产业集群，知识整合和绩效外文翻译

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1、 中文 2380 字 Industrial Clusters, Knowledge Integration and Performance WHAT ARE INDUSTRIAL CLUSTERS? During the 1990s the explosion of specialized and popular literature on industrial clusters gave them an unprecedented relevance across a range of areas, including business management and economic, po

2、litical, public and social policy. There was also a degree of confusion over what the various authors meanand do not meanby industrial clusters. Our rst consideration therefore is terminology. It is important to point out from the outset that we are not concerned here with the kinds of economic aggl

3、omerations found in large cities and urban developments of a certain size. As various authors have noted, large urban realities of necessity and almost inevitably provide opportunities for agglomerations of sorts to emerge, human rst, social and economic next (Gordon &McCann, 2000). Indeed, it is ob

4、vious to those familiar with large cities and urban realities that economic interactions within these kinds of agglomerations are typically governed by the logic of large numbers and random events. But, two basic kinds of economic benets that are important to our understanding of industrial clusters

5、 can also usually be found here. On the one hand, large cities and similar agglomerations nurture urbanization economiesin other words, economic advantages that stem from factors or conditions that benet all economic entities and agents that are part of the agglomeration. For example, the impressive

6、 air transportation facilities and infrastructure of a city such as London, the strategic geographic location of Athens for westeast logistical links and the multiplicity of linguistic skills present in Singapore can lead to economic advantages that can be enjoyed by all entities located inor nearth

7、ese large cities. On the other hand, urban agglomerations lead to localization economies of scale. These are specialized economic advantages stemming from close geographic proximity that benet specic industries only. To follow the previous examples, the City of London is one of the worlds premier ce

8、nters of nancial talent in the form of tensperhaps hundredsof thousands of highly skilled nance professionals. This world-class talent pool presents obvious benets for all nancial services rms that decide to locate themselves in London. Similarly, Athens and its close surroundings is one of the worl

9、ds leading hubs of people, rms, assets and infrastructure specically related to the shipping industry. The same can be said of Singapore, except that its shipping hub is perhaps even larger than that of Athens, with a greater global reach. The idea of localized economies of scale in geographic agglo

10、merations has a long history in economics, going back to Adam Smiths early observations of labor specialization and to Marshalls (1925) explanations of why rms continue to localize in the same areas. Marshall highlighted three key explanations. First, rms get close together geographically because th

11、is allows them to develop a pool of specialized labor that is highly skilled for the specic needs of an industry and relatively easy for the rms in need of these skills to access. Second, these rms can provide nontraded input specic to an industry, i.e. by localizing themselves in close geographic p

12、roximity, the rms can experience economies of scale in developing and using common technologies or a particular capital infrastructure. Third, rms that join together geographically can generate a maximum ow of information and ideas. In other words, product, market and technological knowledge can be

13、more easily shared and more eectively turned into valuable innovations between agents that are in close geographic proximity than between agents that are more geographically dispersed. It is interestingand to some degree quiteparadoxicalthat virtual communication technologies and developments in glo

14、bal transportation and logistics during the 20th century have made localization economies morenotlesscritical to the competitive performance of rms. On the one hand, virtual communications and similar technologies have highlighted tacit knowledge and close personal relationships between economic age

15、nts as key determinants for the competitive success ofrms. On the other hand, global logistics mean that access to basic production factors such as capital and nonspecialized labor are largely open to all, whereas ows of specialized knowledge and rich knowledge interactions that lead to valuable inn

16、ovations remain stronger between agents in the same spatial group than among geographically dispersed rms. Our denition of industrial cluster includes the Marshallian notions of urbanization and especially localization economies of scale, but it clearly departs from the concept of agglomerations in

17、that the knowledge interactions within the cluster are not random but rather deliberate, socially constructed and determinant for its competitive survival: An industrial cluster is a socioeconomic entity characterized by a social community of people and a population of economic agents localized in c

18、lose proximity in a specic geographic region. Within an industrial cluster, a signicant part of both the social community and the economic agents work together in economically linked activities, sharing and nurturing a common stock of product, technology and organizational knowledge in order to gene

19、rate superior products and services in the marketplace. LINK BETWEEN KNOWLEDGE INTEGRATION, SCOPE OF COMPETITION AND THE PERFORMANCE OF CLUSTERS A broad array of existing empirical evidence (some of which is referenced in the previous sections) suggests that both the degree of knowledge integration

20、and the scope of competition are co-evolving factors that are crucial to explain the economic performance of industrial clusters. Although the empirical evidence remains slightly fragmented, it suggests that rms in industrial clusters that present a high degree of knowledge integration and compete g

21、lobally innovate more, present stronger growth patterns, adapt to changing environmental conditions more rapidly and have a more sustainable economic performance than rms in less integrated clusters that tend to compete within strictly local geographic boundaries (Meyer-Stamer, 1998； Porter, 1998； P

22、yke et al., 1990； Rabellotti, 1995； Schmitz, 2000； Simmie & Sennett, 1999). These kinds of empirical evidence underlie the following hypothesis: The higher the degree of knowledge integration between member rms, and the higher the global scope of competition of member rms, the higher the economic pe

23、rformance of industrial clusters. Figure 1 provides a graphic illustration of our hypothesized eects, postulating a comparative taxonomy of industrial clusters across a diversity of industries and geographies, which we assessed within the context of our research. This hypothesized taxonomy is includ

24、ed here for illustrative purposes only. It is based, however, on an examination of over 2,000 pages of archival data, academic and specialized publications as well as expert opinion gathered through a series of eld visits and interviews with industrial cluster agents (e.g., entrepreneurs, associatio

25、n representatives, practitioners) in southern Brazil, Brazils Amazon State and northern Italy. Both the literature review and the expert interviews we carried out were tightly structured around the templates and constructs outlined in Tables 1 and 2 (see Appendix B). Our analyses focused on the mid1

26、990s, for which a relatively large body of empirical data exists on industrial clusters along the following dimensions: degree of knowledge integration, scope of competition and economic performance (e.g., Becattini, 1990； Feser & Bergman, 2000； Gordon & McCann, 2000； Meyer-Stamer, 1998； Rabellotti,

27、 1999). Figure 1 thus illustrates a number of overall patterns that seem to emerge quite clearly from the growingalbeit fragmentedempirical literature on industrial clusters over the last two decades of the 20th century. These patterns unveil a multitude of characteristics that appear both to explai

28、n and determine the economic performance of industrial clusters. Some of these characteristics have to do with competitive factors that are inherent in the industrial sectors in which the clusters operate. Others have to do with factors concerning an industrial clusters institutional fabric, geograp

29、hic closeness, economic linkages and common glue, and here the scope for positive intervention is arguably greater for the macro-economic policy maker and the business planner alike. An empirical test of the hypothesis we have developed, remains, however, a challenging step for a better, holistic un

30、derstanding of the major factors that both explain and determine the economic performance of an industrial cluster. We suggest that this empirical test, conducted along the constructs developed in Tables 1 and 2, could contribute to this understanding in a way that encompasses the economic and social aspects that appear to be equally important to the competitive functioning of industrial clusters.