Embed Size (px)
Citation preview
Int. J. Technology Management, Vol. 62, No. 1, 2013 1
Copyright © 2013 Inderscience Enterprises Ltd.
Open innovation practices and innovative performances: an international comparative perspective
André Spithoven Belgian Science Policy Office, Avenue Louise 231, 1050 Brussels, Belgium E-mail: [email protected]
Abstract: This paper looks at firm-level differences in R&D intensity and sales from product innovations. R&D intensity and innovative sales are explained by a model using harmonised firm-level data from the Third Community Innovation Survey for three European countries: Belgium, Germany and Spain. For each country, the average estimates suggest that incoming knowledge spillovers, research cooperation, appropriability and human capital all exercise a positive influence on R&D intensity and, through this, on innovative sales. Quantile regressions indicate that, although there are differences between countries, the joint relative impact of incoming knowledge spillovers and research cooperation tends to increase quantile-wise in relation to that of appropriability. This suggests that strong innovators have a more open innovation process.
Keywords: open innovation; innovative performances; country comparisons; Belgium; Germany; Spain; quantile regression.
Reference to this paper should be made as follows: Spithoven, A. (2013) ‘Open innovation practices and innovative performances: an international comparative perspective’, Int. J. Technology Management, Vol. 62, No. 1, pp.1–34.
Biographical notes: André Spithoven is Senior Researcher at the Belgian Science Policy Office where he analyses data on R&D and innovation. He holds a PhD in Applied Economics from Ghent University. He publishes on R&D data of the non-profit sector in Belgium, especially on technology transfer intermediaries, open innovation, spatial organisation of R&D and innovation. He has co-edited books on the internationalisation of R&D and on industry-science relationships, and has published in international peer reviewed journals. He is a reviewer of several journals related to R&D and innovation.
1 Introduction
As R&D and innovation are a primary source of economic growth, policies to encourage company-level R&D and innovation figure high on the agenda of most countries. Examples are the European R&D intensity target (i.e., 3% of R&D investment as a percentage of gross domestic product by 2020) and the Innovation Union (European Commission, 2010). Every day, the press reports on countries that move up and down the ranking. As shown in the literature on innovation systems (Nelson, 1993; Schmoch et al.,
2 A. Spithoven
2006; Fagerberg and Srholec, 2008), all countries are idiosyncratically organised and, therefore, have divergent results. Insights on the determinants of company-level R&D intensity are essential to effectively design innovation policies and to estimate how interventions to further open innovation practices might affect innovative sales. The issue is to find out whether these innovation systems affect company-level innovation management in any way. By comparing the open innovation practices in several selected countries, we hope to contribute to this theme by examining whether open innovation is a global phenomenon that is common among different countries.
The Innovation Union Scoreboard (Pro Inno Europe, 2012) identified a profile of clusters of countries. Innovation performance among innovation leaders, such as Germany, is markedly higher than the EU average. Countries whose innovation performance is below that of the innovation leaders but above the EU average, like Belgium, are called innovation followers. Countries with performances below the EU average who do not show signs of improvement, like Spain, are considered to be moderate innovators. These profiles are based on scores from 29 separate indicators. However, R&D intensity as a policy target is the most widely discussed item. With 2.78% in 2009, Germany is indeed the highest; Belgium follows with 1.92% and Spain with 1.38%. A similar picture emerges when R&D intensity is limited to the business sector: Germany leads with 1.90%, followed by Belgium with 1.32% and Spain with 0.72% (OECD, 2011).
The Scoreboard compares the innovation performance of different countries. However, it generally uses various data that are to a certain extent country-specific due to institutional aspects (Chen, 2008) summarised by innovation systems (Bergek et al., 2008). The organisation of the education system or the financing and support measures for R&D and innovation are largely co-determined by the institutional fabric of the innovation systems (Nelson and Rosenberg, 1993; Fagerberg and Srholec, 2008). Yet, these studies remain silent on the behaviour of companies in this innovation process, implicitly assuming that either they react similarly to the innovation system in place, or that there is no company-level impact at all. This paper sets out to investigate whether the differences of the Innovation Union Scoreboard are corroborated by a company-level study of the innovation process. Or, more specifically, if there are country differences in the company-level determinants on R&D intensity and product innovativeness. As these determinants are related to dealing with spillovers, network activities and appropriability issues, the literature on open innovation might prove a useful background. This paper offers some empirical insights into this issue.
During the past decade, econometric literature has improved our understanding of the relationship between innovation and company performance by estimating structural models explaining both the input and the output stages of the innovation process, as well as its impact on productivity or on sales and employment growth [see, especially, the influential studies by Crépon et al. (1998), for France; Lööf and Heshmati (2002, 2006), for Sweden; Klomp and Van Leeuwen (2001), and Van Leeuwen and Klomp (2006), for the Netherlands]. Due attention is also given to the problems of endogeneity and of sample selection, which hampered earlier work.
Most of these studies suffer from a series of shortcomings. By considering separate countries or innovation systems, it is unclear to what extent their results can be generalised. Furthermore, by concentrating on manufacturing they tend to neglect some of the most relevant sectors for the emerging ICT-based ‘new economy’, such as knowledge intensive services. And, most importantly, the estimation methods
Open innovation practices and innovative performances 3
predominantly used only allow general statements about the innovative performance of an ‘average company’. The illusion of a ‘representative company’ may, however, be highly misleading in view of the heterogeneity in innovative behaviour (Ebersberger et al, 2006; Coad and Rao, 2008; Spithoven et al., 2010a).
Some attempts have been made to remedy these problems. Thus, a handful of comparative analyses have endeavoured to identify common patterns and systematic differences in innovative behaviour between sets of countries [see Mohnen and Therrien (2003), on Canada and selected European countries; and Griffith et al. (2006), on France, Germany, Spain and the UK]. But, again, they only concentrate on manufacturing and on the average behaviour of companies. Some recent attempts have also been made to identify systematic differences in behaviour between strong and weak innovators, by means of quantile regression analysis. Thus, Ebersberger et al. (2006) have presented quantile regression estimates on data for Finland of an equation explaining innovative sales, covering services as well as manufacturing. Coad and Rao (2008) use quantile regression to explain the impact of innovation in terms of patents and R&D intensity on company growth measured in terms of total sales. Spithoven et al. (2010a) used quantile regressions on comparable data for Belgium to explain R&D intensity, innovative sales, the growth of sales and employment, and productivity respectively. They found that the relative impact of incoming knowledge spillovers and research cooperation on research intensity increased in quantile terms in relation to that appropriability. This would suggest that strong innovators have a more open innovation process than the weaker ones. But how representative is this result if innovation systems differ across countries?
This paper aims to answer this question by means of a comparative analysis of three European countries, or innovation systems, belonging to separate clusters in the Innovation Union Scoreboard (Pro Inno Europe, 2012) – Belgium, Germany and Spain – on the basis of harmonised company-level data from the Third Community Innovation Survey (CIS3). We shall concentrate on a model explaining R&D intensity and innovative sales. Our attention will first focus on the average behaviour of companies, and then on possible systematic behavioural differences between strong and weak innovators. A comparison of the country-wise results will allow common patterns and country specificities to be identified.
The paper is organised as follows: Section 2 presents the framework and Section 3 discusses the data and the estimation strategy. Section 4 presents the estimates on the average behaviour of companies and Section 5 refines this analysis by using quantile regression to identify behavioural differences between weak and strong innovators. Section 6 summarises and concludes. Tables with a description of the variables and mean values are presented in the Appendix.
2 Shaping the relationship between open innovation and company performance
2.1 Theoretical framework: open innovation practices and innovation systems
The paper draws on two different streams of literature – open innovation and innovation systems – that place external knowledge relations, spillovers, networking and appropriability at the centre of their ideas. The main difference between both approaches is that insights on open innovation practices are usually made at company level; while
4 A. Spithoven
those on innovation systems focus on countries, regions, industries and technologies (Hekkert et al., 2007; Bergek et al., 2008).
In general, the institutional context affects company innovativeness. This context refers to the values, rules and norms within a country (Edquist and Johnson, 1997) and to differences in institutional endowments (Hart, 2009) that have an impact on the capabilities of all actors in the system. A relevant factor in the study of open innovation practices is whether this institutional context affects the interactions between socioeconomic groups or actors, if it influences the routines of interaction, and how it changes the roles that the actors themselves play (Hall et al., 2003). A key concept in depicting the institutional context has been the innovation systems approach. Additionally, the literature on innovation systems offers a framework that explains why country differences exist. Nelson and Rosenberg (1993), and Fagerberg and Srholec (2008) argue that a main source of competitive advantage is the innovative capabilities of companies in a country. Nowadays, the term innovation system is commonly used to describe the conceptual framework to analyse the innovation process and technical changes. Scholars and practitioners using the innovation systems perspective thereby assume that these capabilities are, to a large extent, national in nature (OECD, 1997; Fagerberg and Srholec, 2008). But other geographical scales are also examined in terms of innovation systems. The regional level is exemplary for this (Cooke, 2004). Differences in countries persist because of differences in national history, culture, phases of industrialisation and other processes (Nelson and Rosenberg, 1993). These differences persist because of path dependency and lock-in effects. The globalisation of companies, however, is said to mitigate national differences (OECD, 2008). And the recent emphasis on open innovation further requires the innovative capabilities of companies to be increasingly streamlined (Chesbrough, 2003).
Ideas on ‘open innovation’ emphasise the importance of gaining access to external knowledge through networking activities or bringing products to market via external channels. One of the antecedents in this respect can be found in the actor interrelatedness that is central in innovation systems (Hall et al., 2003). A key element in the literature on innovation systems is organisational learning (Lundvall, 1992; Powell et al., 1996; Laursen, 2011). Powell et al. (1996) which points to the beneficial relationship between network relationships and company performance, even to the degree that the network becomes the locus of innovation. Hence, company activity in technological alliances (Shan et al., 1994; OECD, 2002) and cooperation (Cassiman and Veugelers, 2002; Veugelers and Cassiman, 2005) has a positive impact on innovative performance. Using incoming knowledge spillovers, resulting from a purposeful search for useful external knowledge, has been found to exert a positive impact on innovative performance (Laursen and Salter, 2006; Hekkert et al., 2007).
Recently, the links between innovation systems and open innovation became the focus of certain scholars in open innovation (Van de Vrande et al., 2010; Laursen, 2011). Based on a meta-analysis of 88 international refereed papers published between 2004 to 2008, Van de Vrande et al. (2010) conclude that open innovation should transcend the company level and be rooted more firmly in ideas about innovation systems. Laursen (2011) draws heavily on the ideas of Lundvall (1992) on interactive learning (Hekkert et al., 2007) and the relevance of absorptive capacity (Cohen and Levinthal, 1989), especially in the relationship between users/clients and producers which also figure prominently in the open innovation literature (see e.g., Huston and Sakkab, 2006).
Open innovation practices and innovative performances 5
There are, indeed, quite a number of similarities between these two streams of literature because they are based on insights into knowledge spillovers and market failures. But most importantly, both open innovation and innovation systems deal with inherent system failures in the field of innovation. Hence, open innovation and innovation systems are similar in several respects. First, both characterise innovation as being reliant on cooperation (Hekkert et al., 2007; Veugelers and Cassiman, 2005), networks (Powell et al., 1996; Hekkert et al., 2007) and interactions (Lundvall, 1992; Laursen, 2011) that take advantage of knowledge spillovers without risking appropriability problems in terms of knowledge leakage (Teece, 1986; Chesbrough, 2003). The upcoming role of technology intermediaries and brokers can be seen as illustrative for the increasing tendency for companies to look outside their company walls (Spithoven et al., 2010b). Second, innovation is explicitly pictured as a non-linear process (Kline and Rosenberg, 1986; Chesbrough et al., 2006). Third, internal capabilities are vital if external knowledge and incoming spillovers are used to take advantage of cooperation, networking and interacting (Cohen and Levinthal, 1989; Fagerberg and Srholec, 2008). Fourth, framework conditions are needed to effectively organise the flow of knowledge (Edquist and Johnson, 1997; Bergek et al., 2008). Technology transfer rests on favourable patent laws (Chen, 2008; Blind, 2012) and spillovers from basic research depend on solid relations between public research organisations like universities and companies (Monjon and Waelbroeck, 2003). Other regulatory framework conditions are the availability and mobility of highly qualified and skilled personnel, and the presence of innovative research infrastructures such as science parks and public research centres, etc.
The most blatant difference between open innovation and innovation systems is their unit of analysis, since open innovation focuses on companies and innovation systems on geographical areas, industries and technologies. This is because both strands of literature stem from different fields of study: management and economics. Until recently, the research on open innovation was limited to the practices of individual companies. As argued by Van de Vrande et al. (2010), the scope of studies has been expanded to include elements of innovation systems. Carlsson (2004) further posits that the innovation systems framework increasingly focuses on the microeconomic foundations of innovative performances.
Open innovation does not – by definition – take place in a vacuum. Companies are located somewhere and so are their markets, their information sources, and their research collaboration partners involved in their networks (Christensen and Drejer, 2005). The opening up of companies, and their tapping into the (internationally) distributed knowledge flow, emphasises the need for (cross-border) networking activities. Governments provide specific research infrastructures to facilitate networking activities in general and to strengthen industry-science relations in particular. As emphasised in the literature on innovation systems (Nelson and Rosenberg, 1993; OECD, 2002; Schmoch et al., 2006; Bergek et al., 2008), the role of public policy to accommodate the flow of knowledge and technology is important and differs across countries. A similar argument pertains to open innovation literature (De Jong et al., 2008; OECD, 2008). This makes (spatial) organisation an important topic for the study of open innovation (Cooke, 2005a, 2005b). As Simard and West (2006) put it: “The first implication for open innovation is that location matters. In some industries and technological environments, forming ties with and establishing a physical presence in a region where important knowledge resides will be key” [Simard and West, (2006), p.233]. Up to now, literature on open innovation
6 A. Spithoven
has been particularly non-spatially orientated. This brings the issue of location choices, the possibilities of engaging in networking activities, and the proximity to a knowledge centre to the fore (Christensen and Drejer, 2005).
Based on the preceding discussion, the research question in this paper looks at whether open innovation practices are part of a common story across countries. Is open innovation a global phenomenon? If any, what are the particular idiosyncratic differences between countries with respect to open innovation practices?
2.2 Relating open innovation to company performance
The relationship between innovation and company performance has been studied by using structural models (Crépon et al., 1998). These look at innovation by considering both the input and output of the system. Innovative input explains the R&D intensity of companies. Its explanatory variables are a set of open innovation practices, measures of human capital, competitive pressure and company size, and industry dummies depicting the technological environment.
The open innovation practices consist of incoming knowledge spillovers, research cooperation, appropriability of in-house knowledge and complexity of knowledge. Incoming spillovers are expected to affect R&D intensity positively because they increase the company’s knowledge base, from which it can draw its research process, and because the absorption of external knowledge requires additional in-house research efforts (Cohen and Levinthal, 1989). Cooperation on research projects, on the other hand, is thought to stimulate in-house R&D efforts to the extent that it raises the efficiency of the internal research process, and because the internalisation and commercialisation of the knowledge created through cooperative activities requires further research efforts by the companies concerned (Colombo and Garrone, 1996; Hagedoorn et al., 2000; Monjon and Waelbroeck, 2003; Becker and Dietz, 2004; Veugelers and Cassiman, 2005). A higher capacity in appropriating the benefits flowing from the commercialisation of in-house knowledge tends to reduce negative and unintentional spillovers to competitors and, thereby, increases the incentives to undertake research efforts (Spence, 1984; Teece, 1986). As for the complexity of knowledge, it can be expected to positively affect the level of research efforts to the extent that the greater complexity of basic research requires more effort than applied research.
These considerations lead to the following hypothesis:
Hypothesis 1 Open innovation practices have positive effects on R&D intensity, but these are more important in leading innovation systems.
When research cooperation occurs, the appropriability argument has to be somewhat attenuated, since it now only applies to spillovers to non-partners (Cassiman and Veugelers, 2002). In fact, in a context of increasing openness of the innovation process, a changing attitude is developing towards the creation of value by new ideas, as this may not only occur through product innovation, but also by selling these ideas in a disembodied form, through licensing, trademarks, and so on (Chesbrough and Rosenbloom, 2002; Chesbrough, 2003; Chesbrough and Crowther, 2006; Huston and Sakkab, 2006). To the extent that open innovators are likely to be found among strong innovative performers; a priori expectations are that the joint relative impact on R&D intensity of incoming knowledge spillovers and research cooperation will tend to increase quantile-wise in relation to that of appropriability. Hypothesis 2, therefore, accounts for
Open innovation practices and innovative performances 7
these quantile-wise differences in the effects of open innovation practices on R&D intensity.
Hypothesis 2 The effect of open innovation practices on R&D intensity differs for strong and weak innovators, and these differences vary across innovation systems.
The first control variable of R&D intensity is a measure of human capital (Cohen and Levinthal, 1989). Companies tend to spend more on research if they employ highly qualified workers, who are able to operate in a more complex production environment and may more easily contribute to the innovation process itself (Cohen and Levinthal, 1989; Redding, 1996). According to Chesbrough (2003), these knowledge workers are now abundantly available in each innovation system. Yet, as these systems differ, the impact of R&D intensity might also vary considerably.
To measure the competitive pressure a company experiences, export orientation is included since companies operating on foreign markets are likely to be subject to stronger competition. The need to remain competitive is assumed to affect R&D efforts positively (Aulakh et al., 2000).
To investigate whether company size has an impact on R&D intensity, the number of employees and group affiliation are included. A priori, we have no expectations as to the direction of effect (the sign of the estimated parameter), since there are arguments in the theoretical literature implying either a positive or a negative influence on R&D intensity (Cohen, 1995).
Finally, industry dummy variables based on Pavitt’s (1984) taxonomy have been added, depicting the general technological environment. These reflect inter-industry differences in technological trajectories and are further described below.
Innovation output concentrates on product innovation. It can be conceived as a production function of new knowledge and its dependent variable is the proportion of innovative products in sales. Its explanatory variables are the level of R&D intensity, a measure of process innovation, and the same knowledge, human capital, competitive pressure and size-related variables, as well as environmental dummies, that were used to explain innovation input.
The key explanatory variable is R&D intensity. Its influence on innovation output is not only a direct one, through the creation of new product innovative ideas, but also an indirect one, by contributing to the generation of complementary process innovations required for the implementation of product innovation (Parisi et al., 2006). Since part of the process innovations concern production re-organisations that do not require formal research efforts, a measure of process innovation, PCS, is included as a separate variable.
By already including determinants that affect innovation input, we are able to discover whether these determinants also have a direct influence on innovation output, rather than simply an indirect one, through their impact on R&D intensity. The size-related variables will, in this case, indicate whether there is a size-related research efficiency effect. These considerations on innovative sales lead to a second set of hypotheses.
Hypothesis 3 Open innovation practices exert an indirect impact on innovation sales through R&D intensity, but this indirect impact is more efficient in the case of leading innovation systems.
8 A. Spithoven
Hypothesis 4 The indirect impact of open innovation practices on innovation sales through R&D intensity differs among strong and weak innovators, and these differences vary across innovation systems.
Figure 1 shows the conceptual model used to screen the effects of open innovation practices on company performance in terms of R&D intensity and innovative sales.
Figure 1 Conceptual model
Open innovation practices
R&D intensity
Innovative sales
Complexity of
knowledge
Appro-priability
Research cooperation
Incoming knowledge spillovers
H1: +, and varies across innovation systems
H2: +, for strong innovators and varies across innovation systems
H3: +, and varies across innovation systems
H4: +, differs between strong and weak innovators and varies across innovation systems
3 Data and methods
3.1 Data
This study draws on data for Belgium, Germany and Spain, gathered as part of the Third European Community Innovation Survey, CIS3, released by Eurostat. Two main reasons justify the selection of the countries: firstly, the three countries pertain to different categories in the Innovation Union Scoreboard (Pro Inno Europe, 2012); and secondly, the innovation system in all selected countries is regionally organised, which makes it more easy to focus on knowledge-related features of these systems. The survey was conducted country-wise in 2002 and relates to the period 1998 to 2000. The target population was all enterprises with ten or more employees and the survey was stratified according to size and economic activity. After eliminating enterprises with incomplete data, this left us with 1,223 companies for Belgium, 2,813 for Germany and 7,826 for Spain. Of these, 492, 1,117 and 1,773 respectively reported in-house R&D, and 390, 931 and 1,367 both in-house R&D and innovative sales.
For reasons of confidentiality, these data were released by Eurostat in micro-aggregated form. In the case of Belgium we also had the original data for a larger set of companies (Spithoven, 2005). As a check of robustness, we undertook a similar estimation for Belgium based on this data. Although not presented here, the results showed highly similar parameter estimates. The statistical fit was, however, slightly worse than when micro-aggregated data was used. This confirms that micro-aggregation
Open innovation practices and innovative performances 9
does not affect the structure of the data and it suggests that, if anything, it helps to somewhat reduce noise due to errors of measurement.
Table A1 in the Appendix presents the precise construction and definition of all variables used and Table A2 their mean values. The remainder of this section contains some necessary remarks.
The measure of innovation input is the log of R&D intensity, LRDI. It is calculated as the natural log of the share of in-house R&D expenditures in sales. By concentrating on in-house R&D instead of on total innovation expenditure, the ability of companies to use incoming knowledge spillovers, to engage in research cooperation and to appropriate innovative results is stressed. Moreover, it is not certain whether all respondents fully understood the questions relating to other components of total innovation expenditure, which are more difficult to quantify (e.g., design, costs related to market introduction, packaging, etc.).
The measure of incoming knowledge spillovers, ISP, is based on questions on the importance attached to alternative sources of information on a three-point scale. These sources are: suppliers, clients, competitors, universities, government, professional conferences, and fairs and exhibitions. To generate a company-specific measure of incoming spillovers, we calculated the scores for each of these questions and rescaled the total score to a number between 0 and 1.
The measure of research cooperation, COOP, is a binary variable that assumes the value 1 in case of cooperation and 0 otherwise. Cooperation can occur either with suppliers, clients, competitors, research labs, universities or governmental research centres.
The measure of appropriability, APP, concentrates on methods of strategic protection, rather than on methods of legal protection through patents, because the latter are highly industry-specific (Griliches, 1990; Blind, 2012). Companies were asked whether they made use of strategic protection through secrecy, complexity of design and lead-time advantage over competitors. By giving a score of 1 if they did and 0 otherwise, it was possible to obtain an average score between 0 and 1 to generate a measure of strategic protection.
The measure of complexity of research, CK, is based on the answers to questions with respect to the sources of information. It is obtained by taking the ratio of the sum of the scores with respect to information from universities, government and professional conferences over the sum of the scores of all sources of information, as suggested by Cassiman and Veugelers (2002). In other words, it is inferred from the structure of the incoming knowledge flows, under the assumption that this conditions the character of the company’s in-house research.
Since the dataset does not contain figures on the total number of employees, the measure of higher education intensity, LHEI, is obtained as the log of the number of employees with higher education over the level of turnover. The total number of employees is provided in the original data (for Belgium), enabling the higher education intensity to be measured according to the proportion of employees with higher education. Experimentation showed that normalisation according to the level of turnover instead of total employment does not affect the results.
The level of innovative sales, LINSAL, is considered a measure of innovative output. It is calculated as the log of the proportion of new or significantly improved products (goods or services) in sales. It was preferred over a patent-based indicator of innovative
10 A. Spithoven
output because of the well-known shortcomings of the latter in this respect, as not all patents become innovations. The degree of patenting strongly varies between industries, and few service companies, like software development, tend to patent (Griliches, 1990; Blind, 2012).
The R&D intensity and innovative sales equations include industry dummies representing the general technological environment. These are based on the taxonomy originally proposed by Pavitt (1984), which was revised and elaborated by Tidd et al. (1997) to incorporate the services economy. This classification reflects differences in technological trajectories between groups of industries. It is based on the general characteristics of the technologies used in the production system, the skills needed to operate them, their sources and the monitoring strategy. Five groups of sectors are identified: supplier-dominated sectors (PAVSUPP), scale-intensive sectors (PAVSCAL), specialised supplier sectors (PAVSPEC), science-based sectors (PAVSCIE) and information-intensive sectors (PAVINFO). The two-digit NACE industries included in these groupings are mentioned in Table A1 in the Appendix. As seen from Table A2, the relative number of R&D active companies in the respective PAV groupings in the data, clearly differs between countries. This reflects differences in production specialisation.
3.2 Estimation methods
The innovation data of CIS3 refer to the period 1998 to 2000 and are cross-sectional in nature. The parameter estimates can, therefore, only provide a momentary impression of the long-term relations between the variables. The data do not allow the introduction of lagged responses.
The first goal was to obtain a general idea of the average company behaviour. We started by concentrating on companies who reported being R&D active in the case of the R&D intensity equation, and both R&D active and product innovative in the case of the innovative sales equation. An equation-wise estimation by ordinary least squares produces consistent parameter estimates provided that the explanatory variables are exogenous. Although this may seem plausible in the case of most explanatory variables, this may not be the case for all of them. Thus, it can be argued that since companies undertake R&D efforts to be able to absorb external knowledge, higher research intensity alone may make companies more prone to source information from outside the company and to cooperate in research with external partners. This implies the existence of a reverse causation from R&D intensity, LRDI, to incoming knowledge spillovers, ISP, and research cooperation, COOP, and that these variables are, therefore, endogenous in the R&D intensity equation. Or this may also be the case if these variables – LRDI, ISP and COOP – are simultaneously determined by a common driving force, say, innovativeness, which means that innovative companies are devoting more effort to R&D, information sourcing and research cooperation than less innovative ones, without there being an actual behavioural relation between these variables. This is implied by the concept of open innovation (Chesbrough, 2003) that starts from the idea that global technological development necessitates external knowledge and thus leads to the adoption of open innovation practices (spillovers, cooperation, appropriation) and/or to policy measures at European level, stressing common research objectives like the Framework programmes envisaging research collaboration to enhance R&D intensity in order to achieve the 3% objective. Furthermore, there may be feedback from the level of innovative sales, LINSAL, to the growth of sales to the level of R&D intensity, LRDI, so
Open innovation practices and innovative performances 11
that this variable may be endogenous in the innovative sales equation. To check this, we performed Durbin-Hausman-Wu (DHW) exogeneity tests on the variables concerned, under the assumption that the other explanatory variables were exogenous (Davidson and MacKinnon, 1993). More precisely, testing for the exogeneity of an explanatory variable in an equation is performed by first performing a regression of this variable on all the exogenous variables that may act as instruments (the first of two least square stages), and then, in a second step, to include the estimated residual term from this as an additional explanatory variable in the equation and to perform a t-test on the hypothesis that its parameter estimate is equal to zero. As seen in the tables below, the results systematically imply exogeneity, so that there is apparently no need for instrumenting. Therefore, estimation will start with OLS considering robust standard errors in order to cope with the possible problems of heteroscedasticity.
After performing estimation on samples of R&D active or R&D active and product innovative companies, these samples are extended and complement the respective equations with corresponding sample selection equations to draw inference about a larger population that also includes non-innovative companies. These sample selection equations are probit equations explaining the probability of doing R&D, or of doing R&D and of being product innovative. They have a binary dependent variable that takes the value 1 in case of positive R&D, or in case of positive R&D and innovative sales, and the value 0. When considered together with the corresponding structural equation, they form a generalised tobit model. Even if, as seems the case, there are no problems of endogeneity, OLS will become inappropriate. This is because of the binary nature of the dependent variable in the probit equation, and because of the possible correlation between its error term and the error term of the corresponding structural equation, which will cause a sample selection bias in the parameter estimates. For each of the equations, the generalised tobit model is estimated by maximum likelihood (ML), while using the Heckman (1979) procedure for choosing the initial parameter values in the iterative procedure. The resulting standard errors are, again, heteroscedastic robust. This method has been shown to provide consistent and efficient estimates (Verbeek, 2000).
Refocusing on the samples of R&D active and of R&D active and product innovative companies, a quantile regression estimation was performed in order to identify systematic differences in behaviour between strong and weak innovative performers. Quantile regression estimates the parameters by minimising the sum of asymmetrically weighted absolute values of the residuals. Thus, for example, the 0.25 regression quantile estimates give less weight to the positive residuals than to the negative ones. Our attention is thereby drawn, so to speak, to the left hand tail of the conditional density function of the dependent variable concerned. For the 0.75 quantile, the reverse is true. Varying the quantile parameter between 0 and 1 generates all the regression quantiles, revealing the conditional distribution of the dependent variable given the explanatory variables. Quantile regression estimates have the additional advantage that they are more efficient than least square estimates when the errors deviate from normality, which is the case.
Since the possibility of heteroscedasticity is not ruled out, it might induce dependence between the quantile regressors and the density of the error terms. Hence, the bootstrap design method is used for estimating the standard errors (Buchinsky, 1995, 1998). We performed bootstrap resampling with 1,000 repetitions. Due to space constraints, the results presented in Tables 3 and 4 below relate to the 0.25, 0.50 (or median), and 0.75 quantiles.
12 A. Spithoven
4 Estimates on average behaviour
Table 1 presents the estimates on the average behaviour of companies. Table 1 concentrates on the R&D intensity equation.
Columns (i) present the country-wise OLS estimates on the samples of R&D active companies. The statistical performance appears appropriate in terms of statistical fit (adjusted R2), keeping in mind the errors of measurement in the data. The DHW tests presented underneath, imply exogeneity of incoming knowledge spillovers, ISP, and research cooperation (COOP), justifying the use of these variables as such rather than in instrumented form.
One of the industry dummies in Table 1 had to be dropped to avoid singularity. The choice fell on the high-tech information-intensive services, PAVINFO, so that its impact was captured by the intercept, C. The estimates of the other industry dummies reflect deviations from this. The results in columns (i) in Table 1 indicate that there is a statistically significant negative impact in the case of PAVSUPP and PAVSCAL. This suggests that general technological characteristics mean that supplier-dominated and scale-intensive industries are, on average, less R&D intensive than the specialised supplier, science-based and information-intensive sectors. This finding is particularly relevant for all three countries and suggests that policy efforts towards the establishment of the European Research Area have indeed led to a degree of conversion in the field of technological development.
The two open innovation practices relating to external knowledge, incoming knowledge spillovers, ISP, and research cooperation, COOP, are highly significant for all countries or innovation systems. Research intensity is higher in companies actively sourcing external information to innovate and engaging in research cooperation. This corroborates the finding of Cassiman and Veugelers (2002, 2006) that incoming knowledge spillovers and research collaboration increase the companies’ knowledge base and that the absorption of knowledge requires further in-house R&D. The confirmation by the DHW test that ISP and COOP are exogenous in the R&D intensity equation suggests that the estimates are not suffering from an endogeneity bias, due to a reverse causation or to a simultaneous determination of these variables and the dependent variable by a common driving force. This seems plausible, since information sourcing and research cooperation are deliberate activities, implying an organisational adaptation of the research process, and should not be assumed to be some automatic by-product of higher in-house research efforts. This finding is in line with the evidence, presented below, that only R&D intensity has a direct impact on innovative sales, but not incoming knowledge spillovers and research cooperation. If the knowledge obtained from information sourcing and research cooperation would be readily applicable, there is no reason why this should be the case.
As to the two remaining open innovation practices, only appropriability is statistically significant for all innovation systems under consideration and this with a positive sign conforming to expectations. The capacity in appropriating the benefits of internally developed knowledge increases the incentives to undertake research efforts. The coefficient of the variable on complexity of knowledge, CK, has, for its part, also the expected positive sign, but is non-significant. A consideration of the correlation matrix does suggest that this may be due to multicollinearity with the ISP variable, which was constructed from the same underlying data, so that not much can be concluded from this. These findings lead us to accept Hypothesis 1.
Open innovation practices and innovative performances 13
The variable on human capital, LHEI, is also clearly significant for each country with the expected positive sign. Except for Belgium, this is also the case for export orientation, LEXI. This confirms the complementarity between formal R&D and employee qualification, and it suggests that the stronger competitive pressure in foreign markets does, indeed, positively affect R&D intensity. The lack of significance of export orientation in the case of Belgium can be explained by the fact that here, most R&D active companies are highly export-oriented. This not only implies a substantially higher mean value of this variable here (as shown in Table A2 in the Appendix), but also a lower variability between companies (lower variance) and, therefore, lower explanatory power in the cross-section.
Finally, as far as the size-related variables are concerned, company size, LSIZE, has a negative impact on R&D intensity, as can group membership, GR (in the case of Germany). This negative significance regarding size would suggest that, among research performers, R&D expenditure rises less in proportion to company size. This might be due to the fact that an important part of technical change nowadays stems from small-scale companies mainly active in either information-intensive, science-based or specialised supplier industries, and thus far less in supplier-dominated and scale-intensive sectors, which were far more dominant before the ICT revolution. These new technology-based companies expect to grow rapidly from their innovations and, within the paradigm of open innovation, they are prepared to sell a proportion of their new ideas, which they do not consider useful to exploit themselves. Consequently, they are not greatly affected in their research decisions by their original size constraints or by their short-term sales performance, and they tend to spend relatively more on R&D in relation to their size than larger, less dynamic companies.
The remaining columns of Table 1 present the maximum likelihood estimates of the corresponding generalised tobit model aimed at drawing statistical inference about a larger population that also includes non-research active companies. Columns (ii) present the sample selection equation. It is a probit equation explaining the probability of doing (reporting) R&D. The choice of its variables was conditioned by data availability on the total sample. Columns (iii) present the corresponding tobit equation explaining the level of R&D intensity, LRDI, providing that the companies do (reporting) R&D. In the case of the probit equation (column ii), the PAVSUPP dummy is found to be statistically significant with a negative sign in two of the three innovation systems, whereas the PAVSPEC and PAVSCIE dummies are statistically significant with a positive sign. This suggests that the probability of doing R&D is somewhat lower than average in the low-tech supplier-dominated industries and higher in the specialised supplier and science-based industries. Appropriability is the main explanatory variable. This confirms that companies relying on strategic protection of their ideas through secrecy, complexity of design or lead-time advantage are more likely to undertake research efforts. Human capital, LHEI, is either statistically significant with a positive sign (in the case of Belgium and Germany) or non-significant (Spain), and export orientation, LEXI, is systematically significant with a positive sign. This suggests that companies with more qualified personnel and subject to stronger competitive pressures are more likely to be R&D active (Belgium and Germany).
14 A. Spithoven
Table 1 Research intensity in Belgium, Germany and Spain – average company behaviour
Belg
ium
Ger
man
y
Spai
n
OLS
M
L M
L
OLS
M
L M
L
OLS
M
L M
L
(i)
(ii)
(iii)
(i)
(ii
) (ii
i)
(i)
(ii)
(iii)
Con
stan
t 0.
606
–0.5
44
0.52
9
1.90
0 –1
.120
1.
084
3.
581
–2.9
77
3.23
1
(0.5
6)
(1.0
7)
(0.5
3)
(3
.43)
**
(3.6
7)**
(1
.86)
(7.0
8)**
(1
4.7)
**
(5.2
2)**
PA
VSU
PP
–1.2
19
–0.1
36
–1.2
26
–0
.420
–0
.226
–0
.539
–0.9
95
–0.5
08
–1.0
40
(4
.83)
**
(1.1
5)
(4.9
5)**
(2.2
9)*
(2.7
6)**
(2
.93)
**
(8
.00)
**
(9.0
1)**
(7
.96)
**
PAV
SCA
L –1
.300
–0
.013
–1
.300
–0.2
90
–0.0
25
–0.2
97
–0
.735
–0
.341
–0
.766
(5.1
0)**
(0
.11)
(5
.20)
**
(1
.99)
* (0
.34)
(1
.99)
*
(6.0
8)**
(6
.18)
**
(6.1
6)**
PA
VSP
EC
–0.1
85
0.36
8 –0
.169
0.14
0 0.
316
0.25
5
–0.1
94
0.17
9 –0
.185
(0.7
0)
(2.7
6)**
(0
.66)
(0.9
4)
(3.5
2)**
(1
.66)
(1.5
4)
(2.6
7)**
(1
.48)
PA
VSC
IE
–0.2
62
0.60
3 –0
.236
0.26
6 0.
540
0.45
3
–0.2
06
0.21
5 –0
.196
(0.9
7)
(3.4
3)**
(0
.90)
(1.3
7)
(3.4
6)**
(2
.25)
*
(1.5
3)
(2.5
3)**
(1
.46)
IS
P 1.
075
1.
073
0.
408
0.
414
0.
438
0.
437
(2
.91)
**
(2
.97)
**
(1
.64)
*
(1.6
8)*
(2
.56)
**
(2
.57)
**
CO
OP
0.43
7
0.43
7
0.37
7
0.36
3
0.59
6
0.59
4
(2.9
8)**
(3.0
3)**
(3.7
5)**
(3.6
3)**
(7.8
4)**
(7.8
5)**
A
PP
0.50
6 1.
548
0.57
4
0.32
6 1.
301
0.86
4
0.22
5 1.
438
0.33
5
(3.0
1)**
(1
1.7)
**
(3.2
7)**
(2.6
3)**
(1
5.3)
**
(5.9
2)**
(2.9
5)**
(2
6.0)
**
(2.7
8)**
Not
es: T
he d
epen
dent
var
iabl
e is
the
log
of re
sear
ch in
tens
ity, L
RD
I, ex
cept
in c
olum
n (ii
) whe
re it
is th
e pr
obab
ility
of d
oing
rese
arch
, PR
D. R
HO
st
ands
for t
he c
orre
latio
n co
effic
ient
bet
wee
n th
e er
rors
of t
he sa
mpl
e se
lect
ion
and
tobi
t equ
atio
ns. T
he a
bsol
ute
valu
e of
the
t-val
ues i
s pr
esen
ted
betw
een
brac
kets
. The
se a
re b
ased
on
hete
rosc
edas
tic c
onsi
sten
t est
imat
es o
f the
stan
dard
err
ors.
The
t-tes
ts a
re o
ne-ta
iled
or
two-
taile
d, a
ccor
ding
to a
prio
ri ex
pect
atio
ns w
ith re
spec
t to
the
para
met
er v
alue
s. Th
e p-
valu
es o
f the
Dur
bin-
Hau
sman
-Wu
test
on th
e ex
ogen
eity
of I
SP a
nd C
OO
P ar
e pr
esen
ted
unde
rnea
th a
s DH
WIS
P an
d D
HW
CO
OP.
The
sym
bols
* a
nd *
* de
note
sign
ifica
nce
at th
e 5%
an
d 1%
leve
ls.
Open innovation practices and innovative performances 15
Table 1 Research intensity in Belgium, Germany and Spain – average company behaviour (continued)
Belg
ium
Ger
man
y
Spai
n
OLS
M
L M
L
OLS
M
L M
L
OLS
M
L M
L
(i)
(ii)
(iii)
(i)
(ii
) (ii
i)
(i)
(ii)
(iii)
CK
0.
429
0.
431
0.
080
0.
075
0.
167
0.
167
(1
.12)
(1.1
6)
(0
.33)
(0.3
2)
(1
.14)
(1.1
4)
LHEI
0.
187
0.04
9 0.
190
0.
241
0.11
2 0.
290
0.
053
–0.0
17
0.04
9
(3.2
8)**
(2
.72)
**
(3.4
7)**
(7.1
1)**
(6
.50)
**
(7.7
9)**
(3.0
3)**
(1
.73)
(2
.69)
**
LEX
I 0.
022
0.07
1 0.
026
0.
128
0.08
0 0.
166
0.
087
0.07
0 0.
093
(0
.55)
(4
.02)
**
(0.6
4)
(5
.54)
**
(6.8
1)**
(6
.80)
**
(5
.29)
**
(9.5
5)**
(5
.55)
**
LSIZ
E –0
.166
0.
049
–0.1
64
–0
.174
0.
152
–0.1
24
–0
.442
0.
120
–0.4
32
(2
.16)
* (1
.63)
(2
.24)
*
(6.9
3)**
(9
.20)
**
(4.8
2)**
(14.
8)**
(9
.27)
**
(13.
8)**
G
R
–0.1
89
0.06
8 –0
.186
–0.3
17
–0.0
64
–0.3
37
0.
137
0.27
5 0.
159
(0
.85)
(0
.75)
(0
.86)
(3.2
2)**
(1
.04)
(3
.37)
**
(1
.73)
(6
.31)
**
(1.9
1)
RH
O
0.05
1
0.47
1
0.09
5
(0.8
8)
(7
.80)
**
(1
.14)
Nb.
obs
. 49
2 1,
223
492
1,
117
2,81
3 1,
117
1,
773
7,82
6 1,
773
Adj
. R2
0.30
2
0.26
2
0.34
4
D
HW
ISP
0.07
7
0.07
8
0.06
0
D
HW
CO
OP
0.09
9
0.54
5
0.05
1
Not
es: T
he d
epen
dent
var
iabl
e is
the
log
of re
sear
ch in
tens
ity, L
RD
I, ex
cept
in c
olum
n (ii
) whe
re it
is th
e pr
obab
ility
of d
oing
rese
arch
, PR
D. R
HO
st
ands
for t
he c
orre
latio
n co
effic
ient
bet
wee
n th
e er
rors
of t
he sa
mpl
e se
lect
ion
and
tobi
t equ
atio
ns. T
he a
bsol
ute
valu
e of
the
t-val
ues i
s pr
esen
ted
betw
een
brac
kets
. The
se a
re b
ased
on
hete
rosc
edas
tic c
onsi
sten
t est
imat
es o
f the
stan
dard
err
ors.
The
t-tes
ts a
re o
ne-ta
iled
or
two-
taile
d, a
ccor
ding
to a
prio
ri ex
pect
atio
ns w
ith re
spec
t to
the
para
met
er v
alue
s. Th
e p-
valu
es o
f the
Dur
bin-
Hau
sman
-Wu
test
on
the
exog
enei
ty o
f ISP
and
CO
OP
are
pres
ente
d un
dern
eath
as D
HW
ISP
and
DH
WC
OO
P. T
he sy
mbo
ls *
and
**
deno
te si
gnifi
canc
e at
the
5%
and
1% le
vels
.
16 A. Spithoven
Table 2 Innovative sales in Belgium, Germany and Spain – average company behaviour
Belg
ium
Ger
man
y
Spai
n
OLS
M
L M
L
OLS
M
L M
L
OLS
M
L M
L
(i)
(ii)
(iii)
(i)
(ii
) (ii
i)
(i)
(ii)
(iii)
Con
stan
t –0
.849
–0
.903
–0
.904
–0.2
14
–1.1
76
–0.4
80
–0
.233
–2
.798
–0
.618
(1.8
0)
(1.9
1)
(1.8
0)
(0
.67)
(4
.40)
**
(1.5
2)
(0
.68)
(1
5.1)
**
(1.6
6)
PAV
SUPP
–0
.688
–0
.334
–0
.698
–0.0
40
–0.2
95
–0.0
84
0.
021
–0.4
68
–0.0
26
(4
.32)
**
(3.1
9)**
(4
.39)
**
(0
.37)
(3
.85)
**
(0.7
7)
(0
.19)
(8
.48)
**
(0.2
3)
PAV
SCA
L –0
.436
–0
.208
–0
.441
0.09
4 –0
.144
0.
073
–0
.048
–0
.358
–0
.085
(2.4
8)**
(2
.01)
* (2
.52)
**
(1
.02)
(2
.16)
* (0
.80)
(0.4
6)
(6.6
2)**
(0
.80)
PA
VSP
EC
–0.1
00
0.09
8 –0
.097
0.13
5 0.
307
0.16
7
0.18
8 0.
186
0.20
0
(0.6
8)
(0.8
9)
(0.6
8)
(1
.57)
(4
.27)
**
(1.9
2)
(1
.85)
(3
.05)
**
(2.0
0)*
PAV
SCIE
–0
.641
0.
401
–0.6
31
–0
.118
0.
387
–0.0
76
–0
.338
0.
171
–0.3
27
(3
.66)
**
(2.8
6)**
(3
.64)
**
(0
.93)
(3
.19)
**
(0.6
0)
(2
.91)
**
(2.2
5)*
(2.8
5)**
LR
DI
0.07
7
0.07
6
0.11
9
0.11
7
0.16
3
0.16
3
(2.8
0)**
(2.8
6)**
(6.3
0)**
(6.2
8)**
(5.9
5)**
(6.0
9)**
IS
P 0.
113
0.
112
0.
019
0.
020
0.
071
0.
071
(0
.43)
(0.4
3)
(0
.13)
(0.1
4)
(0
.44)
(0.4
5)
CO
OP
0.07
6
0.07
6
0.08
5
0.08
2
0.02
4
0.02
1
(0.6
8)
(0
.70)
(1.4
2)
(1
.39)
(0.3
3)
(0
.32)
A
PP
–0.0
33
1.53
7 0.
009
0.
063
1.24
7 0.
222
0.
060
1.42
4 0.
192
(0
.24)
(1
6.6)
* (0
.04)
(0.8
1)
(19.
9)**
(2
.61)
**
(0
.92)
(3
3.8)
**
(2.3
9)**
Not
es: T
he d
epen
dent
var
iabl
e is
the
log
of in
nova
tive
sale
s, LI
NSA
L, e
xcep
t in
colu
mn
(ii) w
here
it is
the
prob
abili
ty o
f doi
ng re
sear
ch a
nd o
f be
ing
prod
uct i
nnov
ativ
e, P
RD
INSA
L. R
HO
stan
ds fo
r the
cor
rela
tion
coef
ficie
nt b
etw
een
the
erro
rs o
f the
sam
ple
sele
ctio
n an
d to
bit
equa
tions
. The
abs
olut
e va
lue
of th
e t-v
alue
s is p
rese
nted
bet
wee
n br
acke
ts. T
hese
are
bas
ed o
n he
tero
sced
astic
con
sist
ent e
stim
ates
of t
he
stan
dard
err
ors.
The
t-tes
ts a
re o
ne-ta
iled
or tw
o-ta
iled,
acc
ordi
ng to
a p
riori
expe
ctat
ions
with
resp
ect t
o th
e pa
ram
eter
val
ues.
The
p-va
lue
of th
e D
urbi
n-H
ausm
an-W
u te
st o
n th
e ex
ogen
eity
of L
RD
I is p
rese
nted
und
erne
ath
as D
HW
LRD
I. Th
e sy
mbo
ls *
and
**
deno
te
sign
ifica
nce
at th
e 5%
and
1%
leve
ls.
Open innovation practices and innovative performances 17
Table 2 Innovative sales in Belgium, Germany and Spain – average company behaviour (continued)
Belg
ium
Ger
man
y
Spai
n
OLS
M
L M
L
OLS
M
L M
L
OLS
M
L M
L
(i)
(ii)
(iii)
(i)
(ii
) (ii
i)
(i)
(ii)
(iii)
CK
–0
.133
–0.1
32
0.
088
0.
088
–0
.128
–0.1
29
(0
.55)
(0.5
6)
(0
.53)
(0.5
4)
(0
.87)
(0.9
0)
PCS
0.31
2
0.31
2
0.24
1
0.24
1
0.16
8
0.16
7
(2.9
9)**
(3.0
5)**
(3.9
5)**
(3.9
7)**
(2.6
8)**
(2.6
8)**
LH
EI
–0.0
23
0.06
8 –0
.021
0.03
1 0.
123
0.04
9
0.01
3 –0
.016
0.
009
(0
.97)
(3
.37)
**
(0.7
8)
(1
.52)
(6
.35)
**
(2.2
4)**
(0.9
0)
(1.7
4)
(0.6
4)
LEX
I –0
.002
0.
060
–0.0
04
0.
003
0.08
2 0.
015
0.
012
0.07
3 0.
019
(0
.11)
(3
.76)
**
(0.0
2)
(0
.24)
(8
.18)
**
(1.1
2)
(0
.93)
(1
0.6)
**
(1.5
2)
LSIZ
E –0
.060
0.
080
–0.0
58
–0
.032
0.
156
–0.0
14
–0
.030
0.
102
–0.0
20
(2
.00)
* (2
.98)
**
(1.9
6)*
(2
.53)
**
(11.
8)**
(1
.03)
(1.3
3)
(8.7
0)**
(0
.91)
G
R
0.04
7 –0
.008
0.
047
–0
.208
–0
.098
–0
.220
–0.0
93
0.24
3 –0
.070
(0.4
1)
(0.1
1)
(0.4
1)
(3
.43)
**
(1.9
1)
(3.6
3)**
(1.3
8)
(5.9
9)**
(1
.05)
R
HO
0.
048
0.
247
0.
138
(0
.25)
(3.5
9)**
(2.4
2)**
Nb.
obs
. 39
0 1,
223
390
93
1 2,
813
931
1,
367
7,82
6 1,
367
Adj
. R2
0.12
7
0.11
9
0.10
0
D
HW
LRD
I 0.
281
0.
152
0.
703
Not
es: T
he d
epen
dent
var
iabl
e is
the
log
of in
nova
tive
sale
s, LI
NSA
L, e
xcep
t in
colu
mn
(ii) w
here
it is
the
prob
abili
ty o
f doi
ng re
sear
ch a
nd o
f be
ing
prod
uct i
nnov
ativ
e, P
RD
INSA
L. R
HO
stan
ds fo
r the
cor
rela
tion
coef
ficie
nt b
etw
een
the
erro
rs o
f the
sam
ple
sele
ctio
n an
d to
bit
equa
tions
. The
abs
olut
e va
lue
of th
e t-v
alue
s is p
rese
nted
bet
wee
n br
acke
ts. T
hese
are
bas
ed o
n he
tero
sced
astic
con
sist
ent e
stim
ates
of t
he
stan
dard
err
ors.
The
t-tes
ts a
re o
ne-ta
iled
or tw
o-ta
iled,
acc
ordi
ng to
a p
riori
expe
ctat
ions
with
resp
ect t
o th
e pa
ram
eter
val
ues.
The
p-va
lue
of th
e D
urbi
n-H
ausm
an-W
u te
st o
n th
e ex
ogen
eity
of L
RD
I is p
rese
nted
und
erne
ath
as D
HW
LRD
I. Th
e sy
mbo
ls *
and
**
deno
te
sign
ifica
nce
at th
e 5%
and
1%
leve
ls.
18 A. Spithoven
Employment size, LSIZE, has a positive sign and it is statistically significant in two of the three countries (Germany and Spain). The other size-related variable, group membership, GR, also has, to the extent that it is significant, a positive sign (in the case of Spain). This confirms Stylised Fact 1 of Cohen and Klepper (1996), which states that the likelihood of a company reporting research efforts rises with company size. When considered in conjunction with our earlier findings, this suggests that there is a size threshold level necessary for doing formal research. But, once past this level, further size increase does not lead to a proportional increase in research expenditure for the reasons given above.
The estimated correlation coefficient between the errors of the probit and tobit equations, RHO, is presented at the bottom of columns (iii). It is only statistically significant in the case of Germany. This suggests that there is no evidence of a sample selection effect in the case of the other countries and this is confirmed by the parameter estimates of the tobit equation presented above, which are not very different from the corresponding OLS estimates in columns (i). In the case of Germany, there is a sample selection effect, which is mainly seen in an increase of the parameter estimate of APP, capturing the combined effect of the impact of the variable concerned on the probability of being R&D active on the one hand, and of its impact on the level of R&D intensity, once this is the case, on the other. But one should remain cautious with the interpretation since the relative impact of the other open innovation practices, which could not be included in the probit equation due to data constraints, is likely to be underestimated.
Table 2 presents the impact on innovative sales. Columns (i) present the country-wise estimates by OLS on the samples of R&D
active and product innovative company. The DHW test presented underneath implies the exogeneity of the level of R&D intensity, LRDI, in the innovative sales equation, justifying the use of the variable as such rather than in an instrumented form.
First let us consider the industry dummies: to the extent that they are statistically significant, the parameter estimates are negative. This is the case of PAVSUPP and PAVSCAL for Belgium, and PAVSCIE for Belgium and Spain. This suggests that, when controlling the impact of R&D intensity, the innovative output is lower in the industries concerned than in the information-intensive industries. The evidence that this is predominantly the case in the knowledge intensive science-based industries is somewhat surprising since these industries are also known as dynamic and high-tech oriented. The explanation is probably that the development of new products in industries such as biotech and pharmaceuticals is characterised by long and variable gestation periods (Bruneel et al., 2007).
The level of R&D intensity, LRDI, is, highly significant statistically for all the countries under consideration, indicating that it is, as expected, a key variable explaining innovative sales. The confirmation by the DHW test that LRDI is exogenous in the innovative sales equation suggests, at the same time, that the estimates are not suffering from an endogeneity bias, due to a feedback from the level of innovative sales, LINSAL, to the growth of sales to the level of R&D intensity, LRDI. Further confirmation is sought by experimenting with an extended model that also includes an equation explaining the growth of sales (between 1998 and 2000) and by including the latter variable as an additional explanatory variable in the R&D intensity equation. As is the case in earlier studies with respect to Belgium (Spithoven et al., 2010a), the results indicate that while the level of innovative sales has a significant impact on the growth of
Open innovation practices and innovative performances 19
sales, the latter, however, has no impact on the level of R&D intensity. This suggests that the feedback loop breaks down here and that the model is recursive in nature.
There is also evidence of a positive impact of process innovations, PCS, on the level of innovative sales, in all countries. This confirms that production reorganisations that do not require formal research efforts also play their part in generating product innovations.
There is no evidence of a direct effect of the open innovation practices, or of human capital on product innovativeness, when controlling R&D intensity. This clearly suggests that their influence on the level of product innovation is an indirect one, running through R&D intensity. Incoming spillovers, research cooperation or the ideas of workers do not appear to generate readily usable knowledge: they first need to be absorbed in the company’s knowledge base through additional R&D efforts (Cohen and Levinthal, 1989; Chesbrough and Cowther, 2006). This further strengthens the behavioural interpretation of the statistical significance of these variables in the R&D intensity equation. Also for this variable, the impact is mainly an indirect one, through R&D intensity, further stressing the importance of in-house R&D efforts. Hence, the results point towards an acceptance of Hypothesis 3.
As far as the size-related variables are concerned, the impact of company size, LSIZE, is systematically negative, which is significantly the case in Belgium and Germany. As regards Germany, there is a negative impact of group membership, GR. These results would imply a decrease in efficiency in the innovation process according to company size. This can be explained by the fact that among the R&D active product innovators, the relatively small new technology-based companies tend to be more dynamic. They are not only prepared to spend relatively more on R&D in relation to their size, but also to do so in a more risky manner. Consequently, they are able to generate, on average, a higher number of innovations per unit of R&D spending [see Cohen (1995), for a review of arguments in this sense]. It should be noted that the innovation survey (CIS3) contains no information on the age of companies, therefore, this topic needs further investigation.
Finally, columns (ii) and (iii) of Table 2 present the maximum likelihood, ML, estimates of the corresponding generalised tobit model. In this case, the sample selection equation in columns (ii) is a probit equation explaining the probability of doing R&D and of being product innovative. Its estimates are quite similar to those of the probit equation explaining the probability of doing R&D in columns (ii) of Table 1. The estimated correlation coefficient between the errors of the probit and tobit equations, RHO, presented at the bottom of columns (iii) of Table 2 is now non-significant in the case of Belgium, implying that there is no sample selection effect here. This is confirmed by the parameter estimates of the tobit equation above, which are nearly identical to those obtained by OLS in the corresponding columns (i). In the case of Germany and Spain, there is, however, a modest sample selection effect, which translates into an increase of the impact of appropriability, APP, which becomes significant in the tobit equation estimates in columns (iii), while it is non-significant in the corresponding OLS estimates in columns (i). This reflects that we are now picking up the combined effect of the impact of this variable on the probability of being R&D active and product innovative on the one hand, and of its direct impact on the level of innovative sales, once this is the case, on the other. But, again, one should remain cautious with the interpretation in view of the likely under-specification of the probit equation due to data constraints.
20 A. Spithoven
All in all, when looking at the average effects of the various determinants of R&D intensity and innovative sales, there is an apparent similarity in open innovation practices across countries. But does this mean that these practices are a global phenomenon, regardless of which country the company is located in?
5 Estimates in quantile behaviour
Quantile regression accounts for possible large variations in R&D intensity and innovative sales across companies (Coad and Rao, 2008; Spithoven et al., 2010a). These variations might differ across innovation systems. Can the ‘average’ effects identified in the preceding section be generalised for all types of companies? Or are there marked differences in behaviour between strong and weak innovators? We used quantile regression to answer this question. Table 3 presents the results for the determinants of R&D intensity.
The first striking element is that the negative impact of the PAVSUPP and PAVSCAL dummies, observed in the average estimates in Table 1, tends to become more explicit quantile-wise. This suggests that it is not only true that the level of R&D intensity is, on average, higher in the information-intensive, specialised supplier and science-based industries than in the more traditional supplier-dominated and scale-intensive sectors, but that this is especially the case among the strong R&D performers in the upper quantiles. Or, put differently, the most dynamic R&D investors are found especially in sectors with a technology-intensive trajectory. A comparison with the findings in Table 1 shows that quantile regression reveals, to some extent, the technical specialisation of each country, which was largely obscured by concentrating on average behaviour. Quantile regression indicates that the policy objective to create a European Research Area still leaves ample room for country-specific actions.
As suggested by the average estimates of the open innovation practices, only incoming knowledge spillovers, ISP, research cooperation, COOP, and appropriability, APP, are statistically significant in at least one of the quantiles, whereas complexity of knowledge, CK, is systematically non-significant. In all countries, the joint relative impact of incoming knowledge spillovers, ISP, and research cooperation, COOP, tends to increase quantile-wise in relation to that of appropriability, APP. But there are marked differences across innovation systems. In the case of Belgium, this mainly occurs through a quantile-wise decrease of the impact of appropriability, APP, although the impact of incoming knowledge spillovers also decreases in the higher quantiles, while the effects of research cooperation remain stable over the quantiles. In the case of Germany, it does so through a quantile-wise increase in the impact of research cooperation, COOP. Whereas in the case of Spain, it takes place both through a quantile-wise increase in the impact of incoming knowledge spillovers, ISP, and a slight decrease in appropriability, APP. Hence, Hypothesis 2 is accepted.
Open innovation practices and innovative performances 21
Table 3 Research intensity in Belgium, Germany and Spain – quantile company behaviour
Belg
ium
G
erm
any
Sp
ain
0.25
0.
50
0.75
0.25
0.
50
0.75
0.25
0.
50
0.75
Con
stan
t 1.
662
2.02
8 0.
618
2.
145
1.85
6 1.
569
2.
089
3.57
6 3.
851
(1
.56)
(1
.68)
(0
.61)
(2.2
0)*
(2.1
3)*
(3.1
3)**
(4.4
9)**
(7
.27)
**
(7.9
3)**
PA
VSU
PP
–0.9
78
–1.3
76
–1.6
23
–0
.310
–0
.404
–0
.606
–0.8
09
–1.0
40
–1.2
59
(2
.64)
**
(4.1
8)**
(5
.26)
**
(1
.12)
(1
.85)
(2
.79)
**
(5
.35)
**
(7.1
6)**
(6
.80)
**
PAV
SCA
L –1
.033
–1
.526
–1
.636
–0.3
83
–0.1
86
–0.3
83
–0
.453
–0
.822
–1
.121
(3.3
7)**
(4
.19)
**
(6.4
7)**
(1.3
5)
(0.9
9)
(2.7
2)**
(3.0
4)**
(6
.08)
**
(6.1
2)**
PA
VSP
EC
0.00
5 0.
011
–0.3
03
0.
252
0.32
4 0.
067
0.
187
–0.2
61
–0.4
76
(0
.01)
(0
.04)
(1
.46)
(1.0
4)
(1.9
2)
(0.4
1)
(1
.15)
(1
.58)
(2
.68)
**
PAV
SCIE
–0
.131
–0
.523
–0
.938
0.20
4 0.
021
–0.0
32
0.
095
–0.2
21
–0.5
94
(0
.32)
(1
.70)
(2
.64)
**
(0
.72)
0.
08
(0.1
4)
(0
.55)
(1
.48)
(3
.01)
**
ISP
1.63
9 0.
860
0.80
6
0.34
2 0.
574
0.29
7
0.25
3 0.
429
0.36
9
(3.2
6)**
(1
.97)
**
(2.0
7)**
(0.8
7)
(1.9
1)*
(1.0
0)
(1
.21)
(2
.11)
**
(1.9
7)**
C
OO
P 0.
626
0.63
5 0.
563
0.
250
0.33
1 0.
434
0.
553
0.63
5 0.
554
(2
.91)
**
(3.6
5)**
(3
.37)
**
(1
.52)
(2
.51)
**
(3.9
5)**
(6.7
1)**
(5
.53)
**
(5.6
4)**
Not
es: T
he d
epen
dent
var
iabl
e is
the
log
of re
sear
ch in
tens
ity, L
RD
I. Th
e t-v
alue
s are
bas
ed o
n st
anda
rd e
rror
s obt
aine
d by
the
boot
stra
p de
sign
m
atrix
met
hod.
Boo
tstra
p re
-sam
plin
g w
as p
erfo
rmed
with
1,0
00 re
petit
ions
. The
t-te
sts a
re o
ne-ta
iled
or tw
o-ta
iled,
acc
ordi
ng to
a p
riori
expe
ctat
ions
with
resp
ect t
o th
e pa
ram
eter
val
ues.
The
pseu
do R
2 is o
btai
ned
as o
ne m
inus
the
prop
ortio
n of
the
sum
of a
bsol
ute
wei
ghte
d de
viat
ions
ove
r the
raw
sum
of d
evia
tions
. The
sym
bols
* a
nd *
* de
note
sign
ifica
nce
at th
e 5%
and
1%
leve
ls.
22 A. Spithoven
Table 3 Research intensity in Belgium, Germany and Spain – quantile company behaviour (continued)
Belg
ium
Ger
man
y
Spai
n
0.25
0.
50
0.75
0.25
0.
50
0.75
0.25
0.
50
0.75
APP
0.
600
0.46
3 0.
231
0.
419
0.34
4 0.
406
0.
252
0.19
5 0.
205
(2
.47)
**
(1.9
6)**
(1
.22)
(2.2
4)**
(1
.96)
**
(3.1
8)**
(3.1
0)**
(1
.97)
**
(2.0
6)**
C
K
–0.2
08
0.39
4 0.
526
0.
168
–0.1
02
0.29
7
0.25
7 0.
071
0.13
3
(0.3
9)
(1.1
7)
(1.4
3)
(0
.41)
(0
.31)
(1
.06)
(1.3
8)
(0.3
7)
(0.8
7)
LHEI
0.
291
0.20
1 0.
126
0.
318
0.28
6 0.
200
0.
026
0.02
3 0.
076
(3
.02)
**
(3.0
7)**
(2
.70)
**
(3
.52)
**
(4.0
1)**
(6
.18)
**
(1
.59)
(1
.25)
(3
.55)
**
LEX
I 0.
022
0.03
0 0.
012
0.
097
0.10
1 0.
126
0.
084
0.07
7 0.
055
(0
.46
(0.7
5)
(0.3
8)
(2
.97)
**
(3.9
8)**
(5
.03)
**
(4
.66)
**
(4.7
7)**
(3
.34)
**
LSIZ
E –0
.212
–0
.226
–0
.145
–0.1
88
–0.1
37
–0.1
27
–0
.434
–0
.459
–0
.372
(2.8
2)**
(3
.20)
**
(2.1
3)**
(3.8
2)**
(4
.41)
**
(4.5
6)**
(16.
0)**
(1
6.1)
**
(13.
4)**
G
R
–0.2
78
–0.1
65
0.00
9
–0.2
33
–0.2
84
–0.4
28
0.
161
0.10
0 0.
088
(1
.21)
(0
.67)
(0
.05)
(1.6
1)
(2.3
6)**
(3
.60)
**
(1
.57)
(1
.00)
(1
.03)
Nb.
obs
. 49
2 49
2 49
2
1,11
7 1,
117
1,11
7
1,77
3 1,
773
1,77
3 Ps
d. R
2 0.
201
0.22
5 0.
242
0.
150
0.15
6 0.
166
0.
184
0.19
7 0.
225
Not
es: T
he d
epen
dent
var
iabl
e is
the
log
of re
sear
ch in
tens
ity, L
RD
I. Th
e t-v
alue
s are
bas
ed o
n st
anda
rd e
rror
s obt
aine
d by
the
boot
stra
p de
sign
m
atrix
met
hod.
Boo
tstra
p re
-sam
plin
g w
as p
erfo
rmed
with
1,0
00 re
petit
ions
. The
t-te
sts a
re o
ne-ta
iled
or tw
o-ta
iled,
acc
ordi
ng to
a p
riori
expe
ctat
ions
with
resp
ect t
o th
e pa
ram
eter
val
ues.
The
pseu
do R
2 is o
btai
ned
as o
ne m
inus
the
prop
ortio
n of
the
sum
of a
bsol
ute
wei
ghte
d de
viat
ions
ove
r the
raw
sum
of d
evia
tions
. The
sym
bols
* a
nd *
* de
note
sign
ifica
nce
at th
e 5%
and
1%
leve
ls.
Open innovation practices and innovative performances 23
In order to check the robustness of these findings, we replaced the level of in-house R&D intensity by the level of total R&D intensity (which covers, besides in-house R&D, expenditures on the acquisition of advanced machinery, purchase of the right to use patents, licenses and trademarks, training, and marketing of new products) and repeated estimation on extended samples of total R&D active companies. Although not presented due to space constraints, the results confirmed the finding of a quantile-wise increase in the joint relative impact of incoming knowledge spillovers and research cooperation in relation to appropriability; this was the case in all the countries under consideration. Thus, it seems safe to conclude that, although there are differences between countries regarding the specific manner in which this occurs, the evidence of a quantile-wise increase in the joint impact of incoming knowledge spillovers and research cooperation in relation to appropriability is quite robust. It confirms a priori expectations that the nature of the innovation process tends to become more open among the most dynamic companies.
The parameter estimates of the remaining variables by and large confirm the average findings in Table 1, without showing any specific quantile-wise pattern. Thus, the presence of qualified labour in a company, LHEI, positively affects R&D intensity in a highly significant manner in all quantiles in all countries, except in Spain where it only does so in the highest one. This reflects a general higher qualification of the labour force in Belgium and Germany than in Spain. The impact of export orientation, LEXI, is highly significant in all quantiles in the three countries, where it has a strong average impact in Germany and Spain, and is systematically non-significant in the case of Belgium for the reasons explained above. Finally, the negative impact of company size, LSIZE, is confirmed quantile-wise, as is its re-enforcement by a negative group membership effect, GR, in the case of Germany.
Table 4 presents country-wise quantile estimates of the determinants of innovative sales on the samples of R&D active and product innovative companies.
As in the case of the average estimates in Table 2, as far as the industry dummies are concerned, there is evidence of a negative impact of PAVSUPP and of PAVSCAL on Belgium. This negative influence is clearly evident in the lower quantiles, suggesting that especially among weak innovators, companies active in sectors with a less technology-intensive trajectory perform poorly in the countries concerned. As with the average estimates, there is also evidence of a negative impact of PAVSCIE in the case of Belgium and Spain but here, there is no clear quantile-wise pattern.
As in the case of the average estimates, the key explanatory variable is the level of R&D intensity, LRDI, which is found to be statistically significant in all countries and in all but one quantile. Its impact is seen to increase quantile-wise in Belgium: the impact of R&D intensity on sales for strong innovators in the higher quantiles is broader and more significant than in the case of weak innovators in the lower quantiles. The reverse is true for innovators in Spain. In Germany, the impact of R&D intensity on innovative sales is relatively stable over the quantiles. This suggests that, if anything, strong output innovators are apparently able to organise their R&D processes more efficiently and, thereby, achieve higher returns on their R&D investments. Therefore, Hypothesis 4 can be accepted.
The evidence of a significant influence of process innovation, PCS, in the average estimates in all countries (Belgium, Germany and Spain) is confirmed in these countries in every quantile (except in the highest one in Spain).
24 A. Spithoven
Table 4 Innovative sales in Belgium, Germany and Spain – quantile company behaviour
Belg
ium
G
erm
any
Spai
n
0.25
0.
50
0.75
0.25
0.
50
0.75
0.25
0.
50
0.75
Con
stan
t –1
.490
–0
.526
1.
009
–0
.725
–0
.076
0.
290
–0
.577
–0
.009
0.
297
(1
.98)
* (0
.98)
(1
.89)
(1.4
4)
(0.2
1)
(0.5
1)
(1
.18)
(0
.02)
(0
.73)
PA
VSU
PP
–0.6
68
–0.6
51
–0.5
85
–0
.059
–0
.003
–0
.033
0.03
8 –0
.049
–0
.108
(3.0
2)**
(3
.59)
**
(2.4
6)**
(0.3
8)
(0.0
2)
(0.1
8)
(0
.32)
(0
.33)
(0
.78)
PA
VSC
AL
–0.6
40
–0.4
13
–0.1
45
0.
113
0.14
3 0.
061
–0
.031
–0
.072
–0
.139
(2.0
3)*
(1.9
6)*
(0.5
9)
(0
.74)
(1
.31)
(0
.40)
(0.2
2)
(0.5
3)
(1.0
9)
PAV
SPEC
–0
.026
–0
.058
–0
.107
0.13
8 0.
137
0.10
3
0.32
1 0.
172
0.00
1
(0.0
9)
(0.3
5)
(0.4
7)
(0
.91)
(1
.34)
(0
.78)
(2.4
6)**
(1
.24)
(0
.01)
PA
VSC
IE
–0.8
03
–0.3
83
–0.4
23
–0
.128
–0
.197
–0
.246
–0.2
46
–0.3
69
–0.4
86
(2
.31)
* (1
.94)
(1
.84)
(0.6
7)
(1.1
4)
(1.2
5)
(1
.53)
(2
.42)
**
(3.5
0)**
LR
DI
0.06
4 0.
086
0.11
4
0.11
9 0.
092
0.12
9
0.21
1 0.
192
0.14
0
(1.6
4)*
(2.3
6)**
(2
.54)
**
(4
.02)
**
(3.4
8)**
(4
.08)
**
(5
.20)
**
(6.1
1)**
(3
.98)
**
ISP
–0.1
73
–0.0
15
0.06
9
0.11
0 –0
.028
0.
050
0.
230
0.24
0 –0
.275
(0.4
4)
(0.0
5)
(0.2
0)
(0
.42)
(0
.15)
(0
.22)
(1.2
4)
(1.1
2)
(1.3
2)
CO
OP
0.11
8 0.
088
0.05
4
0.09
9 0.
087
0.18
4
0.01
3 0.
041
–0.0
36
(0
.68)
(0
.74)
(0
.38)
(1.0
7)
(1.1
4)
(1.9
2)*
(0
.14)
(0
.51)
(0
.41)
Not
es: T
he d
epen
dent
var
iabl
e is
the
log
of in
nova
tive
sale
s, LI
NSA
L. T
he t-
valu
es a
re b
ased
on
stan
dard
erro
rs o
btai
ned
by th
e bo
otst
rap
desi
gn
mat
rix m
etho
d. B
oots
trap
re-s
ampl
ing
was
per
form
ed w
ith 1
,000
repe
titio
ns. T
he t-
test
s are
one
-taile
d or
two-
taile
d, a
ccor
ding
to a
prio
ri ex
pect
atio
ns w
ith re
spec
t to
the
para
met
er v
alue
s. Th
e ps
eudo
R2 is
obt
aine
d as
one
min
us th
e pr
opor
tion
of th
e su
m o
f abs
olut
e w
eigh
ted
devi
atio
ns o
ver t
he ra
w su
m o
f dev
iatio
ns. T
he sy
mbo
ls *
and
**
deno
te si
gnifi
canc
e at
the
5% a
nd 1
% le
vels
.
Open innovation practices and innovative performances 25
Table 4 Innovative sales in Belgium, Germany and Spain – quantile company behaviour (continued)
Belg
ium
Ger
man
y
Spai
n
0.25
0.
50
0.75
0.25
0.
50
0.75
0.25
0.
50
0.75
APP
–0
.039
0.
002
–0.2
21
0.
006
0.08
9 0.
065
0.
172
0.06
7 –0
.060
(0.1
7)
(0.0
1)
(1.0
5)
(0
.06)
(0
.88)
(0
.56)
(1.9
7)**
(0
.86)
(0
.71)
C
K
–0.0
10
–0.0
39
–0.1
53
0.
016
0.08
0 –0
.010
–0.2
24
–0.2
08
–0.1
22
(0
.03)
(0
.12)
(0
.55)
(0.0
6)
(0.4
0)
(0.0
4)
(1
.06)
(0
.97)
(0
.63)
PC
S 0.
328
0.23
4 0.
374
0.
281
0.23
2 0.
245
0.
220
0.20
2 0.
067
(1
.96)
**
(1.6
8)*
(2.6
3)**
(2.8
8)**
(3
.24)
**
(2.6
6)**
(2.7
3)**
(2
.43)
**
(0.9
0)
LHEI
–0
.030
–0
.035
–0
.007
0.02
3 0.
327
0.05
3
0.01
9 0.
011
0.00
9
(0.9
4)
(0.9
3)
(0.1
7)
(0
.77)
(1
.42)
(1
.54)
(0.9
6)
(0.6
7)
(0.3
6)
LEX
I –0
.012
0.
019
0.02
0
0.00
6 0.
017
0.00
2
0.03
0 0.
016
0.00
8
(0.4
3)
(0.8
3)
(0.6
6)
(0
.27)
(1
.01)
(0
.14)
(1.7
2)*
(0.9
3)
(0.4
9)
LSIZ
E –0
.057
–0
.083
–0
.112
–0.0
40
–0.0
43
–0.0
05
–0
.032
–0
.039
0.
001
(1
.27)
(2
.46)
**
(2.7
8)**
(2.2
1)*
(2.6
6)**
(0
.22)
(0.9
4)
(1.1
4)
(0.0
3)
GR
–0
.062
0.
067
0.10
7
–0.1
75
–0.1
90
–0.2
69
–0
.167
–0
.028
–0
.151
(0.3
9)
(0.5
3)
(0.6
3)
(2
.04)
* (2
.55)
**
(2.8
2)**
(1.9
3)
(0.3
0)
(1.6
9)
Nb.
obs
. 39
0 39
0 39
0
931
931
931
1,
367
1,36
7 1,
367
Psd.
R2
0.10
0 0.
113
0.10
6
0.08
7 0.
073
0.07
1
0.07
5 0.
075
0.05
8
Not
es: T
he d
epen
dent
var
iabl
e is
the
log
of in
nova
tive
sale
s, LI
NSA
L. T
he t-
valu
es a
re b
ased
on
stan
dard
erro
rs o
btai
ned
by th
e bo
otst
rap
desi
gn
mat
rix m
etho
d. B
oots
trap
re-s
ampl
ing
was
per
form
ed w
ith 1
,000
repe
titio
ns. T
he t-
test
s are
one
-taile
d or
two-
taile
d, a
ccor
ding
to a
prio
ri ex
pect
atio
ns w
ith re
spec
t to
the
para
met
er v
alue
s. Th
e ps
eudo
R2 is
obt
aine
d as
one
min
us th
e pr
opor
tion
of th
e su
m o
f abs
olut
e w
eigh
ted
devi
atio
ns o
ver t
he ra
w su
m o
f dev
iatio
ns. T
he sy
mbo
ls *
and
**
deno
te si
gnifi
canc
e at
the
5% a
nd 1
% le
vels
.
26 A. Spithoven
None of the open innovation practices, ISP, COOP, APP or CK, or worker qualifications, LHEI, seem to have a direct impact on innovative output. Rather, as is shown in the average estimates in Table 2, their influence on innovative output runs indirectly, through R&D intensity. Spain is the exception because it shows a significant impact of appropriability, APP, in the lower quantile. This shows that innovators in low quantiles are inclined to protect their innovations against imitation. Also, the less qualified labour force explains the non-significance of process innovation in the case of high quantiles. The labour costs are relatively low and there is no direct need for investing in labour-saving techniques.
Finally, as for the average estimates, there is evidence of a negative impact regarding company size, LSIZE, in two of the three countries (Belgium and Germany), which can be seen in most quantiles (the only exceptions being the lowest one in Belgium and the highest one in Germany). The negative impact of company size is also re-enforced by a negative influence of group membership, GR, in the case of Germany, which can be seen in all quantiles.
6 Conclusions
When looking at the countries under consideration they show particular characteristics in their innovation systems (Pro Inno Europe, 2012). Belgium excels in human resources (LHEI), excellent and attractive open research systems (international publications cited) and organisational linkages between actors. Germany has strong company investments in R&D, scores well on intellectual assets, ranks high on innovation outputs and has beneficial economic effects in terms of high-tech employment, exports, license revenue and innovative sales. Spain, as a moderate innovator country, has different scores for these indicators. These country-wise differences are linked to their idiosyncratic innovation systems. But since innovation systems do not focus on company-level issues, and when reading about open innovation, one might be tempted to believe that – at company level at least – there is not much difference between company open innovation practices in the different countries. Our research question tackled this particular issue.
All innovation systems consider systemic flows between the nodes of the system, but company organisation and interaction with external knowledge outside company boundaries and their boundary-spanning activities remain largely a black box issue. It is acknowledged that innovation systems are generally heterogeneous across countries, although some of the technological innovation systems are cross-border phenomena (Hekkert et al., 2007; Bergek et al., 2008). Are the key open innovation practices also cross-border phenomena, or are they also heterogeneously dispersed? The answer is not straightforward because a lot of general guidelines for companies are similar for all: companies have profit motives in all countries; the aim of innovation is to raise company competitiveness, etc. Our research first looked at the average behaviour of companies where their performances in terms of R&D intensity and innovative sales are explained as being the result of open innovation practices. In the case of R&D intensity, it was shown that the effect of incoming knowledge spillovers, research cooperation and appropriability are similar for all countries. Only companies in Germany showed a lower significance of impact regarding incoming knowledge spillovers. The impact of competitive pressure on R&D intensity for Belgium was absent which corroborated its relatively weak importance on export in the Innovation Union Scoreboard (Pro Inno
Open innovation practices and innovative performances 27
Europe, 2012). Then again, Belgium is shown to have significant knowledge-intensive services.
A model explaining R&D intensity and innovative sales was estimated according to harmonised company-level data from the Third Community Innovation Survey for Belgium, Germany and Spain. An estimation was initially carried out to gain insight into the average behaviour of companies. The results show a broad similarity between the innovation systems. They indicate that the general technological environment is of importance: companies active in high-technology industries, such as information-intensive, specialised supplier and science-based sectors, show, on average, a higher level of R&D intensity. Although there are differences in their relative impact between innovation systems, open innovation practices, such as incoming knowledge spillovers, research cooperation and appropriability, exercise a major influence on R&D investment. This confirms that the absorption of external knowledge requires substantial further research efforts by companies and that the ability to appropriate in-house knowledge with respect to non-partners increases the incentives to undertake R&D. Higher qualification of workers and stronger competitive pressures, measured by export orientation, also positively affect R&D efforts. These conclusions were not altered in any way by taking into account a sample selection effect.
The actual level of R&D intensity is the main variable explaining innovative sales. Process innovations are also important. The other open innovation practices and human capital related variables are all non-significant in the equation on innovative sales, suggesting that their influence on product innovation is an indirect one, running through R&D intensity. This emphasises the necessity of knowledge absorption, before it can become usable in the innovation process.
Although company size positively affects the probability of reporting R&D, once one passes the threshold necessary to report research, the influence of company size on R&D intensity becomes negative and consequently has a negative influence on innovative sales. This reveals that among the R&D active companies in the countries under consideration, there are a substantial number of young dynamic technology-based companies pursuing ambitious innovation strategies.
Quantile regression shows that the average behaviour of companies hides substantial differences between strong and weak innovators. It also shows that these patterns vary across the innovation systems under consideration.
Companies active in information-intensive, specialised supplier and science-based sectors are over-represented among strong R&D investors. Although there are differences in the precise manner in which this occurs, the joint relative impact on R&D intensity of incoming knowledge spillovers and research cooperation tends to increase quantile-wise in relation to that of appropriability. Furthermore, if anything, the impact of R&D intensity on innovative sales also tends to increase.
When considered in conjunction, these results appear to confirm the assumption of an increasing openness of the innovation process, especially among strong innovators. The internalisation of external knowledge generated through incoming knowledge spillovers and research cooperation has clearly become a major goal of the research activities of innovative companies. Although appropriability does, on average, continue to form an incentive for undertaking research, this is apparently relatively less important in the case of the strongest R&D performers. This would suggest that it is here that one can find most companies prepared to market excess knowledge in a disembodied form. The
28 A. Spithoven
greater impact of R&D intensity on innovative sales among strong product innovators would tend to confirm that these have the most efficient R&D processes.
A final conclusion relates to the justification of the Scoreboard classification. Can companies in Germany indeed be seen as exemplifying innovation leaders? Is the characterisation of companies in Belgium as innovation followers justified? Do companies in Spain all belong to moderate innovators? The analysis shows that leading innovation systems, like those in Germany, use open innovation practices that favour incoming knowledge spillovers and appropriation mechanisms to generate licence income. Follower innovation systems, like those in Belgium, use open innovation practices such as research cooperation relatively more. Moderate innovation systems, like those in Spain, focus on incoming knowledge spillovers directed at imitation and appropriability to protect incremental innovation from imitation in weak innovators.
R&D intensity is a key element in the innovation policies in all innovation systems and is rightfully considered instrumental in the 3% target set by the European Commission. Key open innovation practices – incoming spillovers, cooperation and appropriability – are important external knowledge relations that are envisaged by many policy measures. In the case of firms in Germany, they are, on average, less influenced by incoming knowledge spillovers than Belgium and Spain. The quantile regressions show that in the case of Belgium, these spillovers were especially important for weak innovators although the estimated coefficient was high for strong innovators as well; whereas for Spain, they were increasingly important for strong innovators (but the impact was lower than that in Belgium).
In all innovation systems, innovative sales are influenced by R&D intensity, with the highest impact in Spain and the lowest in Belgium. Quantile regression reveals different patterns: in Germany, a U-shaped pattern indicating importance of R&D intensity for both weak and strong innovators; in Belgium, the effect of R&D intensity is more important for strong innovators and in Spain it is more important for weak innovators. Quantile regressions confirm that the external knowledge relations operate through R&D intensity. But again, there are several differences, as is the case for companies in Belgium; the weak innovators in Spain also benefit from appropriation mechanisms; whereas strong innovators in Germany benefit from research cooperation. Hence, the paragraphs above on company-level R&D intensity and innovative sales confirm to a large extent the aggregate findings derived from general statistics.
Since our aim was to embark on an empirical study, the research has created a number of blind spots. As argued by Van de Vrande et al. (2010), more theoretical research is needed on the relation between open innovation and the innovation system. It would be helpful if the type of open innovation practices identified are those (most) affected by certain characteristics of the innovation system.
Acknowledgements
The author would like to thank the FWO Vlaanderen (project G014708N) for its financial support and Dirk Frantzen for offering suggestions about the econometrics. He would especially like to express his gratitude to the anonymous referees of this journal for their valuable comments.
Open innovation practices and innovative performances 29
References Aulakh, P., Masaaki, K. and Teegen, H. (2000) ‘Export strategies and performance of firms from
emerging economies: evidence from Brazil, Chile and Mexico’, Academy of Management Journal, Vol. 43, No. 3, pp.342–361.
Becker, W. and Dietz, J. (2004) ‘R&D cooperation and innovation activities of firms – evidence for the German manufacturing industry’, Research Policy, Vol. 33, No. 2, pp.209–223.
Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S. and Rickne, A. (2008) ‘Analyzing the functional dynamics of technological innovation systems: a scheme of analysis’, Research Policy, Vol. 37, No. 3, pp.407–429.
Blind, K. (2012) ‘The influence of regulations on innovation: a quantitative assessment for OECD countries’, Research Policy, Vol. 41, No. 2, pp.391–400.
Bruneel, J., Spithoven, A., Maesen, A. and Clarysse, B. (2007) ‘External linkages of high tech start-ups in Flanders: networking and proximity’, in Bruneel, B., Clarysse, A., Maesen, N., Moray and Spithoven, A. (Eds.): An Analysis of Industry-Science Relations and Knowledge Networks of High Tech Start-Ups in Flanders, Flanders District of Creativity, Gent, pp.44–95.
Buchinsky, M. (1995) ‘Estimating the asymptotic covariance matrix for quantile regression models: a Monte Carlo study’, Journal of Econometrics, Vol. 68, No. 2, pp.303–338.
Buchinsky, M. (1998) ‘Recent advances in quantile regression models: a practical guide for empirical research’, Journal of Human Resources, Vol. 33, No. 1, pp.88–126.
Carlsson, B. (2004) ‘Innovation systems: a survey of the literature from a Schumpeterian perspective’, Paper presented at the International J.A. Schumpeter Society Conference, Milan, Italy, 9–12 June, pp.1–22.
Cassiman, B. and Veugelers, R. (2002) ‘R&D cooperation and spillovers: some empirical evidence from Belgium’, American Economic Review, Vol. 92, No. 4, pp.1169–1184.
Cassiman, B. and Veugelers, R. (2006) ‘In search of complementarity in innovation strategy: internal R&D and external knowledge acquisition’, Management Science, Vol. 52, No. 1, pp.68–82.
Chen, Q. (2008) ‘The effect of patent laws on invention rates: evidence from cross-country panels’, Journal of Comparative Economics, Vol. 36, No. 4, pp.694–704.
Chesbrough, H. (2003) Open Innovation: The New Imperative for Creating and Profiting from Technology, Harvard Business Press, Cambridge, MA.
Chesbrough, H. and Crowther, A.K. (2006) ‘Beyond high tech: early adopters of open innovation in other industries’, R&D Management, Vol. 36, No. 3, pp.229–236.
Chesbrough, H. and Rosenbloom, R.S. (2002) ‘The role of the business model in capturing value from innovation: evidence from Xerox corporation’s technology spin-off companies’, Industrial and Corporate Change, Vol. 11, pp.529–555.
Chesbrough, W., Vanhaverbeke, W. and West, J. (2006) Open Innovation: Researching a New Paradigm, Oxford University Press, Oxford.
Christensen, J.L. and Drejer, I. (2005) ‘The strategic importance of location: location decisions and the effects of firm location on innovation and knowledge acquisition’, European Planning Studies, Vol. 13, No. 6, pp.807–814.
Coad, A. and Rao, R. (2008) ‘Innovation and firm growth in high-tech sectors: a quantile regression approach’, Research Policy, Vol. 37, No. 4, pp.633–648.
Cohen, W.M. (1995) ‘Empirical studies of innovative activity’, in Stoneman, P. (Ed.): Handbook of The Economics of Innovation and Technological Change, pp.182–264, Blackwell, Oxford.
Cohen, W.M. and Klepper, S. (1996) ‘A reprise of size and R&D’, Economic Journal, Vol. 106, No. 437, pp.925–951.
Cohen, W.M. and Levinthal, D.A. (1989) ‘Innovation and learning: the two faces of R&D’, Economic Journal, Vol. 99, No. 391, pp.569–596.
30 A. Spithoven
Colombo, M. and Garrone, P. (1996) ‘Technological cooperative agreements and firm’s R&D intensity’, Research Policy, Vol. 25, No. 6, pp.923–932.
Cooke, P. (2004) ‘The role of research in regional innovation systems: new models meeting knowledge economy demands’, International Journal of Technology Management, Vol. 28, Nos. 3–6, pp.507–533.
Cooke, P. (2005a) ‘Regionally asymmetric knowledge capabilities and open innovation. Exploring ‘Globalisation 2 – a new model of industry organisation’, Research Policy, Vol. 34, No. 8, pp.1138–1149.
Cooke, P. (2005b) ‘Research, knowledge and open innovation: spatial impacts upon organisation of knowledge-intensive industry clusters’, Paper presented at the Conference of the Regional Studies Association ‘Regional Growth Agendas’, Aalborg, Denmark, 28–31 May, pp.1–27.
Crépon, B., Duguet, E. and Mairesse, J. (1998) ‘Research, innovation and productivity analysis at the firm level’, Economics of Innovation and New Technology, Vol. 7, No. 2, pp.115–158.
Davidson, R. and MacKinnon, J.G. (1993) Estimation and Inference in Econometrics, Oxford University Press, New York.
De Jong, J.P.J., Vanhaverbeke, W., Kalvet, T. and Chesbrough, H. (2008) ‘Policies for open innovation: theory, framework and cases, Research project funded by VISION-Eranet, Helsinki.
Ebersberger, B., Marsili, O., Reichstein, T. and Salter, A. (2006) ‘Into thin air: using a quantile regression approach to explore the relationship between R&D and innovation’, Working paper, Tanaka Business School, Imperial College, London.
Edquist, C. and Johnson, B. (1997) ‘Institutions and organizations in systems of innovation’, in Edquist, C. (Ed.): Systems of Innovation: Technologies, Institutions and Organizations, pp.41–63, Pinter Publishers, London.
European Commission (2010) Innovation Union, European Commission, Luxembourg. Fagerberg, J. and Srholec, M. (2008) ‘National innovation systems, capabilities and economic
development’, Research Policy, Vol. 37, No. 9, pp.1417–1435. Griffith, R., Huergo, E., Mairesse, J. and Peeters, B. (2006) ‘Innovation and productivity across
four European countries’, Oxford Review of Economic Policy, Vol. 22, No. 4, pp.482–498. Griliches, Z. (1990) ‘Patent statistics as economic indicators: a survey’, Journal of Economic
Literature, Vol. 28, No. 4, pp.1661–1707. Hagedoorn, J., Link, A. and Vonortas, N. (2000) ‘Research partnerships’, Research Policy, Vol. 29,
Nos. 4–5, pp.567–586. Hall, A., Rasheed Sulaiman, V., Clarck, N. and Yoganand, B. (2003) ‘From measuring impact to
learning institutional lessons: an innovation systems perspective on improving management of international agricultural research’, Agricultural Systems, Vol. 78, No. 2, pp.213–241.
Hart, D.M. (2009) ‘Accounting for change in national systems of innovation: a friendly critique based on the U.S. case’, Research Policy, Vol. 38, No. 4, pp.647–654.
Heckman, J. (1979) ‘Sample selection bias as a specification error’, Econometrica, Vol. 47, No. 1, pp.153–161.
Hekkert, M.P., Suurs, R.A.A., Negro, S.O., Kuhlmann, S. and Smits, R.E.H.M. (2007) ‘Functions of innovation systems: a new approach for analysing technological change’, Technological Forecasting and Social Change, Vol. 74, No. 4, pp.413–432.
Huston, L. and Sakkab, N. (2006) ‘Connect and develop: inside Procter & Gamble’s new model for innovation’, Harvard Business Review, Vol. 48, No. 3, pp.58–66.
Kline, S.J. and Rosenberg, N. (1986) ‘An overview of innovation’, in Landau, R. and Rosenberg, N. (Eds.): The Positive Sum Strategy, National Academy Press, Washington, pp.275–305.
Klomp, L. and Van Leeuwen, G. (2001) ‘Linking innovation and firm performance: a new approach’, International Journal of the Economics of Business, Vol. 8, No. 3, pp.343–364.
Open innovation practices and innovative performances 31
Laursen, K. (2011) ‘User-producer interaction as a driver of innovation: costs and advantages in an open innovation model’, Science and Public Policy, Vol. 38, No. 9, pp.713–723.
Laursen, K. and Salter, A. (2006) ‘Open innovation: the role of openness in explaining innovative performance among UK manufacturing firms’, Strategic Management Journal, Vol. 27, No. 2, pp.131–150.
Lööf, H. and Heshmati, A. (2002) ‘Knowledge capital and performance heterogeneity: a firm level innovation study’, International Journal of Production Economics, Vol. 76, No. 1, pp.61–85.
Lööf, H. and Heshmati, A. (2006) ‘Knowledge capital and heterogeneity in firm performance: a sensitivity analysis’, Economics of Innovation and New Technology, Vol. 15, Nos. 4–5, pp.317–344.
Lundvall, B. (1992) National Systems of Innovation. Towards a Theory of Innovation and Interactive Learning, Pinter, London.
Mohnen, P. and Therrien, P. (2003) ‘Comparing the innovation performance of manufacturing firms in Canada and in selected European countries: an econometric analysis’, in Gault, F. (Ed.): Understanding Innovation in Canadian Industry, pp.313–339, McGill-Queens University Press, Montreal.
Monjon, S. and Waelbroeck, P. (2003) ‘Assessing spillovers from universities to firms: evidence from French firm-level data’, International Journal of Industrial Organization, Vol. 21, No. 9, pp.1255–1270.
Nelson, R. and Rosenberg, N. (1993) ‘Technical innovation and innovation systems’, in Nelson, R.R. (Ed.): National Innovation Systems. A Comparative Analysis, pp.3–21, Oxford University Press, New York.
Nelson, R.R. (1993) National Innovation Systems: A Comparative Analysis, Oxford University Press, Oxford.
OECD (1997) National Innovation Systems, OECD, Paris. OECD (2002) Dynamising National Innovation Systems, OECD, Paris. OECD (2008) Open Innovation in Global Networks, OECD, Paris. OECD (2011) Main Statistics on Science and Technology Indicators, OECD, Paris. Parisi, M.L., Schiantarelli, F. and Sembenelli, A. (2006) ‘Productivity, innovation and R&D: micro
evidence for Italy’, European Economic Review, Vol. 50, No. 8, pp.2037–2061. Pavitt, K. (1984) ‘Sectoral patterns of technological change: towards a taxonomy and a theory’,
Research Policy, Vol. 13, No. 6, pp.343–373. Powell, W., Koput, K. and Smith-Doerr, L. (1996) ‘Interorganizational collaboration and the locus
of innovation: networks of learning in biotechnology’, Administrative Science Quarterly, Vol. 41, No. 1, pp.116–145.
Pro Inno Europe (2012) ‘Innovation Union Scoreboard 2011’, available at http://www.proinno-europe.eu/metrics (accessed on 15 February 2012).
Redding, S. (1996) ‘The low-skill, low-quality trap: strategic complementarities between human capital and R&D’, Economic Journal, Vol. 106, No. 435, pp.458–470.
Schmoch, U., Rammer, C. and Legler, H. (2006) National Systems of Innovation in Comparison. Structure and Performance Indicators for Knowledge Societies, Springer, Dordrecht.
Shan, W., Walker, G. and Kogut, B. (1994) ‘Interfirm cooperation and startup innovation in the biotechnology industry’, Strategic Management Journal, Vol. 15, No. 5, pp.387–394.
Simard, C. and West, J. (2006) ‘Knowledge networks and the geographic locus of innovation’, in Chesbrough, H., Vanhaverbeke, W. and West, J. (Eds.): Open Innovation: Researching a New Paradigm, pp.220–240, Oxford University Press, Oxford.
Spence, M. (1984) ‘Cost reduction, competition and industry performance’, Econometrica, Vol. 52, No. 1, pp.101–121.
32 A. Spithoven
Spithoven, A. (2005) ‘Internationalization of innovation in a small open economy. exploring the role of ownership structure on performances and R&D collaboration’, in Spithoven, A. and Teirlinck, P. (Eds.): Beyond Borders. Internationalization of R&D and Policy Implications for Small Open Economies, pp.259–295, Elsevier, Amsterdam.
Spithoven, A., Frantzen, D. and Clarysse, B. (2010a) ‘Heterogeneous effects of knowledge exchanges on product innovation: differences between lagging and dynamic product innovators’, Journal of Product Innovation Management, Vol. 27, No. 3, pp.363–382.
Spithoven, A., Clarysse, B. and Knockaert, M. (2010b) ‘Building absorptive capacity to organise inbound open innovation in traditional industries’, Technovation, Vol. 30, No. 2, pp.130–141.
Teece, D.J. (1986) ‘Profiting from technological innovation: implications for integration, collaboration, licensing and public policy’, Research Policy, Vol. 15, No. 6, pp.285–305.
Tidd, J., Bessant, J. and Pavitt, K. (1997) Integrating Technological, Market and Organizational Change, John Wiley and Sons Ltd., Chichester.
Van de Vrande, V., Vanhaverbeke, W. and Gassmann, O. (2010) ‘Broadening the scope of open innovation: past research, current state and future directions’, International Journal of Technology Management, Vol. 52, Nos. 3–4, pp.221–235.
Van Leeuwen, G. and Klomp, L. (2006) ‘On the contribution of innovation to multi-factor productivity growth’, Economics of Innovation and New Technology, Vol. 15, Nos. 4–5, pp.367–390.
Verbeek, M. (2000) A Guide to Modern Econometrics, John Wiley and Sons Ltd., Chichester. Veugelers, R. and Cassiman, B. (2005) ‘R&D cooperation between firms and universities: some
empirical evidence from Belgian manufacturing’, International Journal of Industrial Organization, Vol. 23, Nos. 5–6, pp.355–379.
Appendix
Table A1 presents a description of the manner in which the variables were constructed from the raw data of the CIS3 questionnaire and Table A2 presents the mean values of the variables with respect to the total sample, the sample of research active companies, and the corresponding industry dummies. Table A1 Description of the variables
Variable Definition Log of research intensity (LRDI)
Natural log of the ratio of intramural R&D expenditure over turnover in 2000
Log of innovative sales (LINSAL)
Natural log of the % of new or significantly improved products (goods and services) in sales in 2000
Incoming knowledge spillovers (ISP)
Sum of scores of the importance attached to information from suppliers, clients, competitors, universities, government, professional conferences, exhibitions (rescaled between 0 and 1)
Research cooperation (COOP)
Cooperation = 1 if the company collaborates either with suppliers, clients, competitors, research labs, universities or government
Appropriability (APP) Average score of the effectiveness of strategic protection through secrecy, complexity of design and lead-time advantage
Complexity of knowledge (CK)
Ratio of the sum of the scores of the importance attached to information from universities, government and professional conferences over the sum of the scores of all sources of information
Log of higher education intensity (LHEI)
Natural log of the number of employees with higher education over the level of turnover in 2000
Open innovation practices and innovative performances 33
Table A1 Description of the variables (continued)
Variable Definition Log of export intensity (LEXI)
Natural log of the proportion of exports in turnover in 1998
Log of size (LSIZE) Natural log of the level of turnover in 1998 Group affiliation (GR) Group affiliation = 1 if the company is a member of a group Process innovation (PCS)
Process innovation = 1 if the company has introduced new or significantly improved production processes during the period 1998–2000
PAVSUPP PAVSUPP = 1 if the company is classified in the ‘supplier-dominated’ sectors (mining, food, beverages and tobacco, textiles, clothing, leather products, wood and furniture, recycling, wholesale)
PAVSCAL PAVSCAL = 1 if the company is classified in the ‘scale-intensive’ sectors (paper and printing, rubber and plastics, non-metallic minerals, ferrous metals, non-ferrous metals, fabricated metal products, motor vehicles, other transport equipment)
PAVSPEC PAVSPEC = 1 if the company is classified in the ‘specialised supplier’ sectors (non-electrical machinery, electronics and communication, electrical machinery)
PAVSCIE PAVSCIE = 1 if the company is classified in the ‘science-based’ sectors (chemicals, pharmaceuticals, computers and office machinery, scientific instruments)
PAVINFO PAVINFO = 1 if the company is classified in the ‘information-intensive’ sectors (financial intermediation, telecommunication, transport services, business services, computer and related service activities, research and development, technical engineering)
Table A2 Mean values of the variables
Variable All companies
R&D active companies*
Of which:
resource intensive
Scale intensive
Special. supplier
Science based
Informat. intensive
BEL obs.
1,223 492 96 114 102 68 112
LRDI –4.546 –5.497 –5.400 –3.927 –4.337 –3.552 LINSAL –1.912 –2.354 –2.067 –1.637 –2.265 –1.492 ISP 0.419 0.379 0.424 0.476 0.410 0.402 COOP 0.380 0.312 0.403 0.402 0.456 0.348 APP 0.221 0.415 0.344 0.427 0.474 0.426 0.405 CK 0.328 0.276 0.304 0.360 0.373 0.340 PCS 0.669 0.698 0.754 0.618 0.676 0.598 LHEI –14.747 –14.190 –15.102 –14.721 –14.079 –14.277 –12.841 LEXI –2.875 –2.024 –1.896 –1.571 –1.421 –1.834 –3.258 LSIZE 15.762 16.147 16.460 16.520 15.970 17.160 15.044 GR 0.555 0.634 0.510 0.781 0.569 0.691 0.616
Note: *In the case of LINSAL, this relates to R&D active and product innovative companies.
34 A. Spithoven
Table A2 Mean values of the variables (continued)
Variable All companies
R&D active companies*
Of which:
resource intensive
Scale intensive
Special. supplier
Science based
Informat. intensive
GER obs.
2,813 1,117 154 296 312 77 278
LRDI –4.560 –5.218 –4.900 –4.022 –4.233 –4.527 LINSAL –1.525 –1.702 –1.532 –1.380 –1.717 –1.551 ISP 0.470 0.447 0.456 0.502 0.482 0.459 COOP 0.355 0.260 0.321 0.413 0.363 0.378 APP 0.249 0.447 0.409 0.422 0.503 0.467 0.426 CK 0.358 0.305 0.348 0.365 0.377 0.384 PCS 0.636 0.630 0.699 0.596 0.636 0.619 LHEI –14.518 –13.930 –14.914 –14.452 –13.576 –14.098 –13.182 LEXI –4.381 –3.274 –3.688 –2.826 –1.774 –1.793 –5.614 LSIZE 16.192 16.954 16.834 17.131 17.007 17.732 16.558 GR 0.374 0.471 0.416 0.476 0.477 0.623 0.446 Spain obs.
7,826 1,773 434 499 338 201 301
LRDI –4.532 –5.139 –4.844 –4.055 –4.314 –3.821 LINSAL –1.577 –1.642 –1.665 –1.290 –1.927 –1.441 ISP 0.429 0.411 0.414 0.430 0.467 0.456 COOP 0.310 0.194 0.327 0.287 0.468 0.375 APP 0.128 0.374 0.369 0.382 0.425 0.437 0.268 CK 0.353 0.344 0.342 0.309 0.429 0.384 PCS 0.707 0.737 0.780 0.633 0.667 0.654 LHEI –12.561 –13.059 –13.368 –13.297 –13.111 –13.405 –11.928 LEXI –4.757 –3.382 –3.342 –2.872 –2.491 –2.705 –5.737 LSIZE 15.162 16.288 16.320 16.581 16.048 16.934 15.589 GR 0.287 0.510 0.410 0.565 0.434 0.662 0.548
Note: *In the case of LINSAL, this relates to R&D active and product innovative companies.
