SECTORAL AND FIRM-LEVEL DIFFERENCES IN INNOVATION PERFORMANCE: EVIDENCE FROM FINNISH MANUFACTURING FIRMS

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1 Knowledge for Growth Industrial Research & Innovation (IRI) SECTORAL AND FIRM-LEVEL DIFFERENCES IN INNOVATION PERFORMANCE: EVIDENCE FROM FINNISH MANUFACTURING FIRMS CONTRIBUTED PAPER FOR THE 2007 CONFERENCE ON CORPORATE R&D (CONCORD) File name: <FILENAME> Author: Hanna Kuittinen Status: <Draft> Last updated: 03/10/2007 Organisation: Lappeenranta University of Technology, School of Business Page 1 of 11

2 TABLE OF CONTENTS 1 Introduction... Error! Bookmark not defined. 1.1 Introduction Pavitt taxonomy R&D cooperation and knowledge sharing Empirical evidence from Finnish firms Data Sectoral differences in R&D measures The effect of firm level and sectoral differences on innovative performance Measures and descriptive statistics Results Conclusions...9 References...10 Annex Annex 1 Descriptive statistics of R&D measures LIST OF ANNEXES Page 2 of 11

3 1 Sectoral and firm level differences in innovation performance 1.1 Introduction During the recent years Finland has been ranked among the most competitive economies in the world. This success has commonly been attributed to R&D investments that are above the OECD average. The indicators of competitive measures, however, often use more input side data (e.g. R&D costs per capital), and we know relatively little about the effectiveness of the investments in innovation activities. The big question after all, is not only the R&D expenditures but productivity and innovation performance generated by the investments. The relationship between corporate R&D investments and innovative performance is not so straight forward, however. In our view, the nature of innovation activity is strongly influenced by industry which firm operates in. In some industry sectors, investments in R&D are very important drivers of productivity. This is especially the case in the sciencebased industries such as pharmaceuticals or information technology. In some other industries the productivity growth can be accomplished by means of process improvements, and these efforts are very rarely counted as R&D investments. These different types of industries are not doing their R&D activities in isolation; on the contrary, the R&D activities of different industries coincide. The innovations occurring in one industry can be utilized to achieve productivity growth in some other industry. This is especially the case in some scale intensive production, where the actors are dependent on production machinery providers. This is why we suggest that innovation performance should not be measured without taking sectoral differences in innovative activities into account. In this paper, we apply modified Pavitt taxonomy (Pavitt 1984) to classify industries in relatively homogeneous groups in terms of basic nature of innovation activity. Recent studies (Bönte 2003, Vargas et al. 2007) have stressed the growing importance of external sources of R&D. As research and development activities are costly and time consuming endeavours, firms simply can not rely solely on internal sources of R&D if they want to keep up with ever fastening pace of technological change. This has led to diverse forms of externally sourced R&D including sourcing agreements, in which some parts of R&D activities are completely outsourced from other firms or various forms of R&D collaboration including joint ventures and strategic alliances. Although external sources of R&D may be defined by industry firm operates, as for example innovation in supplier dominated sector is driven by machinery and material providers, there might be firmspecific differences on how much and how well these external sources are utilized. This paper takes part to the wide discussion of corporate R&D measurement by first describing how commonly used measures gives different signals about corporate R&D activities depending on industry group analyzed. Secondly, this paper attempts to show that innovation performance is driven by both sector and firm specific characteristics. This study utilizes survey data from the sample of 299 medium sized and large Finnish companies that carry out R&D activities. The data were collected by means of a structured Page 3 of 11

4 questionnaire, using the key informant technique. In addition, Amadeus data base was used to retrieve financial data for sample companies. 1.2 Pavitt taxonomy The Pavitt (1984) taxonomy of industrial sectors categorizes industries according to determinants and patterns of technological change. It uses the nature and sources of innovation, technology user types and means of appropriability as central factors used in industry classification. The Pavitt (1984) taxonomy categorizes firms into four groups: 1) scale intensive, 2) supplier dominated, 3) specialized suppliers and 4) science based. Scale intensive industries are characterized by innovation activities attempting to achieve more efficient production process. Learning by doing play a major role in the innovative activities of these industries. Typically, scale intensive firms are large firms producing basic materials or consumer durables. Supplier dominated sector is dependent of external sources of innovation, mainly the providers of machinery and materials. Supplier dominated industry sector include firms from traditional manufacturing such as textile industry. Specialized suppliers are small machinery firms producing technology mainly for other firms. High technology firms, such as chemicals and electronics are classified as science based industries, which rely strongly on in house R&D and cooperation with universities and research institutes. This original taxonomy classified whole service sector in supplier dominated industry group. Latter revision (Tidd et al. 2001) suggested a fifth category that separates information intensive service firms from supplier dominated industry sector. This classification has been utilized in previous innovation research by Laursen and Meliciani (1999), Marsili and Verspagen (2001) and Souitaris (2002) among others. Similarly with Pavitt (1984) and others (e.g. Dosi 1982, Malerba and Orsenigo 1996) we see that the essentially different conditions of technological development in different industry sectors have an effect not only on the average R&D intensity of industries but also on the type of innovation activities of firms operating in these sectors. In other words, R&D costs consist of different types of input costs in different sectors, and outcomes of innovation activities are also dependent on industry characteristics. Previous studies (e.g. Souitaris 2000) suggest that Pavitt taxonomy is useful tool for characterizing industries in terms of determinant of innovation. 1.3 R&D cooperation and knowledge sharing The motives to engage R&D collaboration have been attributed to the chance to share risks and costs related to research and development endeavors (Das & Teng 2000) or to the possibility to outperform competitors by shortening innovation time span (Pisano 1990). Another motive leading to the formation of collaboration is expanding the firm s set of capabilities through knowledge sharing and learning (Kogut 1988, Blomqvist et al. 2002). Joining complementary resources and knowledge may create novel value creating combinations that could not be achieved by operating alone. The firms internal accumulated knowledge has a significant role in innovation activities (Cohen and Levinthal 1990). Also as several studies indicate the external knowledge Page 4 of 11

5 sources are vital for innovativeness (Chesbrough 2002, Laursen and Salter 2006, Caloghirou et al. 2004). For example, suppliers and customers as well as other cooperation partners may be important information sources for innovation. Shortening innovation cycles emphasize the need to organize innovation activities effectively, and this calls for rich knowledge exchange in production systems. Previous studies (see e.g. Belderbos ) highlight the role of cooperation and knowledge sharing in innovation activity. This study combines the industry sector level and firm level perspectives when trying to explain innovation performance of Finnish firms. 2 Empirical evidence from Finnish firms The empirical part of this paper introduces first the data we used and then describes how companies were grouped according the applied Pavitt taxonomy. To describe the six industry groups more in detail, we show sector averages of different types of innovation measures. These measures include (i) R&D input side measures (size, R&D intensity, share of R&D personnel, and share of university educated employees), (ii) innovation performance measures (sales from new products, process innovations), (iii) financial performance measures (profit margin, sales growth), (iv) measures related to collaboration and knowledge sharing). Lastly, we present a regression model accounting for effect of both industry and firm specific differences. 2.1 Data The empirical data used in this paper was drawn from a survey conducted in Data was collected by means of a structured questionnaire, using a key informant technique. The initial population consisted of Finnish companies from nine industrial sectors engaged in R&D. The sample frame comprised all firms with at least 50 employees from selected industry sectors. The companies were drawn from the Blue Book Database. A total of 1140 firms were identified, and 881 of them were found eligible when contacted by telephone. Of the 881 firms reached by telephone, 200 refused to participate. The survey questionnaire with an introductory cover letter was thus mailed to the 681 participating companies. Responses were received from 299 companies, yielding a satisfactory effective response rate of 33.9% (299/881). Most of the respondents had a title such as chief executive officer, managing director or R&D manager, indicating a senior position in the firm. For each company that responded the survey, financial data was drawn from Amadeus database. The data base contains financial statement information for European companies. 2.2 Sectoral differences in R&D measures We first grouped 299 companies in six industry groups following the applied Pavitt taxonomy presented in paragraph xx. Three digit NACE codes were used as base of classification. Scale intensive group include those production intensive industries that are producing standard materials or durable goods. Industries such as food and beverages and metal manufacturing were classified as scale intensive industries. In our sample there were all together 83 scale intensive companies. Companies classified as supplier dominated included mainly forest sector industries. Supplier dominated firms are Page 5 of 11

6 characterized by weak in house R&D and their innovations are drawn from suppliers of equipment or materials. In our sample there were 39 firms belonging to supplier dominated group. Specialized suppliers are also production intensive, but what differentiate them from scale intensive industries is that they tend to be smaller firms. This group included 75 firms manufacturing e.g. machinery, instruments or electronics. Fourth group, sciencebased industries, is characterized by strong linkages to universities and other scienceinstitutes. In our sample, the science based industries were chemical industries as well as manufacturers in information and communication technology industries. There were 40 firms belonging to this group. From science based sector we separated service firms, such as IT consulting and communication service, and classified these 22 firms as knowledge intensive services. The remaining 40 firms were from various service industries such as logistics and finance. Table 1 provides some more description of these six Pavitt based sectors and shows sector averages of firm size, different types of innovation measures and profitability. More detailed descriptive statistics can be found from Annex 1. Table 1. Sector averages. Scale intensive Supplier dominated Specialized supplier Sciencebased Services Knowledge intensive services Sales M Sales from new products % Introduced new process % R&D intensity % Sales from products % Sales from services % R&D personnel % University degree % Sales growth % Operating profit margin % R&D collab.customers % R&D collab.suppliers % R&D collab.competitors % R&D collab.universities % Total Table 1 shows that science based and specialized suppliers have the highest new product sales percentages, nearly twice as much as in service and supplier dominated sectors. Most of the firms (total average 70.3%) introduced new processes during three years time period thus resulting to very small variation between the six industry groups. Firms in science based industries have considerably high R&D intensity in comparison to other sectors, for which the average R&D intensity varies between one and four percentages. Supplier dominated sector is having the highest share of product sales, whereas most sales from services are attained by two service groups. Science based sector is having in average more R&D personnel in comparison to other sectors, as well as the most educated employees. Sales from new products seem to reinforce growth as the specialized suppliers and science based groups are growing the fastest. The lowest sales growth percentage is in service sector. When financial performance is considered, specialized suppliers and knowledge intensive service sectors are in average outperforming the others. Close to 80 percentage of all firms in the sample are having some collaboration with their customers. Supplier dominated sector is cooperating with Page 6 of 11

7 suppliers little more actively than other sectors as 65 percentage of the firms are declaring to have cooperation with their suppliers. Surprisingly, science based sector is not the most active one to collaborate with universities as more than half of the firms classified in scale intensive group are collaborating with universities. 2.3 The effect of firm level and sectoral differences on innovative performance Measures and descriptive statistics We applied linear regression model in order to show empirically how firm level and sectoral differences effect on innovative performance. All the variables used in regression models are described in table 2. The dependent variable innovation performance was measured as the natural logarithm of percentage of sales from products which have been launched or substantially improved within three years. To get the extent of the use of collaboration in R&D, we summed up four variables, each presenting the extent (1=0 20%, 5=80 100%) of cooperation with different types of partners (1) customers, (2) suppliers, (3) competitors and (4) universities. The collaboration index had a minimum of 4 and the maximum was 22 indicating that firms have very heterogeneous R&D cooperation strategies. The extent of knowledge sharing was computed as an overall index summing up the extent to which (1=to a little extent, 5=to a great extent) knowledge is received from and shared with internal as well as external parties. The parties listed in the questionnaire included subsidiary, parent company, suppliers, customers, universities, research institutes, company s other units, general management, consultants, new employees, and trade organizations. We included the Pavitt taxonomy dummies (5) into the model to account for sectoral effects on innovative performance. As control variables we used company size (log transformed sales) and R&D intensity (log transformed R&D expenses to sales). Table 2. Descriptive statistics and correlations. Measure Description n Min. Max. Mean Std.Dev Innovation performance log transformed sales% from new products Size log transformed sales R&D intensity log transformed R&D expenditure to sales ** 3 Collaboration index sum of four items Knowledge sharing index sum of ten items ** ** * Correlation is significant at the 0.05 level. ** Correlation is significant at the 0.01 level. Page 7 of 11

8 2.3.2 Results Altogether three regression models were conducted to specify how firm specific and industry specific variables affect the innovation performance of firm. Bringing new variables in Model 2 and Model 3 increases R square significantly, although the R squares are quite modest. At first stage (Model 1 in table xx), only control variables firm size and R&D intensity are included. R&D intensity has a positive and significant effect on innovative performance, but firm size seems not to be significant. In Model 2, the variables accounting for the use of external R&D sources are added as explanatory variables. The coefficient for knowledge sharing index is positive and significant implicating that the more companies share knowledge within the firm and with external actors including customers, universities, research institutes and trade organizations, the higher is the innovation performance. The extent of collaborative R&D activities is not statistically significant. The coefficient of R&D intensity remains significant and positive. In third stage (Model 3), sector dummies are included into analysis to account for variation in innovation performance that may be industry specific. Interestingly, after adding the sector dummies, the R square change is statistically significant, indicating that some of the innovation performance is indeed depending on industry conditions (Pavitt group). The control variable R&D intensity is no longer significant, thus the R&D expenditure seems to be sector dependent. The other control variable firm size, on the other hand, has statistically significant negative coefficient after adding the sector dummies. The coefficient of knowledge sharing index remains its positive and significant implying that the extent of knowledge sharing is not homogeneous among the companies of same industry sector. Table 3. Results of regression model. Model 1 Model 2 Model 3 B Std.Error B Std.Error B Std.Error Constant 2.984** ** ** Size (ln) * R&D Intensity (ln) 0.089* * Collaboration index Knowledge sharing index 0.055** ** Scale intensive Supplier dominated Specialized supplier 0.728** Science based 0.721** Knowledge intensive n Adj. R R 2 Change 0.049** 0.066** 0.076** Dependent variable is ln of innovation performance. * Significant at 5%. ** Significant at 1%. Page 8 of 11

9 3 Conclusions Table 1 describing the six industry groups is in some extent in line with Pavitt taxonomy pre conditions and previous studies. Science based sector is characterized by strong internal R&D (R&D intensity, R&D personnel, share of university educated employees) and collaboration with customers and with universities. Specialized suppliers have strong linkage to their customers as well, and have high percentage of sales coming from new products. In supplier dominated sector, the investments on R&D are in very low level and almost entire turnover is coming from products. Supplier dominated group collaborates with suppliers more intensively in comparison to other industry groups. Results from regression models indicate that the nature of innovation activities is industry sector specific to certain extent. However, also the firm level determinants such as the extent of knowledge sharing with different actors, inside and outside of the firm, seems to have a significant role in innovation activities. Even though this study did not find significant relationship between collaboration with various partners and innovation performance, on the basis of the previous studies there is reason to study this more. It might be that the measures used in this study do not capture this effect and in further studies more specific considerations are needed. However, in our view, it is very fruitful for innovation studies to combine industry specific and firm level factors in empirical analyses. Further, the findings of this study demonstrate that the use of Pavitt taxonomy in categorizing firm is relevant and reasonable. In terms of nature of innovative activities and innovation performance, there are differences between industry sectors, but there are also differences between firms belonging to same industry category. The interplay between firm level factors and industry sector specific conditions clearly deserves more attention in future research in the field of innovation studies. Page 9 of 11

10 References: Belderbos, R., Carree, M., Lokshin, B. (2004). Cooperative R&D and firm performance, Research Policy, vol. 33, Blomqvist, KM., Kylaheiko, K. and Virolainen, VM. (2002). Filling a gap in traditional transaction cost economics, International Journal of Production Economics, vol. no. 79, Bönte, W. (2003) R&D and productivity: internal vs. external R&D Evidence from West German manufacturing industries, Economics of Innovation and New Technology, vol. 12 no. 4, Caloghirou, Y., Kastelli, I., Tsakanikas, A. (2004). Internal capabilities and external knowledge sources: complements or substitutes for innovative performance?, Technovation, vol. 24, Chesbrough, H. (2002). Open innovation, the new imperative for creating and profiting from technology. Boston, Harvard Business School Press. Cohen, W.M., Levinthal, D.A. (1990). Absorptive capacity: a new perspective on learning and innovation, Administrative Science Quarterly, vol. 35, Das, T.K., Teng, B. S. (2000). A resource based theory of strategic alliances, Journal of Management, vol. 26, no. 1, Kogut, B. (1988). Joint ventures: theoretical and empirical perspectives, Strategic Management Journal, vol. 9, Laursen, K., Salter, A. (2006). Open for innovation the role of openness in explaining innovation performance among U.K. manufacturing firms, Strategic Management Journal, vol. 27, no. 2, Malerba, F., Orsenigo, L. (1996) Schumpeterian patterns of innovation are technologyspecific, Research Policy, vol. 25, no. 3, Marsili, O.,Verspagen, B. (2001). Technological regimes and innovation: Looking for regularities in Dutch manufacturing. Working Paper, ECIS, Eindhoven University of Technology. Pavitt, K. (1984). Sectoral patterns of technical change: towards a taxonomy and a theory, Research Policy, vol. 13, no. 6, Pisano, G. (1990). The R&D boundaries of the firm: an empirical analysis, Administrative Science Quarterly, vol. 35, Souitaris, V. (2000). Application of a sectoral taxonomy to explore contingencies in the theory of determinants of innovation, Proceedings of the IEEE International Conference on Management of Innovation and Technology (ICMT 2000). Souitaris, V. (2002). Technological trajectories as moderators of firm level determinants of innovation, Research Policy, vol. 31, Vargas, P., Salinas, R., Guerras, L.A. (2007). Does the technological sourcing decision matter? Evidence from Spanish panel data, R&D Management, vol. 37, no. 2, Page 10 of 11

11 Annex 1 Sales M Sales from new products % R&D intensity % Sales from products % Sales from services % R&D personnel % University degree % Sales growth % Operating profit margin % Scale intensive Supplier dominated Specialized supplier Sciencebased Services Knowledge int. services n Mean Std.Dev Min Max n Mean Std.Dev Min Max n Mean Std.Dev Min Max n Mean Std.Dev Min Max n Mean Std.Dev Min Max n Mean Std.Dev Min Max n Mean Std.Dev Min Max n Mean Std.Dev Min Max n Mean Std.Dev Min Max Total Page 11 of 11