded by the Flemish Government in 2004 to stimulate innovation in the ICT domain.
i-City's User involvement in future technology analysis Science and Public policy February 2010 55 large-scale living lab was the main research location.
and companies to gain an insight into the main drivers and constraints in service innovation and into the conditions for meeting social and user requirements (Lievens and Pierson, 2006).
Methodological framework The common methodological framework covered three main research stages in the innovationdevellopmen process (Lievens and Pierson, 2006.
and find it difficult to empathize with other users'lifestyyles e g. a 25-year-old reflects only on his daily Innovation-development process Prior-to-launch Post-launch R&d Opportunity identification Concept design Concept development
evaluation Figure 1. Schematic overview of the three research phases User involvement in future technology analysis Science and Public policy February 2010 56 activities and finds it difficult to identify with the life
An example of one of the archetypes is Patricia (see Table 1). Patricia is 40 years old, a manager in a major international firm,
which took place at the end of 2006, participants imagined they were in the year 2010 and were restricted therefore not by current legislation
and technological limitations. 47‘wild ideas'were generated in these sessions, all original and very useful for subsequent stages of the research project.
and Public policy February 2010 57 the use of mobile applications to support their existiin products and services.
2008). ) Since the results from earlier user research were Table 3. 13 clusters and corresponding Cronbach's alpha values Application cluster Cronbach's alpha Food and shopping help 0. 871 Tourist information 0. 775 Mobile social contact
User involvement in future technology analysis Science and Public policy February 2010 58 disregarded, this choice illustrates that decisions are made sometimes at the expense of the user-centred rationale.
2008), this paper limits itself to a discussion of the research process and the way the abovementioned challenges were tackled.
2010 59 4. battery lifetime plus security; and 5. response time. 2. Pre-usage translation workshops.
For example, we selected user 10 (male, 33 years old) to expllai the results for an individual user.
Q2, Q5 and Q6 User involvement in future technology analysis Science and Public policy February 2010 60 Conclusion In this paper, we have focused on the shift from traditiiona technology push to more user-oriented and user
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Science and Public policy February 2010 0302-3427/10/010063-16 US$12. 00 Beech tree Publishing 2010 63 Science and Public policy, 37 (1 february 2010, pages
63 78 DOI: 10.3152/030234210x491623; http://www. ingentaconnect. com/content/beech/spp Impact of Swiss technology policy on firm innovation performance:
and Innovation (CTI) on the innovation performance of the supported firms based on a matched-pairs analysis of 199 firms supported by the CTI in the period 2000 2002.
and the corresponding control groups for the period 2002 2004. Matching methods based on direct comparisons of participating
2003; Czarnitzki and Fier, 2002 (for Germany; Pointner and Rammer, 2005 (for Austria; Görg and Strobl, 2007 (for Ireland.
1 A major advantage of the matching methods rather than the regression approach is that the matching is nonparametric.
As such, it T Spyros Arvanitis is at KOF Swiss Economic Institute, ETH Zurich, 8092 Zurich, Switzerland;
Email: arvanitis@kof. ethz. ch; Tel:++41 44 632 51 68; Fax:++41 44 632 13 52.
Impact of technology policy on innovation by firms Science and Public policy February 2010 64 avoids the functional form restrictions implicit in running a regression of some type.
we identified the subsidized firms in the period 2000 2002 from the CTI database. We collected innovation data for the promoted firms similla to those already existing for a sample of innovating firms of The swiss Innovation Survey 2002 (Arvanitis et al.
2004). ) We estimated the propensity scores with respect to the likelihood of receiving a CTI subsidy. We then applied four different matching methods
in order to find the structurally similar‘twin'firms for every subsidized firm. We tested the statistical signifiicanc of the difference of the means of six differeen innovation measures of the subsidized firms and the non-subsidized firms of the matched control group.
For the period 2002 2004 we found that (with one exception), for all six innovation measures and for all four matching methods applied,
A further way of supporting private investment in innovation is through tax incentives for R&d expenditures (see Jaumotte and Pain, 2005 for a survey of the main fiscal policies to support innovation.
2007). ) This tradition is based on a wide Spyros Arvanitis is a senior researcher at the KOF Swiss Economic Institute and a lecturer in economics at the ETH Zurich.
Luarent Donzé has been an associate professor at the Univerrsit of Fribourg, Switzerland since 2002. Before this appoinntmen he was a senior researcher at the KOF Swiss Economic Institute at the ETH Zurich.
Nora Sydow has been at the Economic Research Departmeen of Credit suisse since 2008. Before this appointment she was a researcher at the KOF Swiss Economic Institute at the ETH Zurich and responsible for the KOF Enterprise Panel.
Impact of technology policy on innovation by firms Science and Public policy February 2010 65 consensus not only among political actors but also among organizations representing business interests.
2007), less than 10%of Swiss firms perceive a lack of public R&d promotion to be a strong,
this percentage has remained practically constant since 1990. As a consequence, only a few fiscal initiatives to support research
and innovation at firm level have been launched in recent years. CTI is the government agency through which public funds are poured into the business sector.
2000; Jaffe, 2002; Arvanitis and Keilbach, 2002. In this study we apply matching methods to evaluate the impact of R&d subsidies on the innovatiio performance of subsidized firms.
A major advanntag of the matching methods over the regression approach is that the matching is nonparametric.
As such, it avoids the functional form restrictions impliici in running a regression of some Kind of course,
in order to avoid ex ante selection bias would be to undertake an evaluation by awarding grants (subsidies) randooml within a pool of actors who are judged suitabbl for funding (Jaffe, 2002).
2000; Hall and Van Reenen, 2000; Klette et al. 2000; Jaumotte and Pain, 2005), a finding also confirmed by the meta-analysis by Garcia-Quevado (2004)
which was based on the results of 39 studies of the effectivenees of public subsidies. All overviews emphasize the importance of the control variables included in any empirical assessment and the level of aggregation at
which a study is conducted. Differences with respect to these two factors seem to explain a large part of the differences found between empirical studies.
Thus, for the assessment of a study, it is necessary to take these two factors into consideration.
To the best of our knowledge, it is unique in Europe as a main promotional policy Impact of technology policy on innovation by firms Science and Public policy February 2010 66 either matching approaches (as in this paper) or selecctio
Most studies Table 1. Summary of selected empirical studies Study/country Policy instrument being evaluated Number of firms Approach Impact on target variable Sakakibara (1997),
Japan Government-sponsored cooperrativ R&d projects organized by Ministry of International Trade and Industry (1983 1989) 226 Selection correction:
+Busom (2000), Spain R&d subsidy programme 1988 154 Selection correction: Two-equation system (participation eqn.:
+Wallsten (2000), USA Small Business Innovation research (SBIR) Programme (1990 1992) 81 Selection correction: Three-equation system (two different participation eqns.:
R&d spending 1992: -employment 1993: no effect Arvanitis et al. 2002), Switzerland Programme of promoting use of Computer Integrated Manufacturing Technologies (CIMT)( CIM Programme, 1990 1996) 463 Selection correction:
Two-equation system (participation eqn. CIMT adoption eqn. Change in CIMT intensity (1990 1996:++for firms with less than 200 employees+for firms adopting CIMT for first time Donzé (2002),
Switzerland Programme of promoting use of CIMT (CIM Programme, 1990 1996) 463 Matched-pair analysis (several alternative methods) Change in CIMT intensity (1990 1996):+
+for firms with less than 200 employees+for firms adopting CIMT for first time Lach (2002), Israel R&d grants from Office of Chief Scientist at Ministry of Industry and Trade (1990 1995) 325 Difference
-indifference estimator R&d spending:++for small firms no effect for large firms Czarnitzki and Fier (2002), Germany Public innovation subsidies in German service sector 210 Matched-pairs analysis (nearest
neighbour matching) Innovation expenditure: innovation expenditure/sales:++Almus and Czarnitzki (2003), Germany R&d subsidies to East german firms (1994,1996, 1999) 622 Matched-pairs analysis (calliper matching) R&d intensity:+
+Pointner and Rammer (2005), Austria Programme of promoting use of CIMT (Flexcim Programme, 1991 1996) 301 (a) Selection correction:
Two-equation system (participation eqn.:CIMTADOPPTIO eqn. b) matched-pair analysis Change in CIMT intensity (1992 1998:+
+for firms with less than 200 employees+for firms with low intensity of CIMT use Görg andstrobl (2007), Ireland R&d grants from (Industrial Development Agency (IDA) Ireland and Forbairt
(1999 2002) 828 Combination of matching approach and difference-indiffeerenc estimator R&d spending; R&d spending per employee:
small domestic firms:++medium domestic firms: no effect; large domestic effects: -all size classes of foreign firms:
no effect Bérubé and Mohnen (2007), Canada R&d tax credits versus R&d tax credits+R&d grants 584 Matched-pairs analysis (nearest neighbour matching) Firms with tax credits
+R&d grants are more innovative than firms with only tax credits for 6 out of 8 innovation indicators Notes:+(
+-positive (negative) and statistically significant effect at 10%test level Impact of technology policy on innovation by firms Science and Public policy February 2010 67 find a positive policy effect but in some cases
only for small firms. The USA study is the only one, which finds a negative effect for R&d spending,
a list of the firm projects that were subsidized by the CTI in the period 2000 2002;
additional information on the firms whose projeect were subsidized that was collected through a survey of the subsidized firms based on a shortenne version of the questionnaire used in The swiss Innovation Survey 2002;
and the data for firms that reported the introduction of innovations in the period 2000 2002 in The swiss Innovation Survey 2002.
The CTI database contained information on 634 subsiddize R&d projects that were finished between 1 january 2000 and 31 december 2002.
firms that had ceased to exist by December 2003 were also remoove from the sample. The final sample contained 307 subsidized firms.
A further 14 subsidized firms were identiffie among the participants of The swiss Innovation Survey 2002.
The 996 firms that participated in The swiss Innovattio Survey 2002 and reported the introduction of innovations in the period 2000 2002 built the pool of non-subsidized firms from
which a control group was constructed (KOF panel database). For the firms that finished their projects subsidiize by the CTI during the first half of the period 2000 2002,
i e. until the middle of 2001, we reckon that they would still have had one -and-a-half years until the end of the reference period to realize some impact of these projects on their innovation performmanc (e g. introduce new products);
one-and-ahaal years is an adequate time lag between R&d and realization of R&d outcomes for most industries and for incremental innovations.
For the firms that compleete their subsidized R&d during the second half of the reference period, particularly in the year 2002, it is questionable,
whether or not they would have had enough time until the end of 2002 to realize any additional innovation gains. 53%of projects were finished by the middle of 2001,78%by the end of 2001.
Hence, for the large majority of the projects there was enough time to have a measurable impact of R&d on their innovation performance.
For the remaining 28%of the firms it is possible that only part of the impact could be realized before the end of 2002.
In this sense our estimations of the impact of CTI promotion would thus represent a lower bound on the possible effects.
Patterns of CTI promotion in period 2000 2002 As already mentioned, in the period 2000 2002 634 R&d projects were supported by the CTI.
Table 2 shows the scientific fields in which these projects were located and the amount of the subsidies granted by scientific field.
%Impact of technology policy on innovation by firms Science and Public policy February 2010 68 significantly lower than the respective share of projeect of these scientific fields.
1998) developed a methodology to approximate this non-observable(‘counterfactual')state of a certain firm with the observable same state of another firm which is‘structurally similar'to the first one according to a series of firm characteriistic formally defined by a vector
we need a pool of Table 2. Subsidized projects and volume of subsidy by scientific field 2000 2002 Scientific field Number of projects Percentage CTI subsidy (in CHF (Swiss francs)) Percentage CTI
CTI database, authors'calculations Table 3. Subsidized enterprises by scientific field 2000 2002 Scientific field Number of firms Percentage Construction technology 11 5. 5
CTI database, authors'calculations Impact of technology policy on innovation by firms Science and Public policy February 2010 69 firms which are subsidized not out
1983) to a monodimennsiona (scalar) propensity score which comprehhend the entire information of all relevant characteristics. 5 The state of a firm belonging to the group of the‘treated'firms is described by d=1,
age of firm(‘firm founded before 1996'),size of firm (dummy variables for six size classes), industry affiliation (dummy variables for three sub-sectors),
G g a-=0 N a Impact of technology policy on innovation by firms Science and Public policy February 2010 70 (6) where and is the kernel7 at the point In a fifth step,
In a sixth and last step we calculated a subsidy quotient for every subsidized firm by dividing the amount of the granted subsidy by the total R&d expendiiture in the period 2000 2002.
5%test level Impact of technology policy on innovation by firms Science and Public policy February 2010 71 innovation performance than non-subsidized firms (at the 5%test level.
5%test level Impact of technology policy on innovation by firms Science and Public policy February 2010 72‘low-subsidy'firms from that of the respective groups of non-subsidized firms.
Conclusion Based on a matched-pairs analysis of 199 firms supporrte by the CTI in the period 2000 2002 and a control group of 996 firms that were supported not by the CTI,
if an amount of about CHF60 million in 2004 (meanwhile CHF100 150 million of additional R&d support per annum) could have a discernible impact on an economy that invested about CHF19 billion in R&d in 2004.
Impact of technology policy on innovation by firms Science and Public policy February 2010 73 Appendix Table A1.
research project subsidized by CTI in period 2000 2002, yes/no) Firm characteristics Test level 5%Firm characteristics Test level 5%Firm size:
Firm founded before 1996-0. 86 French 0. 56 (0. 14)( 0. 10) German N 1317 Adj. Mcfadden-R2 0. 14
Italian (continued) Impact of technology policy on innovation by firms Science and Public policy February 2010 74appendix (continued) Table A3.
*See footnotes to Table A3 for key (continued) Impact of technology policy on innovation by firms Science and Public policy February 2010 75 Appendix (continued) Table A5.
*See footnotes to Table A3 for key (continued) Impact of technology policy on innovation by firms Science and Public policy February 2010 76 Appendix (continued) Table A7.
Results with respect to magnitude of subsidy quotient for 2000 2002, calculated using‘nearest neighbour'method Measures of innovation performance Subsidized firms:
Results with respect to magnitude of subsidy quotient (2000 2002) using‘calliper'method Measures of innovation performance Subsidized firms:
and Public policy February 2010 77 Notes 1. See Bozeman (2000); Georghiou and Roessner (2000; and Feller (2007) for recent reviews of the central issues related to the evaluation of the effectiveness of technology programmes.
See also Science and Public policy (34 (10), 679 752) dedicaate to‘New frontiers in evaluation'.'Finally, see OECD (2006a) for an analysis more from the point of view of the policy-maker;
Polt et al. 2001) for the role of framework conditions for the evaluation of industry university collaboratioons and Polt and Streicher (2005) for the evaluation of large programmes such as the Framework programmes of the Europeea Union. 2. For overviews of Swiss
technology policy see OECD (2006b) and European commission (2008. Lepori (2006) gives a longteer analysis of public research policy primarily with respect to universities and public research organizations.
Griessen and Braun (2006) deal with the problems of political coordination of innovation policies in Switzerland.
Appendix (continued) Table A. 9. Results with respect to magnitude of subsidy quotient (2000 2002) using‘kernel'method Measures of innovation performance Subsidized firms:
subsidy quotient>median Subsidized firms: subsidy quotient<median Difference of means of subsidized/nonsubsiidize firms Statist. signif.
test level 10%)Difference of means of subsidized/nonsubsiidize firms Statist. signif. test level 10%)Difference of the difference of means (column 3-column 2) Importance of introduced innovations from a technical point of view*0. 39 Yes 0. 30 Yes Yes Importance of introduced innovations from an economic
Results with respect to magnitude of subsidy quotient (2000 2002) using‘local linear regression'method Measures of innovation performance Subsidized firms:
and Public policy February 2010 78 3. The questionnaire may be obtained from the authors. It is available in German,
1999) for a survey on various matching procedures. Caliendo and Huber (2005) and Caliendo and Kopeinig (2005) give overviews of recent developments with respect to matching methods. 6. We used a‘biweight kernel'(quartic kernel) for the function G(.)It is defined as follows:
The results are sensitive, not to the kernel function used, but to the choice of the bandwidth. 7. We also used the‘biweight kernel'here (see Note 6). The bandwidth was determined as follows (Silverman, 1986):
an=2. 7768 (H/1. 34) N-1/5 where N is the number of observations of the control group or the group of treated firms,
There is some measurement error in this calculation due to the time incongruence between subsidies granted before the beginning of 2000
and R&d expenditures strictly referring to the period 2000 2002 that unfortunately cannot be quantified and corrected.
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