This research analyses whether German Machine Tool Builders headquartered in industrial clusters differ in patent activities from companies outside these regions. Therefore this paper provides standard references as well as current research activities in the field of cluster processes and examines if the theoretical approaches and characteristics also apply on a sample of 112 German Machine Tool Builders. Based on the German Patent and Trademark Office database the results show that cluster based and non-cluster based firms´ patent activities differ just insignificantly regarding the patent and utility model applications per year and per 1 Mio. € revenue. This may come from the decreasing importance of spatial proximity as knowledge seems to be well codified and therefore easily transferable in a mature stage of the industry lifecycle.
Cluster, Innovation, Knowledge transfer, Patent activity, German Machine Tool Builders
As globalization and modern information and communication technologies allow firms to source all over the world in order to exploit the competitive advantage of each nation for particular business functions, one may expect a bulk of extremely widespread and fragmented companies. Interestingly in some industrial sectors the prevalence of clusters shows a kind of paradox [Porter, 2000, 1998] [Audretsch, 1998]. Therefore numerous research initiatives focused on resolving this contradiction, on the reasons for existence of clusters as well as on their competitive advantages - mainly in the 1990s [Baptista & Swann, 1998].
Based on the literature review and the case study, the purpose and aim of this paper is on the one hand to examine if there is a linkage between geographical proximity and patent activity in general and on the other hand if this possible effect also applies on a particular industrial sector - namely the one of German Machine Tool Builders.
The literature review in chapter two analyses all relevant literature comprehensively, summarizes basic and current strands of research and provides common scientific findings in the fields of innovation in clusters, knowledge transfer and spillovers as well as patent activities. The third chapter argues and determines the case study as applied research design and database research as the chosen method of data collection. Subsequently detailed information about data cleaning, data usage and data evaluation is given. Therefore the research approach of the paper applies three filters on the basic sample and defines two kinds of companies which are cluster based and non-cluster based ones in order to compare their patent activities. The fourth chapter interprets the results of the data collection and evaluation. The discussion also refers to the second chapter and argues to what extent the results of the literature review apply on the paper´s findings. The conclusion gives a summary of the paper´s scientific contribution and presents fields in which further research activity is needed.
2 Literature Review
Nowadays competition is dynamic and based on innovation [Porter, 2000]. As innovation and technological change depend highly upon new economic knowledge which is neither a routine pattern nor standardized information, it´s essential to be informed about the latest changes and get access to the specialized know-how that individuals got through practice and mistakes [Desrochers, 2001] [Audretsch & Feldman, 1996].
As this kind of knowledge about current insights and know-how gained by trial and error cannot be acquired by conventional market research or transferred by long-distance learning, the nonubiquitous and context-specific nature of the so called tacit knowledge makes companies keen on going to regions where it´s produced and shared [Gertler, 2003] [Reagans & McEvily, 2003] [Desrochers, 2001]. And if tacit knowledge is decisive to the generation of innovative activity, geographic proximity matters the most [Audretsch & Feldman, 1996].
According to [Audretsch, 1998], tacit knowledge mainly occurs as a result of R&D activities and of high presence of scientists and engineers. Precisely [Audretsch, 1998] mentions the link of the particular connection between R&D and innovative output and states that it´s very strong in terms of either patents or new product innovations considering industry. Since universities and research centers provide scores of R&D activities as well as a high presence of scientists and engineers, [Ponds, et al., 2010] argue that these institutes are crucial for localized knowledge spillover due to their explicit focus on creating and diffusing new economic knowledge, where spillovers are defined as flows of ideas between agents at less than the original costs [Griliches, 1991]. Indeed [Acs, et al., 1994] and [Jaffe, 1989] present that there are spillovers of university and R&D center researches on local companies as well as a significant effect on local innovation by inducing industrial R&D spendings.
Based on literature reviews, [Ponds, et al., 2010] define three kinds of spillover mechanisms. First, while spin-offs commercialize academic knowledge, they tend to locate close to the parent organization, which results in a geographical agglomeration of these firms around universities and research institutes [Klepper, 2007] [Zucker, et al., 1994]. Second, mobile employees move from one company to another and hence transfer the knowledge embodied in them [Breschi & Lissoni, 2006, 2003]. Third, social networks, which are local to a large extent, enable informal knowledge exchange [Singh, 2005].
According to [Porter, 2000], “clusters are geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries, and associated institutions (e.g., universities, standards agencies, trade associations) in a particular field that compete but also cooperate.” Especially the combination of competition and cooperation assures that cluster based companies are maintaining their flexibility while benefiting like they had joined with each other formally [Porter, 1998]. Moreover [Desrochers, 2001], [Maskell & Malmberg , 1999] and [Hamel, et al., 1989] argue that spatially proximate related companies are in a kind of cooperative as well as competitive relationship which facilitates new business formation and the development of trust relationships. These trust relationships combined with geographic proximity allow and foster frequent face-to-face interactions between partners who share basic similarities e.g. same languages, common codes of communication, common conventions and norms, knowledge of each other based on former collaboration or informal interaction [Gertler, 2003].
Mobile employees within social networks based on trust relationships and the background of shared similarities build up social cohesion which results in greater willingness of individuals to share knowledge with others [Reagans & McEvily, 2003]. [Dahl & Pedersen, 2004] found out that especially engineers share even valuable knowledge with informal contacts.
[Baptista & Swann, 1999] compared clustering dynamics in U.S. and UK computer industries and found out that industry strength attracts new entrants and that firms based in strong clusters tend to grow faster. These facts increase the effect of highly skilled workers being available in just few areas of the world [Audretsch, 1998]. Combining these effect with the findings of [Audretsch & Feldman, 1996], saying that the more an industry workforce is composed of skilled workers, the higher the likelihood of knowledge spillovers gets, means that clusters reinforce themselves with ever more skilled workers, knowledge and innovations. However [Baptista & Swann, 1998] remind that attractiveness of clusters levels off, which leads to few regions sharing an industry in the long term.
In spite of the considered self reinforcing interactions within a cluster, it has to be pointed out that these effects also depend on the specific industry maturity level as well as the kind of new economic knowledge needed for innovation. The meaning of academic research activities for innovation differs considerably across industries [Cohen, et al., 2002] [Klevorick, et al., 1995]. Especially science based industries, e.g biotechnology and semiconductors depend on scientific knowledge for innovative activities, investing large sums in R&D and collaborate intensively with academia [Ponds, et al., 2010]. [Audretsch & Feldman, 1996] call attention to the phenomenon of innovative activities tending to disperse at a mature and declining life cycle stage. Also [Baptista & Swann, 1998] mention that the simpler and the more codified the knowledge of a particular industry is, the easier its spatial transfer gets, as it results in less importance of geographical concentration for innovators. [Bathelt, et al., 2004] findings confirm the suggestions that codified knowledge may travel much easier.
3 Research design and method
The underlying research method of this paper considers a case study comprising a sample of 112 firms in order to examine if innovative activity based on clustering also applies on congestions of German Machine Tool Builders. The sample results from the members list of the German Machine Tool Builders´ Association, as this list reflects all major companies in this particular industry comprehensively.
Even if not all innovative activities lead to patent applications, the patent activity represents a quantifiable propensity of innovative activity up to a certain degree. Therefore the amount of patent and utility model applications per year was chosen as the key performance indicator in order to measure and compare patent activity of German Machine Tool Builders. This industrial sector is object of research on the one hand due to its considerable contribution of success to the German economy and on the other hand due to the availability of broad and solid data [German Machine Tool Builders’ Association, 2015].
- Quote paper
- Tobias Mayer (Author), 2015, Do cluster based companies differ in patent activity to non-cluster based ones? A Case study of German Machine Tool Builders, Munich, GRIN Verlag, https://www.grin.com/document/307440