آیا موقعیت خوشه ای برای عملکرد شرکت های فن آوری پیشرفته حائز اهمیت است؟ مورد فن آوری زیستی در هلند
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20030||2007||16 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Technological Forecasting and Social Change, Volume 74, Issue 9, November 2007, Pages 1681–1696
Young entrepreneurial companies in biotechnology tend to cluster in space, nearby research universities and research centers. This pattern is often ascribed to the availability of external economies, mainly local knowledge spillovers that help to reduce the uncertainty from a disruptive technology faced by these companies. Given a shortage of empirical research on cluster advantages and performance of clustered companies, we present results of a comparative analysis of clustered and non-clustered companies in biotechnology and Bionanotechnology in the Netherlands. It appears that, among other influences, a clustered location has no significant influence on innovation and speed of growth. However, a location in the largest cluster (Leiden) does contribute to a better performance in terms of innovativeness compared with all other locations. The kind of external economies involved seems to vary according to the stage in the knowledge value chain and the segment in biotechnology industry. Knowledge spillovers tend to be local for companies involved in new drugs and diagnostics research only in the first stage of the knowledge chain and for service companies regardless of the stage in the knowledge chain.
No other concept in regional economics and policy seems so often used and yet so poorly understood on the basis of systematic research than the cluster concept. This particularly holds true for one of the proposed underlying benefits of a clustered location, i.e. local knowledge spillovers and local learning processes. Advantages of agglomeration or clustering have been argued theoretically and empirically, starting with Marshall in the late 1880s in an economic tradition and “reinvented” by authors of the new industrial district since the late 1980s (see, for example, ). Advantages of agglomeration are rooted in three sets of economies: specialized inputs from various intermediate and subsidiary industries, a pool of skilled labor; and a dedicated infrastructure and other collective resources. In particular approaches (“learning regions”), a strong attention has been given to knowledge spillovers and tacit knowledge flows as a result of spatial proximity and networking. In this line of thinking the assumption is that companies benefit from a clustered location through meeting colleagues repeatedly and in person allowing for the exchange of tacit information ,  and . Clustering is not simply the co-location of a number of biotechnology or nanotechnology companies but it includes the ideas of regional competitive advantage, political drivers, educational activities, synergies between industry, government and academia, entrepreneurial activity, and interaction between large firms and small and medium-sized firms. Concepts of economic clusters emphasizing entrepreneurial efforts have been postulated by many authors. There are at least four popular approaches to economic development through clustering, each articulating different critical mechanisms and requirements for success. They include the entrepreneurial small firm (or Birch) model, drawing on ideas of Schumpeter ,  and , in which emphasis is put on R&D activity, entrepreneurship and on advocating a “bottom up” approach in policies for the enhancement of small business development. The latter is supported by the empirical evidence that the vast majority of net new jobs is generated not by existing companies but new ones. The role of small companies is also emphasized in the industrial district literature, mainly concerning traditional (artisanal) production but providing a clear model for networked high-technology production linked with flexible specialization  and . In this view, independently small companies in collaborative networks share the costs of developing new technologies and responding flexibly to new user demands, in a dense network of institutions and companies. This allows small companies to compete effectively in or aside from channels normally controlled by larger firms. Analysis of such clusters draws heavily on social capital theory  that views economic development as partly determined by cultural characteristics of a local community, particularly by supportive civic traditions. A second cluster approach – the Triple Helix model – centers on the relationships between universities, industry and governments as actors providing fertile seedbeds for knowledge utilization by industry . A first step in such relationships usually involves joint projects to enhance a local cluster or technopole. In such arrangements, the initial lack of fit between the Triple Helix partners urges them to partially take on the role and view of the other partners. Thus, one of the clearest changes perceived in recent times is a blurring of the edges between the functions of all three actors involved. For example, universities have become more entrepreneurial and companies have started knowledge production and education in their own campuses . In this situation, the development of incubators is one of the effective means to mediate the network among university, government and industry, and to contribute to regional-economic growth . A third approach is found in the concepts advocated by Porter . A cluster can be defined as a geographically proximate group of interconnected companies and associated institutions in a particular area, linked by communalities and complementarities, providing various economies. Following these concepts, the growth of regional clusters of excellence occurs when quality factors inputs (such as from universities, national laboratories, etc. in a particular application) are combined with demanding regional customers, competing (similar) firms and high-standard supplying and servicing companies; the latter enabling a kind of systems integration that produces values potentially perceived on a worldwide basis. A relatively new clustering concept is found in Florida's effort in explaining high levels of innovativeness in certain cities and regions . What is new in the concept is that not higher educated people but a broader class of creative professionals is seen as driving the local knowledge economy. The creative class, according to Florida, includes art writers, fashion designers, architects, actors, musicians, composers, engineers, mathematicians, painters, photographers, dancers, physicians, etc. This class prefers to live in or close to historical cities facing an abundant and differentiated supply of social and cultural services, and spends large amounts of consumer money, all contributing to a higher level of innovativeness and growth of the area. It seems that the previous ideas on the rise of the creative class have special application for Micro and Nano based clusters . Despite a growing literature, researchers have only just started to investigate with precision whether a clustered location is reflected in a better company performance and what the underlying mechanisms are. Notable exceptions are  and . Particularly, the assumption that spatial proximity promotes local learning and innovation in an exclusive way is not yet proven and remains challenged by several authors. Critics use the argument that highly specialized knowledge can also be transmitted between spatially distant actors, facilitated by organizational and social networks, like in communities of practice and strategic alliances, leading to benefits in clusters from a co-existence of local knowledge spillovers and global knowledge networks  and . Biotechnology is a generic technology encompassing technologies connected to recombinant DNA techniques and cell fusion . In more recent definitions the concept of biotechnology is broadened with areas like combinatorial chemistry and genome analysis. In the context of human health care, the latter aims to depict the genetic sequence variation in humans — as the basis for variation in risks among individuals for medically important diseases. Genomics, in terms of functioning of genes, functioning of proteins and metabolism in the cell, together with systems biology constitute an important technological trend today  and . In addition, biotechnology in health care increasingly connects with three other generic technologies, namely (1) informatics and computational science, e.g. in data-mining in searching of new hits and in processing genomics data, but also in remote diagnostics and clinical trials; (2) new materials technology, for example, in new types of artificial bone and tissues; and connected with the latter, (3) nanotechnology, e.g. in bio-sensors and nanotubes. Bionanotechnology is a disruptive technology providing new ways of diagnostics and treatment (like through new dosage systems) and enables a better use of artificial implants , ,  and . The combination and integration of the previous technologies with biotechnology open ways in a disruptive manner to entirely new applications in products, processes and services, in terms of scale, processes and function, and provide a wide range of new business opportunities. Aside from large opportunities there are also threats and a manifold uncertainty, usually all hazardous to new firm survival and much stronger than in other high-technology sectors . A main threat is failure of research, particularly in the development of modern drugs because of their long development and testing time — including approval by regulatory authorities. Research-intensive biotechnology companies are often clustered around public scientific institutes as knowledge and facility providers, like universities, research hospitals and research laboratories, and (mainly in the US) also around venture financing , ,  and . The acceptance of the existence of cluster advantages is clearly visible in many national and regional policies in Europe to enhance new firm formation in biotechnology. We may mention the BioRegio initiative in Germany  and the Action Plan Life-Sciences in the Netherlands . However, what is true for empirical work on cluster advantages in general – as addressed by Cumbers and MacKinnon  – is with a few exceptions  and  also true for biotechnology clusters. Thus, there is a shortage of studies in which the theoretical and policy claims of the cluster concept in biotechnology are systematically and critically evaluated. Against this background we address the following questions: (1) to what extent are cluster advantages manifesting themselves in the actual performance of companies, (2) what is the influence of a clustered location among other factors and which benefits are involved, and (3) are clusters to be seen as homogeneous or heterogeneous in this respect? We examine the above questions on the basis of an empirical study drawing on data concerning all clustered and non-clustered entrepreneurial companies in biotechnology in the Netherlands. We explore differences in innovative level and growth of these companies and analyze the kind of cluster advantages for these companies. To this purpose we adopt the perspective of the knowledge value chain that differentiates between three types of knowledge: knowledge for exploration, examination (testing) and exploitation . The paper is structured as follows. First, we discuss relevant notions from resource dependence theory and attempt to link the needs for resources of companies with the supply of particular resources in clusters in a theoretical way (Section 2). This is followed by an introduction to the biotechnology sector in the Netherlands (Section 3). We then discuss the research methodology (Section 4) and present the results of our comparative analysis and regression modeling (Section 5). Further, we attempt to identify the benefits of a clustered location for biotechnology companies, particularly knowledge spillovers while adopting the perspective of knowledge value chains (Section 6). We conclude the paper with some implications for policymaking and indicate future research needs (Section 7).