In-house capabilities or cooperation? The effect of innovation methods on innovation outputs

Transcription

1 In-house capabilities or cooperation? The effect of innovation methods on innovation outputs Anthony Arundel and Catalina Bordoy ( * ) MERIT, University of Maastricht, The Netherlands Abstract Widespread support within the European Union for cooperative R&D and for innovation networks between firms is founded on the belief that these types of knowledge flows improve innovation outputs. The general consensus, based on previous survey research, is that more innovative firms tend to use an above average number of external knowledge sources. However, most of these studies have not evaluated measures of the relative importance of inhouse research versus the use of external information sources, particularly via collaboration. The KNOW survey results permits a closer look at these issues. We evaluated the effect of the share of product innovations developed in-house and via collaboration on the share of innovative products or services in the firm s total sales (innovative sales share). The regression results show that the share of innovations developed in-house has a positive and statistically significant effect on the innovative sales share, while the share of innovations developed via collaboration had no effect in a full data model. However, some collaboration increases the innovative sales share. Separate analyses by country show that German firms benefit more from collaboration, although this could be due to collaboration with other divisions within the same firm, and not from collaboration with independent partners. Introduction The role of collaboration in the creation and diffusion of innovations has received a considerable amount of interest in the past decade from both innovation economists and European policy makers. The European Commission, for example, requires collaboration for research funded under the Framework Programme and encourages national policies to increase the rate of collaboration between firms and between firms and public research institutes (PRIs) (Hagedoorn et al, 2000). Part of the increased attention given to collaboration is due to a shift in economic theories of innovation from a linear to an interactive model and to a recognition of the importance of tacit knowledge to innovation and technology transfer. The former shift places greater importance on information and knowledge flows, including via collaboration. This is partly because there are more linkages within an interactive or chain-link innovation system, and partly because modern innovation theories stress the diffusion of knowledge among many different actors (Kline and Rosenberg, 1986; Rothwell, 1992; Freeman, 1987; Nelson,1993; Lundvall, 1992; Edquist 1997; Asheim and Smith, 1999). Within this framework, the literature has focused on two types of linkages: inter-firm relationships such as networks of user-producers or * We would like to thank A. Marcos Vera for his helpful comments on an earlier draft. C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 1

2 competing firms (von Hippel, 1988; Lundvall, 1991; 1992) and linkages between firms and PRIs such as universities, government laboratories, and technical institutes (Mansfield, 1991; 1996; Pavitt, 1991). One perspective is that collaboration has increased in importance over the past few decades and is becoming an essential method of producing new knowledge of value to innovation. As an example, Gibbons argues that knowledge production has shifted from a mode 1 model in which problems are identified and solved in academic and corporate research labs to a mode 2 model, in which knowledge is created in a great variety of networked organisations, including large firms, small high-technology based firms, government institutions, universities, research laboratories and institutes (Gibbons, 1994). Georghiou (1998) further develops Gibbon s mode 2 model of innovation and suggests that contract research, based on cooperation and oiled by intellectual property rights, plays a central role in the exchange and production of new knowledge among governmental, industry, and university participants. Furthermore, the new mode of knowledge production includes a wider and more heterogeneous set of actors that collaborate temporarily on specific problems. One feature is the development of a collaborative space that links PRIs, firms, and other institutions. Similarly, Antonelli (1999) refers to the systematic use of technological co-operation as the dominant form of producing new knowledge. A second theoretical development with implications for collaboration is derived from research on the nature of knowledge and information. This posits a continuum between highly codified knowledge that is widely accessible in publications and patents and highly tacit knowledge that is only held in the minds of science and engineering staff (Saviotti, 1998; Cowan et al, 2000). Much of the knowledge developed within companies, particularly those active at the technological frontier, results from learning-by-doing or learning-by-using and may not be available in a codified form. Compared to other forms of knowledge flows based on codified information, cooperation provides better opportunities for the transfer of non-codified and tacit knowledge through interpersonal interactions (Senker, 1995) 1. In addition to providing access to non-codified knowledge, cooperative research can have several other advantages, including a reduction in technological and market uncertainty, costsharing, risk spreading, reduced duplication of research, economies of scale, and an ability to combine different types of expertise. These advantages could improve the economic efficiency of innovation. Although there has been an increase in R&D partnerships over the 1980s (Hagedoorn and Schakenraad, 1992) and the percentage of business R&D in the UK that is spent on external 1. Scientific knowledge is by nature more formal and transmittable in systematic language. However, the assumption that the results of science are a public good (i.e. codified, published, easily reproduced) is increasingly questioned. The application of formal information needs to be complemented by relevant tacit knowledge and skills, especially in new areas of research (Senker, 1995). C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 2

3 sources has increased from 5% in 1985 to 10% in 1995 (Howells, 1999), this does not mean that various forms of cooperation will become the dominant method for knowledge production or that the external R&D share will reach 20% or 50% in the near future. There could be a limit to the proportion of R&D spent on external sources that is set by several disadvantages of cooperation. Furthermore, specific factors that have driven much of the increase in PRI-industry cooperation, such as the development of biotechnology and ICT, could cease to play a role once these technologies mature. Recent innovation surveys, based on guidelines set forth in the OECD s Oslo Manual, provide additional empirical data on the role of cooperation. Analyses of the 1993 and 1997 European Community Innovation Surveys (CIS) show that firms rate the contribution to their innovative activities of their internal knowledge sources more highly than external sources. This could also be reflected in a preference for innovating in-house. Results from the 1997 CIS for seven European countries show that only 28% of innovative firms developed one or more product innovations in conjunction with other enterprises or institutes during the three years between 1994 and However, the second CIS provides no indicators that can be used to assess the relative importance of in-house versus cooperative innovation, nor do we know if the importance of cooperation is increasing, as suggested by the results of Hagedoorn and Schakenraad (1992). A preference for developing innovations within the firm could be due to several major disadvantages of cooperation. One is the diversion of energy and talents (Geroski, 1995), while another is concerns over appropriating the results, plus the need to reveal information to a cooperation partner. The latter can be an essential benefit of collaboration when most knoweldge is in tacit form, but can be a serious disadvantage if one or both partners are concerned about revealing information that they would prefer to keep secret. The cost savings to a firm from collaboration could also be reduced by the cost of developing an appropriate level of absorptive capacity (Cohen and Levinthal, 1990) to be able to successfully incorporate the results into new products and processes. The necessary economies of scale would favour large firms over small firms (Minne, 1996), although Mowery (1995) notes that economies of scale for R&D cannot always be necessary or only the largest firms would perform R&D. The need for firms to cooperate could also depend on characteristics of the technologies that are used to innovate. Well-understood technologies will probably be developed in house, while firms should be more likely to cooperate on innovations that are based on complex technologies that require a range of expertise or those at the technological frontier. An example of a complex technology is telecom equipment, which includes software, electronics, and computer parts. However, telecom equipment firms and firms in other sectors face three options for acquiring technologies outside their area of expertise. They can develop the 2 The six countries are Germany, France, Italy, Sweden, Norway., Portugal and Ireland. The rate of cooperation varies very little by firm size. Analyses by the author. C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 3

4 necessary capabilities in-house, collaborate on their development with other organisations, or they can buy-in sub-components from other firms. Veugelers and Cassiman (1999) characterise these choices as a make or buy decision and find that most firms use several strategies. If the benefits of cooperation to a firm outweigh the disadvantages, as suggested by Antonelli (1999) and others, we should expect cooperation to influence innovation outcomes: either more cooperation should lead to more innovations, and/or more cooperation should lead to more economically valuable innovations. Only a few studies, mostly based on the CIS, provide information on the impact of cooperation on innovation outcomes. The most commonly used outcome is the share of product sales from new or improved products that had been introduced onto the market in the previous three years. This is termed here the innovative sales share. First, Bosworth and Stoneman (1997) report that more innovative firms tend to use an above average number of external knowledge sources 3. Autio et al (1997) report similar results for the pulp and paper sector. Although firms can use external sources without collaborating 4, these results suggest that an outward looking strategy can increase innovative success. Nas et al (1994) look directly at participation in cooperative R&D and report that such cooperation increases the innovative sales share. Albach et al (1997) report that R&D cooperation among CIS respondents in the chemical sector is most prevalent among the more innovative subsectors of this industry, such as pharmaceuticals and agrochemicals. Conversely, two studies of the CIS results for the telecom and office equipment sectors found that firms that participate in cooperative R&D have a lower innovative sales share compared to firms that did not cooperate (Arundel and Steinmueller, 1996; Malerba et al, 1996). This raises the possibility that firms in these two sectors cooperate as part of a catch-up strategy. Vonortas (1997) reports that firms that did not cooperate in research joint ventures had higher profits than firms that did participate, which raises many questions about the value of collaboration and the reasons why firms participate in such projects. This report uses the KNOW survey results from seven European countries to explore the effect of collaboration on the innovative sales share. The KNOW survey has several advantages over the CIS. The greatest advantage is that the KNOW survey obtains information on the percentage of each firm s new or improved product innovations that were developed in-house, through buying in, or via collaboration with other divisions of the same firm or with independent firms or PRIs. In contrast, CIS only asks if the firm cooperated, on a yes or no basis, and then obtains information on the types of cooperation partners. It does not collect any information on whether or not cooperation led to innovations or the share of 3 Part of this effect is probably because more innovative firms also tend to be larger and larger firms simply have more opportunities to form external contacts (Arundel et al, 1995). 4 Examples are scanning patent databases and the literature for useful information, attending trade fairs and exhibitions, and reverse engineering of products on the market. A firm can use all four methods without revealing any information to its competitors. C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 4

5 innovations developed through cooperation. Second, the KNOW survey obtains information on each firm s use of three appropriation methods, secrecy, patents, and lead-times, and asks which method was the most important to the firm for protecting its innovations. Third, KNOW obtains data on the distribution of research spending by location: in-house, other divisions of the firm, and external to firm. Finally, the KNOW survey asks for the number of employees with an academic degree in science or engineering. This provides an alternative to R&D spending as a measure of the firm s innovative capacity. Methodology The KNOW survey was conducted in the Spring of 2000 in seven EU countries: the UK, Denmark, the Netherlands, France, Germany, Italy, and Greece. The choice of countries to include in the survey depended on the origin of the participants in the KNOW project, funded by the Framework Programme of the European Commission. Although the survey does not cover all EU countries due to funding limitations, the four largest EU economies were included plus two of the smaller, developed economies and one of the less developed economies. The survey was limited to five sectors: food and beverages (NACE 15), chemicals excluding pharmaceuticals (NACE 24 minus NACE 24.2), telecom equipment (NACE 32), telecom services (NACE 64.2), and computer services (NACE 72). These specific sectors were chosen to provide a range of low, medium and high technology manufacturing and to include two innovative service sectors. In addition, the survey was limited to small and mid-sized firms to avoid problems with defining the sector of activity of large firms, many of which are active in several sectors. In each country, a random sample of firms from two size classes ( employees and employees) within each of the five sectors was drawn from a national business registry. A standard survey protocol based on a telephone CATI technique with up to three call-backs was used in all countries with the exception of the UK, where a postal survey was used. Table 1 gives basic information on the number of firms surveyed, the number of responses, the response rates, and the number of useable responses for this paper. The latter is less than the number of responses because the analyses exclude non-innovative firms, firms that did not fit the sampling criteria for size and sector 5, and firms in one sector (see below). The response rates by country vary from 9.6% in the UK to 76.5% in Denmark, with an average of 25.3% if the UK is included and 33.2% if the UK is excluded. The KNOW survey asked a series of questions about the total innovative activities of the firm and a second series of questions on the firm s most economically important innovation. The analyses in this report are limited to the first question series. There are two key questions. The first obtains an estimate of the innovative sales share: the percentage of the firms sales, in the 5 For size, a maximum cut-off of 1,250 employees was used to allow for natural employment growth between the time that the data in the business registries were collected and the survey date. C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 5

6 previous year, from products or services that were significantly improved or new to your firm in the last three years 6. The second key question obtains an estimate of the percentage of the firm s innovations in the last three years that were introduced through buying in, in-house development, or collaboration with external partners. Buying-in is defined as purchasing, licensing, or contracting out. External partners include other divisions of the same firm plus PRIs and other firms, while in-house activities are restricted to the site of the firm itself, or where relevant, to the site of the respondent s division or subsidiary. Table 1. Survey results by country Firms Surveyed Responses Response rate Useable responses UK % 44 Denmark % 78 Netherlands % 114 France % 65 Germany % 51 Italy % 75 Greece % 100 Total % 527 Total excluding UK % Other relevant questions obtain information on the ownership status of the firm (division, national subsidiary, or independent), the use of three appropriation methods (patents, secrecy and lead-time), number of employees, sector of activity, research projects with PRIs, the firm s R&D status, personnel employed in R&D, the division of R&D expenditures by location (in-house, other divisions or subsidiaries of the same firm, independent organisations), and the number of employees with an academic degree. Preliminary analyses of the distribution of the results for the two key questions by country and sector found that the results for telecom services (NACE 64.2) varied widely by country, whereas there was considerably less variation for the other four sectors. For example, the innovative sales share for telecom services ranged by 50 percentage points between countries, compared to 23 points for computer services, 19 points for food and beverages, and 12 points each for chemicals and telecom equipment. National differences in the date and degree to which telecom services were liberalised probably explains part of the differences by country and would distort the results for the innovative sales share. For this reason, all of the analyses given below exclude telecom service firms. Some of the firms did not complete all survey questions. Where possible, the value of missing variables for interval variables (except for the dependent variable in the regressions) were 6 This question has an equivalent in both CIS surveys. C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 6

7 estimated using regression techniques based on the firm s sector, country, and size. These estimated values are included in the results given below. The descriptive results given below (with the exception of firm counts) are weighted by the number of employees in each firm, under the reasonable assumption that this provides a measure of the economic contribution of each firm to innovation outputs. As an example, a firm with 200 employees will contribute twice as much to a weighted average for the importance of patents as a protection method than a firm with 100 employees. The regression results are not weighted by employment because they include a variable to control for the effect of firm size. Descriptive Results 349 firms in the sample (68.8%) have less than 249 employees, while 158 (31.2%) firms are mid-sized, with between 250 and 1,250 employees. The mid-size firms account for 82.3% of total employment among the sample. Table 2 provides basic results for the characteristics of the firms for the two size classes. As shown in Table 2, the sample is mostly limited to innovative, R&D performing firms. The majority of both small and large firms have had one or more research projects with a PRI in the previous three years, over 77% perform R&D on a continuous basis, and over 63% report R&D spending outside of their division or firm. For both small and mid-size firms, patents are the least frequently cited most important method for protecting the firms innovations, while lead time advantages are cited by 36% of small firms and 44.9% of mid-size firms. (More small than large firms cite an other protection method or none of these three). Table 2. Characteristics of the respondent firms Measures of innovative capabilities Small (< 250 emps) Mid-size (250 1,250) At least one research project with a PRI in the previous 3 years 55.1% 74.4% Performs R&D continuously 78.5% 77.4% Occasionally 17.6% 17.4% Positive R&D spending in independent firms or PRIs 63.0% 75.1% Share of employees with an academic degree 21.8% 17.9% Appropriation methods Most important protection method is: Patents 12.1% 19.8% Secrecy 32.2% 26.8% Lead times 36.0% 44.9% Notes: Employment weighted within each size class. C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 7

8 Table 3 provides the percentage of firms that report developing one or more product or process innovations in-house, via buying-in, or through collaboration. The most commonly used innovation method is in-house development, followed by buying-in. Slightly less than half of the firms report collaborating on the development of either a product or process innovation. There is very little difference in the percentage of firms that collaborate on process compared to product innovations, but slightly more firms develop product innovations in-house or via buying-in. Table 3. Percentage of firms making some use of each innovation method Products/services Processes Either products or processes In-house Buying-in Collaboration Notes: Employment weighted. The remaining results for innovation methods are limited to product/service innovations because the main variable of interest, innovative sales share, does not include process innovations. Table 4 gives, for each sector, the average percentage of all product/service innovations that are developed through each innovation method. In all sectors, over 63% of product/service innovations are developed in-house, between 9% and 13% are bought in, and around 20% are developed via collaboration. The highest rate of collaboration is in the telecom equipment sector (24.1%) while the lowest rate is in computer services (13.7%). Table 4. Percent of product/service innovations developed through each method N In-house Buying-in Collaboration Food & beverages % Chemicals % Telecom equipment % Computer services % Total % Notes: Employment weighted. The mean innovative sales share varies by country, from a low of 27% in Greece to a high of 44% in Germany (results not shown). This is partly due to differences in the industrial distribution in each country and the average firm size. Both factors influence the innovative sales share, as shown in Table 5. The share is higher among small compared to mid-size telecom equipment firms while lower among small compared to large computer service firms. The difference by firm size is negligible in food and beverages and in chemicals. By sector, the innovative sales share is highest in telecom equipment and computer services. C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 8

9 Table 5. Innovative sales shares by sector and firm size Small (< 250 emps) Mid-size (250 1,250) Food & beverages 21.6% 20.2% Chemicals 29.5% 26.0% Telecom equipment 55.5% 43.5% Computer services 52.4% 72.6% Total 39.2% 44.4% Notes: Employment weighted within each size class. Regression Results The regression results investigate the effect of several factors on the innovative sales share. The main independent variable of interest is the percentage of products or services developed through collaboration (collaboration product share). Although the theoretical literature strongly supports a positive relationship between collaboration and the innovative product share, we do not predict the direction of the relationship because the empirical evidence is ambiguous. The alternatives to collaboration are to develop products in-house or via buying-in. Buying-in accounts for only 11% of all innovations developed by the sample firms. This means that the alternative to collaboration, for most firms, is to develop innovations in-house, which accounts for 71% of all innovations. Given the very high proportion of innovations developed in-house, it is not possible to develop a model that includes both in-house development and collaboration. For this reason, we construct two models. The first model includes a variable for the percentage of products developed in-house, plus a dummy variable that equals one when at least one innovation is developed via collaboration (positive value for the collaboration product share). The second model includes a variable for the percentage of products developed via collaboration, plus a dummy variable that equals one when at least one innovation is developed in-house. Other factors that could influence the innovative sales share are firm size, sector, internal innovative capabilities, an outward looking approach to innovation, ownership status, and appropriation conditions. Survey research consistently shows that larger firms are more likely to collaborate than small firms, but this could be because larger firms have more opportunities and more resources for collaboration. Firm size might have no affect on collaboration if the latter was measured in terms of a rate variable, such as the number of collaborations per employee or unit of R&D expenditure. Of course, the advantage of the KNOW data is that we can go a step further and evaluate the effect of firm size on the innovative sales share and how this is mediated by a C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 9

10 collaboration variable that is neutral in terms of firm size the percentage of innovations developed by collaboration. The firm s sector of activity could have a large impact on the innovative product share. Rapidly changing sectors such as telecom equipment and computer software should have a higher innovative product share than sectors, such as food and beverages, with slower rates of change. The chemical sector should have a higher innovative product share than food and beverages, but a lower share than telecom equipment. The firm s innovative capabilities should increase the innovative product share. Several measures of innovative capability are available from the KNOW survey. The model includes the percentage of personnel with an academic degree and whether or not the firm performs R&D on a continuous basis. Firms with an outward-looking approach to innovation could have a higher innovative sales share if these external contacts improve development efficiencies or provide access to new ideas. The model includes two relevant dummy variables: one or more research projects with PRIs (PRIPROJ) and positive R&D spending outside the firm in independent organisations (EXTR&D). Both variables also provide measures of potentially collaborative research with independent organisations. The KNOW survey includes independent firms (57.2% of the unweighted total), divisions of larger firms (26.4%), and national subsidiaries of foreign firms (15.6%). Both divisions and subsidiaries could have a higher innovation sales share, due to products obtained from other units within their firm. To control for this effect, the model includes a dummy variable (INDEPENDENT) that is equal to 1 if the firm is independent. We expect the coefficient for this variable to be negative. Finally, appropriation conditions could influence the innovative sales share. This would occur, for instance, if patents were an effective method of protecting markets from competitors. Alternatively, secrecy could reduce the innovative sales share if concerns over releasing information reduced a firm s willingness to collaborate and if collaboration had a positive effect on innovative sales. The importance of lead time advantages as an appropriation strategy is strongly correlated with the innovative sales share, but the cause and effect relationship here is complex. A strategy of competing on the basis of lead time advantages could explain the positive correlation with innovative sales shares, but highly innovative products could create lead-time advantages, without the firm adopting a lead time strategy. In contrast, firms will adopt a strategy of using secrecy or patents for appropriation before the product is on the market. C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 10

11 Model specification The dependent variable, the proportion of sales from innovative products, can vary from 0 to 1, with all possible values falling within this range 7. Since the dependent variable cannot take a negative value or exceed unity, an OLS model will provide biased coefficients. The model used here is a non-linear which assumes a S shaped distribution in the dependent variable. This assumption is reasonable because firms will need to develop a minimum level of internal expertise before their innovative sales share can increase notably from zero. Conversely, several factors will constrain the innovative sales share from reaching 100%, such as sales from existing products and appropriation and competition effects that prevent firms from dominating the market with their innovative products. The model takes the following form: y i ' exp( β xi + ε i ) = ' 1 + exp( β x + ε ) i i where y i is the innovative sales share (ISSHARE) and x i is a vector of independent variables. This functional form guarantees that for any value of both the variables and the parameters, the value of the conditional mean lies between 0 and 1. The vectors of independent variables for each model are the following: Vector for model 1: ISSHARE = F( β + β INHOUSESHARE + β COLLPROD + β R & DEMPSHARE + β LOGEMP + β CHEMICALS + β TELECOMEQ + β COMPSER + β EXTR & D + 4 β PRIPROJ + β INDEPENDENT + β CONTR & D + β COMPETS + β β SECRECY 14 + γ COUNTRY j j + ε i ) SUBSIDY + Vector for model 2: ISSHARE = F( β + β COLLABSHARE + β INHOUSE + β R & DEMPSHARE + β LOGEMP + β CHEMICALS + β TELECOMEQ + β COMPSER + β EXTR & D + 4 β PRIPROJ + β INDEPENDENT + β CONTR & D + β COMPETS + β β SECRECY 14 + γ COUNTRY j j + ε i ) SUBSIDY + The full models for all countries combined are unweighted and include dummy variables for country (results not shown), with the Netherlands as the reference category. The models for each country are weighted by the sample fraction in each sampling cell, defined by firm size and sector. 7 We favour this specification against a Tobit model since the Tobit model assumes that values outside the observed range are possible but are unobserved due to truncation or censoring. In our case, by construction of the dependent variable, values outside the range are not possible. C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 11

12 Results Table 6 provides the results for equation 2 (Model 1) and equation 3 (Model 2) for all countries combined. Model 1 shows that there is a positive relationship between the percentage of products developed in-house and the innovative sales share. Conversely, the percentage of products developed through collaboration has no statistically significant effect on the innovative sales share. Furthermore, in both models the variable for external R&D spending (EXTR&D), which could involve collaboration, has no effect on the innovative sales share. The variable for collaboration with PRIs (PRIPROJ) is unexpectedly negative, although only significant with p <.1. The only variable that shows a positive effect for collaboration is the variable COLLPROD (some innovations developed via collaboration) in model 1. These results indicate that in-house development has the greatest positive impact on the innovative sales share, but that some experience with collaboration will also positively increase the innovative sales share. Table 6. Non-linear logistic model results for innovative sales share 1 2 B Std. Error P B Std. Error P Constant *** *** In-house product share *** COLLPROD ** Collaboration product share INHOUSE ** R&D employee share * Log employees Dummy variables for sector Chemicals ** ** Telecom equipment *** *** Computer services *** *** EXTR&D PRIPROJ * * INDEPENDENT CONTR&D ** ** COMPETS SUBSIDY SECRECY N Model F R Notes: Although not shown, both regressions include dummy variables for country, with the Netherlands as the reference category. The reference category for the sector dummies is Food and Beverages. The reference categories for the other dummy variables are: INHOUSE: zero products developed in-house, COLLPROD: zero products developed via collaboration; PRIPROJ: no research projects with public research institutes; INDEPENDENT: a division or subsidiary of another firm; CONTR&D: performs R&D occasionally or never; C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 12

13 COMPETS: less than 10 competitors; SUBSIDY: does not report government subsidies for R&D, SECRECY: secrecy is not the most important appropriation method. *** p <.01; **.01 <p <.05; *.05 < p <.1 The number of employees has no notable impact on the innovative sales share in both models. In contrast, the coefficient for the share of R&D personnel is not significant in model 1, although it increases the innovative sales share in model 2. As expected, firms that perform R&D on a continuous basis have a higher innovative sales share. The dummy variable for secrecy as the firm s most important appropriation method has no effect on the innovative sales share, nor does having over 10 competitors (COMPETS), or the receipt of government subsidies for R&D. Independent firms could have a lower innovation sales share, since they cannot obtain new products from other divisions or their head office. However, the coefficient for INDEPENDENT is not significant in either model. Both models were also run after limiting the cases to 286 independent firms. Neither the in-house product share nor COLLPROD was significant in model 1. In model 2, the collaboration product share was not significant, but INPROD is positive and of borderline significance (p <.1). Other than sector effects, the most important variables for both models for independent firms only were CONTR&D and SECRECY, which had a positive and a negative effect on the innovation sales share, respectively. Results by sector As expected, the firm s sector of activity has a significant effect on the innovative sales share. Compared to the reference category of food and beverages, all other sectors have significantly larger innovative sales shares. The difference is greater for telecom equipment and computer service firms than for chemical firms. The two regressions were run separately for each sector. An acceptable model fit was obtained only for 140 firms in the computer services sector (a lack of fit in the other sectors is likely to be due to the small sample size combined with low variability in the dependent variable). The results for computer services are similar to the results given in Table 6 for all firms. The in-house product share and some collaborative activity increase the innovative sales share, but the collaboration product share has no effect. The coefficient for CONTR&D is only statistically significant (p<.1) in the second model. Results by country One concern is that there could be unexplained differences by country in what constitutes an innovation, which could influence the estimated innovation sales share. Consistent national differences will partly be accounted for by the inclusion of country dummies in the main regressions presented in Table 6. Nevertheless, both models were also run separately for each country, since different national systems of innovation could cause different patterns in the C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 13

14 influence of several factors on the innovative sales share. The country results, however, need to be interpreted cautiously, since they are based on a small number of firms. These regressions for each country are weighted to represent the national distribution of innovative firms by size and sector 8. Table 7 provides the results for model 1 (contribution of in-house development of innovations) while Table 8 provides results for model 2 (collaborative development). No results are given for the UK, due to a small number of cases, while neither model fits the data for France or Italy. The in-house product share is only significant for Holland in model 1, while COLLPROD is significant and positive in three of the four countries. This suggests that Germany, Denmark and Greece do not contribute very much to the significant coefficient for in-house product share in Table 6. Conversely, the model 2 results shown in Table 8 show that the collaboration product share is significant and positive in Germany but significant and negative in Greece. INHOUSE (some innovations developed in-house) is positive and significant for Holland and Germany, but significant and negative for Greece. Table 7. Non-linear logistic model results for innovative sales share Model 1. In-house product share Holland Germany Denmark Greece Constant *** ** In-house product share *** COLLPROD ** *** ** R&D employee share Log employees * * ** Dummy variables for sector Chemicals ** Telecom equipment *** ** ** Computer services *** *** EXTR&D * PRIPROJ * INDEPENDENT ** *** ** CONTR&D ** COMPETS * ** SUBSIDY ** ** SECRECY * N Model F R Notes: Weighted results. *** p <.01, ** p <.05, * p < The weighted results are rescaled to equal the actual sample size while maintaining the weighted distribution. C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 14

15 These and other results from the country models suggest that collaboration in Germany makes a positive contribution to the innovation sales share, but has no effect or a negative effect (Greece) in the other three countries. The positive effect of collaboration in Germany could be due to links with other divisions within the same firm, since coefficient for INDEPENDENT is negative and highly significant in both models. This shows that the innovative sales share for German firms is higher for firms that are part of a group. Another interesting result in the case of Germany is that the variable SUBSIDY has a significant and positive effect on the innovative sales share in both models. Moreover, this is the only result that still holds in an unweighted regression for Germany, other than sector effects. Table 8. Non-linear logistic model results for innovative sales share Model 2: Collaboration product share Holland Germany Denmark Greece b B B B Constant *** Collaboration product share *** *** IN-HOUSE * ** ** R&D employee share Log employees * Dummy variables for sector Chemicals * Telecom equipment *** * ** Computer services *** *** * EXTR&D PRIPROJ * INDEPENDENT ** *** ** CONTR&D ** COMPETS * ** *** SUBSIDY * ** SECRECY N Model F R Notes: Weighted results. *** p <.01, ** p <.05, * p <.10. For Denmark, neither the in-house product share (Table 7) nor the collaboration product share (Table 8) increases the innovation sales share. The most interesting result is that in both models independent firms have a higher innovative sales share, while participation in research projects with PRIs (PRIPROJ) and subsidies (SUBSIDY) reduces the innovative sales share. The latter result could be due to the survey capturing small high technology start-ups which have yet to introduce products onto the market 9. An alternative explanation is due to the small 9 Question to KNOW Danish partners: Is this plausible? C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 15

16 number of firms for Denmark and the lack of significance for the sector variables, which in contrast are highly significant for both the Netherlands and Germany. For Denmark, the specific variables for firm characteristics could be explaining the dependent variable, while for the Netherlands and Germany the larger sample size means that differences in firm characteristics are captured by sector variables. The unweighted regressions for Denmark provide similar results, except that the coefficients for INDEPENDENT and SUBSIDY are no longer significant. The results for Greece show no effect for in-house product share, although some collaboration (COLLPROD) increases the innovation sales share in model 1. In model 2, the coefficients for collaboration product share and IN-HOUSE are both significant and negative. The unweighted results are similar, except for the coefficients for COLLPROD and INDEPENDENT in model 1 and INHOUSE in model 2, which are no longer significant. Since both the collaboration product share (model 2) and the in-house product share (model 1) are negative for Greece, the share of innovations out of total sales in Greece is probably more dependent on buying-in innovations than in other countries. While the average percentage of product innovations developed via buying in is 12.5% across all countries, Germany and Greece buy in 17.3% and 21.14% of all their product innovations, respectively. Conclusions Based on the regression results for the full data set, in-house capabilities, as shown by the percentage of product innovations developed in-house, increase the innovative sales share, although some collaboration is also beneficial. In contrast, other measures of collaboration, such as the percentage of products developed via collaboration, participation in research projects with PRIs, and external R&D spending, have little or no effect. The results for specific countries show that differences in national systems of innovation could influence the role of collaboration. The collaborative product share increases the innovative sales share in Germany, although this result could be due to collaboration with other divisions within the same firm, rather than collaboration with external partners. In both Germany and the Netherlands, independent firms have lower innovative sales shares, as expected, but in Denmark independent firms have a higher innovative sales share. However, there is little evidence from Denmark that either collaborative activities or in-house activities influence the innovative sales share, with the exception of a decline in the innovative sales share for firms that collaborate with PRIs. These results provide little empirical support for the very positive view of collaboration in recent innovation theory. These results are of interest to European innovation policies to encourage collaboration. The results clearly question the advantages of too much collaboration, although some collaboration is beneficial. There are several limitations to the results: they are limited to seven countries, four sectors, and small and medium-sized firms with less than 1,250 employees. Collaboration could be far more effective and productive in other countries or C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 16

17 among larger firms, or what is more likely, in specific sectors such as biotechnology where there is intense collaboration between PRIs, dedicated biotechnology firms, and large pharmaceutical and seed firms. If true, this means that policies to encourage collaboration need to carefully consider how collaboration is mediated by the development stage of the technology. References Albach et al. Innovation in the European Chemical Industry. EIMS Project Report, Luxembourg, May, Antonelli C. Industrial organisation and the production of knowledge. Cambridge Journal of Economics 23: , Arundel A, Soete L, van der Paal G. Innovation Strategies of Europe s Largest Industrial Firms, European Commission, Arundel, A, Steinmueller E, Hawkins R. Strategies for the Future: Innovation in the European Telecom Equipment Industry. MERIT, July Asheim, B. and Smith, K. Regional Innovation Systems, Regional Networks and Regional Policy. Cheltenham: Edward Elgar Autio E, Dietrichs E, Fηhrer K, Smith K. Innovation Activities in Pulp, Paper and Paper Products in Europe. STEP Report 4/97, STEP, Oslo, Beise M, Stahl H. Public research and industrial innovations in Germany. Research Policy 28: , Cohen W, Levinthal D. Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly 35:128-52, Cowan R, Foray D. The economics of codification and the diffusion of knowledge. MERIT research memorandum 2/97-005, MERIT, Maastricht, Bosworth D, Stoneman P. Information and technology transfer in Europe. In Arundel A, Garrelfs R (Eds) Innovation Measurement and Policies, pp , European Commission, Luxembourg, EUR EN, Edquist, C. (ed.) Systems of Innovation: Technologies, Institutions and Organisations. London: Pinter, Freeman, C. Technology Policy and Economic Performance: Lessons from Japan. London: Pinter, Georghiou L. Science, technology and innovation policy for the 21st century. Science and Public Policy 25: , Geroski P. Markets for technology: Knowledge, innovaiton and appropriability. In Stoneman P. Handbook of the economics of Innovation and Technological Change. Basil Blackwell, Oxford, pp , Hagedoorn, J. and Schakenraad, J. Leading companies and networks of strategic alliances in information technologies. Research Policy, vol 21, Hagedoorn, J, Link AN, Vonortas, N. Research partnerships. Research Policy 29: , Howells J. Research and technology outsourcing. Technology Analysis and Strategic Management 11:17-29, Kleinknecht A, Reijnen J. Why do firms cooperate on R&D? Research Policy 21: , C:\WINDOWS\Επιφάνεια εργασίας\rename-ioanna\in-housemerit_final.doc 17

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