Sources of information for innovation and innovation activities in the high technology sector in Poland

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1 Sources of information for innovation and innovation activities in the high technology sector in Poland Piotr Dzikowski Abstract This paper presents the results of a study aimed at identifying the nature of the relationship between the type of innovation activity and sources of information for innovation in high technology sector in Poland between It was assumed that the importance of the innovation source is proportional to the number and scope of innovation activity involved. The scope of the survey relates to innovation in industrial enterprises belonging to the high-tech sector. It is characterized by innovation at the firm level and takes into account the diffusion to the "new for the company". Innovation activity includes (1) expenditure on research and development and investments in fixed assets not used so far such as: a) buildings, premises and land; b) machinery and equipment, c) computer software, (2) implementation of new products and technological processes, and (3) innovation cooperation. Sources of information for innovation concerned in the analysis include: customers, internal sources, suppliers, conferences, fairs, exhibitions, competitors, journals, trade magazines, scientific societies, foreign and domestic R&D units, universities, Polish Academy of Sciences units. The survey covers 374 HT industry companies. The methodological part of the analysis is based on a probit modeling, which allows to determine the probability of innovative behaviors depending on the source of information for innovation. Each model is described by two probabilities. P 1 determines the probability of the innovative activity in the selected set of sources. P 2 determines the degree of probability of the innovative activity for the rest of sources. If the model is positive (a>0), P 1 means that the probability is higher for a given source than for all other sources together. Keywords: innovation activity, high technology, innovation, sources of information Introduction The creation, transfer and absorption of new knowledge are essential parts of innovation activity. Innovation requires enterprises to develop the capacity of providing products, services, processes, organization and marketing message to satisfy not only present, but future customer needs (Janasz, 2009). Any innovative action is the result of the relationship between the enterprise and the different sources of information, knowledge and technology, but firms should individually decide which sources: external or internal are the most profitable (Chesbrough, 2006). Internal sources include both formal research and development (R&D) activity performed inside the enterprise and informal information that comes from different departments (marketing, production, distribution) or other units within a group the company belongs to (OECD, 2008). Formal R&D knowledge can also be obtained from external sources such as universities or national and international research institutes (Link, Rees, 1990; Harryson, Kliknaite, Dudkowski, 2008). One of the most important sources for innovation especially for latecomer countries, such as Poland are vertical linkages including suppliers (Schiele, 2010), customers (Grimpe and Sofka, 2009) and competitors (Lim, Chesbrough, and Ruan, 2010) and participation in the inter-industry supply chains (Świadek, 2012). Other category includes individuals who may be involved by companies in the process of co-creation (Prahalad, Ramaswamy, 2004). Users who utilize their own knowledge which may be used for the benefit of firms (Morrison, 1

2 Roberts, von Hippel, 2000) play particular role. When users gather in crowd of users we get crowdsourcing (Poetz, Schreier, 2012). Public available information for innovation includes patent disclosures, professional conferences, meetings and journals, fairs and exhibitions (Ramírez-Pasillas, 2010) The information for innovation may be produced locally, regionally or abroad (Palumbo, O.Hara, Herbin, 1998). One of the most important sources of information is innovative cooperation. It relates to the ability of active cooperation with other market players such as suppliers, customers, competitors, research institutions or regulatory agencies (Miotti and Sachwald, 2003). The main purpose of this article is to present the nature of relationships between innovation activities and sources of information for innovation in high technology industry in Poland. The main hypothesis is an assumption that the more linkages the enterprise has got the more innovation activities it presents. Innovation activity literature review Innovation is a multi-faceted phenomenon which includes factors that enable inventions to become innovations (Chandy, Hopstaken, Narasimhan, & Prabhu, 2006), determinants of innovation (Love & Roper, 1999) and consumer responses to innovation (Hauser, Tellis, & Griffin, 2006) among others. Innovation can be a process or a result of its implementation. The creation of an idea, research and development, design, production and dissemination can create the innovation process (Dolińska, 2010). The result of innovation can be a good or service or an idea that is perceived by the customer as a new one (Pomykalski, 2001). The implementation of a new or significantly improved product (service) or process, a new marketing method or organizational business practice, organization, workplace or relationship with the environment is a definition of the innovation by OECD (OECD, op.cit., p.49). Innovation can only occur if a firm has the capability to innovate (Laforet, 2011). Innovation capability is composed of the main processes within the firm (Lawson and Samson, 2001). To become innovative, an organization has to develop the resource or its ability to effectively and efficiently create, implement and manage its innovation potential. Both the structure and organization of innovative companies facilitate the process of innovation. Every innovative enterprise has got the ability to generate permanent innovation, creativity and the ability to maintain a high competitive position based on core competencies, the competence to anticipate the future, the ability to effectively explore the needs of customers, innovators team to ensure a high level of innovation in the company, the flexibility of adapting to changing conditions (Sosnowska, Łobejko, Kłopotek, 2000). On the other side innovation activity allows innovative firms to sustain their innovativeness. It depends on the firm structure and its relationship with the various sources of information, knowledge, technology, work practices and human and financial resources (Okoń-Horodynska, Zachorowska-Mazurkiewicz, 2007). Innovation activity involves investments in: (a) research and development, (b) technology assets, (c) the purchase of advanced machinery, equipment, computer hardware or software, as well as land and buildings (including upgrades and repairs), (d) training of staff and marketing of new and improved products (e) other activities including design work, planning and testing of new products and services, (production processes and methods of delivery. The pace and nature of those dynamic relationships also depend on the market (Dierkes, 2003). Methodology of the study The methodological part of this analysis is based on probity modeling. This instrument allows to determine the probability of innovative behaviors in relation to a prevoiusly defined set of 2

3 information sources (Dzikowski, 2013). All models meet the following assumptions: (1) the data came from a random sample, (2) Y can take only two values: 0 or 1, (3) subsequent Y values are statistically independent, (4) the probability that Y = 1 is defined by normal distribution for the probity or logistic distribution for logit model. There is no perfect linear relationship between the variables in the logit model Xi (Lipiec-Zajchowska, 2003). Parameter estimation is performed using the maximum likelihood method (MLE). It allows us to find a vector of parameters that guarantees the highest probability of obtaining the observed value of the sample (Welfe, 1998). MLE requires the definition of likelihood function and finding its extreme. The nonlinear estimation procedure uses a quasi-newton algorithm to find the minimum of the loss function. In this way, a collection of the best estimators for the loss function is calculated (Stanisz, 2007). Maximizing the likelihood function for the probity model is made using the techniques used in the nonlinear estimation (Maddala 2006). All relations are linear equations, because both innovation activities (the dependent variables) and sources of information for innovation (independent variables) are binary. Every model is described by two probabilities. P 1 determines the probability of innovation activity for a particular source of information. P 2 determines the probability of innovation activity for all other sources. If the function parameter is positive (a>0), then P 1 takes higher value in a given group. All statistically significant models include standard error (Std). Research sample The scope of this study concerns innovation activity in high technology industry at the level of firm and new to the firm. This group includes industries which has the highest expenditure on research and development. It ranges from 8% to 15% of total income. It includes such industries as: aircraft and aerospace industry, production of office machinery and computers, consumers electronics manufacturing and pharmaceutical industry and the production of medical instruments, optical and precision equipment. Table 1 shows the structure of high technology industry sector. PKD Symbol Industry type Table 1. The structure of high technology industry sector The average share of direct expenditure of R&D in the value of production * Number of firms/ share (%) 35.3 Manufacture of air and spacecraft and related machinery 13,3 5 (1,34) 24.4 Manufacture of basic pharmaceutical products and pharmaceutical preparations 10,5 60 (16,04) 30 Manufacture of computers and peripheral equipment 9,2 52 (13,90) 32 Manufacture of communication equipment 8 70 (18,72) Manufacture of instruments and appliances for measuring, 33 testing and navigation; watches and clocks Source: Author s research & (Hatzichronoglou, 1996) 7,7 187 (50) The survey is based on a questionnaire send by or conduct during a telephone interview with a manager or company founder. All data was gathered between in Poland. Data was collected on the region base what helps to follow the data structure introduced by GUS. Information was collected from every Polish region and stored in a database. The information about potencial enterprises was taken from commercial and noncommercial sources of information such as Teleadreson, PKT and others. The success rate was about 15%. The final set contains 374 enterprises including 172 (45,99%) micro, 103 (27,54%) small, 66 (17,65%) medium and 33 (8,82%) large companies. 313 enterprises * The indicator is based on data gathered from the USA, Canada, Japan, Denmark, Finland, France, Germany, Ireland, Italy, Spain, Sweden and Great Britain between 1991 and

4 (83,69%) represent national capital whereas foreign capital has got 33 companies (8,82%) and 28 (7,49%) units have mix capital. The links between sources of information for innovation and innovation activity The overall number of likages with all kind of information sources is 1036 what gives 2,77 linkages per one enterprise. The greatest number of dependences concerns customers (195), conferences, fairs and exhibitions (153) and internal sources (133). The lowest number of linkages concerns national R&D units (15), Polish Academy of Sciences (21) and universities (35). Figure 1 shows the structure of linkages to sources of information for innovation in HT industry sector in Poland. Figure 1. The structure of linkages to sources of information for innovation in HT industry sector Source: Author s research Further analysis shows differences in nature of linkages among different types of HT industry. The highest average number of connections relates to manufacturers of basic pharmaceutical products and pharmaceutical products and pharmaceutical preparations (3,07), whereas the lowest number concerns manufacturers of instruments and appliances for measuring, testing and navigation; watches and clocks (2,66). The average number of linkages in both computer and peripheral equipment manufacturers and communication equipment manufacturers industry equals (2,8). The greatest discrepancy (6,76%) concerns scientific societies where the highest number of links concerns manufacturers of basic pharmaceutical products and pharmaceutical products and pharmaceutical preparations (PKD ,87%), whereas the lowest number concerns manufacturers of computer and peripheral equipment (PKD 30-4,11%). 4

5 The lowest discrepancy (0,79%) is between the number of linkages with competitors where the highest number of links concerns of communication equipment manufacturers (PKD 32-9,69%) and the lowest number includes manufacturers of computer and peripheral equipment (PKD 30-8,90%). The highest number of differences is between manufacture of computers and peripheral equipment (PKD 30) and manufacture of basic pharmaceutical products and pharmaceutical preparations (PKD 24.4) whereas the most important source for the manufacturers of instruments and appliances for measuring, testing and navigation; watches and clocks includes conferences, fair and exhibitions (only one extremum). Manufacturers of basic pharmaceutical products and pharmaceutical products and pharmaceutical preparations (PKD 24.4) are linked to knowledge sources (both national and foreign R&D units, Polish Academy of Sciences units and scientific societies) the most. The Figure 2 includes the detailed structure of linkages for 4 out of 5 subgroups (the number of companies in the manufacture of air and spacecraft and related machinery industry is too low to analyze). Figure2. The importance of each source of information for innovation from the perspective of four HT industry sector subgroups Source: Author s research The next step of the analysis is to find how those linkages influence innovation activity. By using the probit modelling method it is possible to say which sources are real incentives and to describe some basic features of this kind of relationship such as its probablitity of occurance, its strenght and influence (positive or negative) or what kind of information or knowledge flows 5

6 (market information or scientific knowledge). 55 out of 198 models are statistically significant. They represent 27,78% of total models. Table 2 includes all statistically significant models for suppliers, customers and competitors. All models have a positive parameter (a >0) what indicates a positive influence. The numbers in brackets describe the probability of performing a given innovative action. Thanks to suppliers, companies are more often open for innovative cooperation (0,62), including innovative cooperation with suppliers (0,42), customers (0,36) and competitors (0,10). Customers help companies to make positive decisions in the area of implementation of new technology processes (0,81), including implementation of non production systems (0,45). Thanks to customers firms are more often willing to cooperate both with customers (0,35) and competitors (0,08). Links with competitors are facilitated by competitors (0,12). Table 2. Probit models describing linkages between innovation activities and suppliers, customers and competitors Innovation activity Suppliers Customers Competitors Implementation of new technology processes Par Std P 1 P 2 Par Std P 1 P 2 Par Std P 1 P 2 ---,34,14,81, b) non production systems ---,38,13,45, Cooperation with suppliers,68,15,42, Cooperation with competitors,64,22,10,03,49,23,08,03,66,22,12,03 Cooperation with customers,41,14,36 21,55,14,35, Overall cooperation,31,14,62, Source: Author s research. Three out of 18 models for internal sources of information for innovation are statistically significant. They are introduced in Table 3. Internal sources of information help companies to invest in buildings and grounds (0,32), implement of non production systems (0,45) and they facilitate the cooperation with customers (0,35) as well. Table 3. Probit models describing linkages between innovation activities and internal sources of information for innovation Innovation activity Internal sources of information for innovation Par Std P 1 P 2 Investments in buildings and grounds,37,15,32,20 Implementation of non production systems,28,14,45,34 Cooperation with customers,38,14,35,22 Source: Authors research. All statistically significant models for "national R&D units" and "foreign R&D units" are presented in Table 3. The influence of foreign R&D units is slightly greater than national R&D units(7 models vs. 6 models). National R&D units influence is the most valuable for overall innovative cooperation (0,93), including cooperation with Polish Academy of Sciences units (0,20), cooperation with both national (0,67) and foreign (0,47) R&D units. Furthermore, national R&D units positively impact on implementation of non production systems (0,73). Foreign R&D units influence concerns mainly international technology transfer. Firms more often implement new products (0,80), new technology processes (0,91), including production methods (0,64) and non production systems (0,60). They also invest in R&D (0,72). 6

7 Table 4. Probit models describing linkages between innovation activities and national R&D units and foreign R&D units Innovation activity National R&D units Foreign R&D units Par Std P 1 P 2 Par Std P 1 P 2 R&D expenditure ---,58,19,72,51 Implementation of new products 1,11,50,93,65,49,21,80,64 Implementation of new technology processes ---,73,25,91,73 Implementation of production methods ---,54,19,64,42 Implementation of non production systems,96,35,73,37,65,19,60,34 Cooperation with Polish Academy of Sciences units,89,39,20, Cooperation with national R&D units 1,50,34,67,14,46,20,27,14 Cooperation with foreign R&D units 2,31,39,47,01 1,75,39,16,00 Overall cooperation 1,45,50,93, Source: Author s research. Table 5 includes all statistically significant models for Polish Academy of Sciences units and universities. The most significant source of information for innovation is the Polish Academy of Sciences units network. Its impact concerns 12 out of 18 innovation activities. Particularly high influence is seen in the area of total investments in new fived assets (0,95), implementation of new technology processes (0,95) and overall innovation cooperation (0,86). The influence of universities is also quite high and relates to implementation of new technology processes (0,91), including production methods (0,63) and supporting systems (0,49). Universities facilitate overall innovation cooperation (0,83), including cooperation with universities (0,37) and national R&D units (0,31). As the result of their activity companies more often invest in building and grounds (0,46). Table 5. Probit models describing linkages between innovation activities and Polish Academy of Sciences units and universities Innovation activity Polish Academy of Sciences units Universities Par Std P 1 P 2 Par Std P 1 P 2 Total investments in new fixed assets,96,47,95, Investments in buildings and grounds,80,28,52,23,65,22,46,22 Investments in computer software 1,13,47,95, Implementation of new products,93,38,90, Implementation of new technology processes 1,02,47,95,74,73,31,91,74 Implementation of production methods,58,29,66,44,49,23,63,44 Implementation of non production systems,92,30,71, Implementation of supporting systems,81,29,62,30,47,22,49,31 Cooperation with Polish Academy of Sciences units 1,94,31,48, Cooperation with universities,70,32,24,08 1,24,24,37,06 Cooperation with national R&D units 1,27,29,57,14,56,24,31,15 Overall cooperation 1,02,34,86,52,93,26,83,51 7

8 Source: Author s research. Table 6 includes probit models describing linkages between innovation activities and conferences, fairs, exhibitions, journals and trade magazines and scientific societies. One of the most influential sources of information for innovation are scientific societies. Their impact is particularly visible in the area of investments in technical equipment and machinery (0,83) and implementation of new technology processes (0,88), including non production systems (0,61) and supporting systems (0,43). They help companies to cooperate with Polish Academy of Sciences units (0,10) and with universities (0,16). Conferences, fairs and exhibitions are less influential, but their impact is particularly important for investments in technical equipment and machinery (0,67). Journals and trade magazines support implementation of non production systems (0,46) and cooperation with customers (0,36). Table 6. Probit models describing linkages between innovation activities and conferences, fairs, exhibitions, journals and trade magazines and scientific societies Innovation activity Conferences, fairs, exhibitions Journals and trade magazines Scientific societies Par Std P 1 P 2 Par Std P 1 P 2 Par Std P 1 P 2 Investments in technical equipment and machinery,32,14,77,67 ---,50,19,83,67 Implementation of new technology processes ,61,20,88,72 Implementation of non production systems,33,13,46,33,32,14,46,34,74,16,61,32 Implementation of supporting systems ,36,16,43,30 Cooperation with Polish Academy of Sciences units,51,23,08,03 ---,57,24,10,03 Cooperation with universities ,46,20,16,07 Cooperation with national R&D units,40,16,22, Cooperation with customers ---,46,14,36, Source: Author s research. Conclusion The overall number of likages with all kind of information sources is 1036 what gives 2,77 linkages per one enterprise. The greatest number of dependences concerns customers (195), conferences, fairs and exhibitions (153) and internal sources (133). The lowest number of linkages concerns national R&D units (15), Polish Academy of Sciences (21) and universities (35). It suggests that national academic system is not an attractive partner for the HT industry and the most important role play customers. Further analysis shows differences in nature of linkages among different subgroups of HT industry. The highest average number of connections relates to manufacturers of basic pharmaceutical products and pharmaceutical products and pharmaceutical preparations (3,07), whereas the lowest number concerns manufacturers of instruments and appliances for measuring, testing and navigation; watches and clocks (2,66). Among the most influential sources of information in stimulating high technology industry are: Polish Academy of Sciences units (12 statistically significant models), foreign R&D units (7 models) and universities (7 models). National R&D units (6 models) and scientific societies (6 models) are also inspiring. The least influential source of information are competitors (1 model). The type of the source of information for innovation impacts on the character of derived information and kind of facilitated innovation activity. Customers, suppliers and competitors supply market information that concerns innovation cooperation. Internal sources also supply market knowledge and in this way help companies to invest in buildings and grounds, implement of non production systems and cooperate with customers. Both technology and scientific knowledge are supplied by 8

9 national and foreign R&D units, but foreign R&D units support R&D expenditure as well. The most significant source of information for innovation is the Polish Academy of Sciences units network. Its impact concerns 12 out of 18 innovation activities. The influence of universities is also quite high. The scientific societies play the important role as they support both investments and implementation processes as well. By comparing the structure of linkages to sources with the structure of (statistically significant) linkages to innovation activities we see that in case of HT industry in Poland low number of linkages generates high influence (information & knowledge transfer). In case of Polish Academy of Sciences units 2,03% linkages is responsible for 21,05% of total innovation activities linkages whereas customers18,82% linkages generates only 7% activities, so the hypothesis that the more linkages the enterprise has got the more innovation activities it presents is false. The key factor is nature of the linkage, what requires further research. References 1. Chandy, R., Hopstaken, B., Narasimhan, O., & Prabhu, J From invention to innovation: Conversion ability in product development. Journal of Marketing Research, Chesbrough, H Open innovation: A new paradigm for understanding industrial innovation. In Open Innovation: Researching a New Paradigm, ed. H. Chesbrough, W.Vanhaverbeke, and J. West, Oxford: Oxford University Press. 3. Dierkes M Visions, Technology, and Organizational Knowledge: An Analysis of the Interplay between Enabling Factors and Triggers of Knowledge Generation in: John de la Mothe and Dominique Foray (ed.), Knowledge Management in the Innovation Process, Kluwer Academic Publishers, Boston, p Dolińska M Innowacje w gospodarce opartej na wiedzy, PWE, Warszawa, p Dzikowski P The impact of information sources for innovation on stimulating innovation activity of food and beverage manufacturers in Western Poland In: Business development opportunities (ed.) Kardas J.S., Brodowska-Szewczuk J., University of Natural Sciences and Humanities Publishing House, Siedlce: Grimpe, C, Sofka, W Search patterns and absorptive capacity: Low- and high-technology sectors in European countries. Research Policy 38 (3): Harryson, S., Kliknaite, S., and Dudkowski, R Flexibility in innovation through external learning: Exploring two models for enhanced industry-university collaboration. International Journal of Technology Management 41 (1-2): Hatzichronoglou T. 1996, Revision of the high-technology sector and product classification, OECD, Paris, p Hauser, J. R., Tellis, G. J., & Griffin, A Research on innovation: A review and agenda for marketing science. (11-June), Retrieved May from SSRN: Janasz W Innowacje w strategii rozwoju organizacji w Unii Europejskiej, Difin, Warszawa: Laforet, S. 2011, A framework of organizational innovation and outcomes in SMEs, International Journal of Entrepreneurial Behaviour & Research, Vol. 17 No. 4: Lawson, B. and Samson, D Developing innovation capability in organizations: a dynamic capabilities approach, International Journal of Innovation Management, Vol. 5 No. 3: Lim, K, Chesbrough, H, Ruan, Y Open innovation and patterns of R&D competition. International Journal of Technology Management 52 (3-4): Link, A.N., and Rees, J Firm size, university based research, and the returns to R&D. Small Business Economics 2 (1): Lipiec-Zajchowska M. (Ed.) 2003, Wspomaganie procesów decyzyjnych. Ekonometria, Wyd. C.H. Beck. Warszawa: Love, J., Roper, S The determinants of innovation: R&D, technology transfer and networking effects. Review of Industrial Organization, 15, Maddala G. S Ekonometria, PWN, Warszawa, p Miotti, L. and Sachwald, F Co-operative R&D: why and with whom? An integrated framework of analysis. Research Policy, 32: Morrison, P.D., Roberts, J.H., and von Hippel, E Determinants of user innovation and innovation sharing in a local market. Management Science 46 (12): Narula, R., and Hagedoorn, J Innovating through strategic alliances: Moving towards international partnerships and contractual agreements. Technovation 19 (5):

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