On the Nature and Geography of Innovation and Interactive Learning: A Case Study of the Biotechnology Industry in the Aachen Technology Region, Germany

Abstract

So far, relatively little research has been done on sectoral differences of innovation processes. In order to learn more about these differences, we apply the knowledge base concept which helps us to characterize the nature of critical knowledge that is indispensable for innovation activities. Two knowledge bases are distinguished: the analytical (science based) and the synthetic (engineering based) knowledge base. This paper focuses on the emerging biotechnology industry in the Aachen Technology Region in Germany. It aims to identify the knowledge base which is crucial for the development of new products and processes. Additional questions are as follows: How intense are cross-sectoral knowledge transfers and labour mobility? In which way can we observe innovation-oriented systemic interactions within the region and to which extent are the biotechnology firms connected to extra-regional knowledge sources? In order to investigate these questions, we apply social network analyses and descriptive statistics. Our results show that the knowledge base that is crucial for innovation activities is primarily of analytical nature. Interactive learning of biotechnology firms within the region is clearly dominated by industry–university links, while the vertical dimension of co-operative innovation processes is rather shaped on national and global scales for most firms.

Acknowledgements

This research was sponsored by the European Science Foundation and the Research Council of Norway. An earlier version of this paper has been presented at the Fourth International Seminar on Regional Innovation Policies in Edinburgh, 2009. We have benefited from the useful comments made by the participants of this event and particularly by two anonymous reviewers, and by Jerker Moodysson. We would like to thank the firm representatives for spending their time with us. We are also thankful to Ramin Kawiani, Andreas Techen, Marco Trienes and Claudia Mourran for their assistance during the case study. The usual disclaimer applies, however.

Notes

This paper is written in the context of an international research project called “Constructing Regional Advantage: Towards State-of-the-Art Regional Innovation System Policies in Europe?” The project is co-funded by the European Science Foundation and the Research Council of Norway.

We also collected information on some basic characteristics of the four remaining firms that did not take part in our case study. Since none of them shows significant differences (with regard to size or age) compared with the other 23 firms, the results should be highly relevant for the regional biotechnology industry as a whole.

The German “Fachhochschulen” (engl. = universities of applied sciences) are comparable with technical colleges or polytechnics in other countries.

We are aware that innovation processes might differ between these different strands of biotechnology. However, we do not subdivide our sample any further in order to avoid a lack of representativeness due to low case numbers.

However, those results have to be interpreted carefully since standard deviation figures indicate high disparities regarding the specifications made by the firms.

Firms with no patents mentioned several reasons for not applying for patents so far. One firm indicated that all rights for the developed methods were contractually held by its customers. Even two firms argued that they did not apply for patents so far to protect themselves against unintended knowledge and technology transfer. Other reasons were that the levels of complexity and the costs of patent application processes (one mention in each case) were too high. Also, one young firm was totally at the beginning of the inventory process with no products or processes to hold the potential to be patented.

We distinguish two different types of organizations within both network illustrations. First there are knowledge-generating organizations (i.e. university institutes and public research organizations) and second there are knowledge-exploiting organizations (i.e. industrial companies) (Autio, 1998). This rather simplistic distinction highlights the crucial role that these organizations play from the perspective of the (regional) innovation system approach. The network figures roughly indicate a first impression of the high involvement of university institutes, or research laboratories that play a decisive role in providing the system with new knowledge which is relevant for biotechnology. However, we have to be aware of the fact that the production of new knowledge and problem solutions can also occur within and between collaborating firms, too, even though their role within the system of innovation is rather to exploit knowledge in order to enhance the potential for commercializing biotechnology products.

Degree centrality refers to the knowledge exchange links that one firm has with the other firms. In-degree centrality measures the extent to which knowledge is acquired by a firm from other local firms by counting the number of technological/market knowledge exchange ties incident to the firm. Out-degree centrality measures the extent to which knowledge originates from a firm to be used by other local firms by counting the number of ties incident from the firm. Both indicators are calculated in two ways: dichotomous and valued. The dichotomous indicator reflects the sheer presence/absence of a linkage, while the valued one analyses the value given to each linkage (i.e. importance of each link for the firm's innovative performance; a 1–5 range) by the knowledge-using firm. The centralization measures refer to the network populations as a whole and express the degree of inequality or variance in both networks as a percentage of that of a perfect star network of the same sizes. In the current case, for example, the dichotomous out-degree centralization of the inter-firm TKN within the region is 22%, while the in-degree centralization is 12% of the theoretical 100% centralization maximum. For further information on network centralization measures, see, for example, Borgatti et al. (1999); Freeman (1979), or Hanneman and Riddle (2005).

With regard to the MKN, this type of network contacts contains organizations such as business membership organizations, economic development and transfer agencies, sister and mother companies, co-operation partners (which consist, in principle, of firms that could not be definitely attached to single categories along the value chain), consultants, financial service providers and organizers of conventions and trade shows.