نشان هم افزایی در سیستم نوآوری نروژ کجا است ؟ روابط سه گانه مارپیچ میان فن آوری، سازماندهی و جغرافیا
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|4187||2013||14 صفحه PDF||سفارش دهید||8450 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Technological Forecasting and Social Change, Volume 80, Issue 3, March 2013, Pages 471–484
Using information theory and data for all (0.5 million) Norwegian firms, the national and regional innovation systems are decomposed into three subdynamics: (i) economic wealth generation, (ii) technological novelty production, and (iii) government interventions and administrative control. The mutual information in three dimensions can then be used as an indicator of potential synergy, that is, reduction of uncertainty. We aggregate the data at the NUTS3 level for 19 counties, the NUTS2 level for seven regions, and the single NUTS1 level for the nation. Measured as in-between group reduction of uncertainty, 11.7% of the synergy was found at the regional level, whereas only another 2.7% was added by aggregation at the national level. Using this Triple-Helix indicator, the counties along the west coast are indicated as more knowledge-based than the metropolitan area of Oslo or the geographical environment of the Technical University in Trondheim. Foreign direct investment seems to have larger knowledge spill-overs in Norway (oil, gas, offshore, chemistry, and marine) than the institutional knowledge infrastructure in established universities. The northern part of the country, which receives large government subsidies, shows a deviant pattern.
Innovation takes place in a landscape of interactions, collaboration, and knowledge exchanges among firms, academic institutions, and various government agencies . Firms and institutional agents cooperate and participate in networks at various geographical scales; locally, regionally, nationally, and internationally . Whether and how government interventions, or the presence of academia, matter for regional innovation is an issue of political significance in many countries because innovation in the regions is considered to be a condition for increasing prosperity ,  and . Accordingly, national and regional governments in several countries have developed programs and centers for enhancing innovation in the regions  and . A number of factors are important in this context: the industry structure , the role of the universities  and , the role of knowledge networks  and , proximity and localization , ,  and , and organization and culture  and . Leydesdorff and Meyer  raised the question of how to measure whether a knowledge base in the economy is developed more at the regional than the national level (or vice versa). Can something as elusive as the knowledge base of an economy be measured in terms of the interactions in a Triple Helix between economic development, organized knowledge production, and political control? The purpose of this paper is to estimate the characteristics of such Triple-Helix dynamics in the Norwegian innovation system. Combining the use of information theory and the Triple-Helix model of university–industry–government relations, we propose a tool for measuring the extent to which innovations have become systemic. Canter et al. , for example, used patent data from firms in three industrial regions to characterize the knowledge base of the regions. Our approach provides an empirical alternative to the a priori assumption that such systems would exist geographically either at the national or regional levels. We use an information-theoretical method on a complete set of micro-level data for all – that is, almost half a million – Norwegian firms registered during the last quarter of 2008. Each of these firms is attributed a municipality code (as a proxy for geography), a sector code (proxy for technology), and a size code for firms (proxy for organization). The study leans on three previous papers using a similar method, but containing data from the Netherlands , Germany , and Hungary . These studies have similarities in their methodological approach, but were different in several ways. The Hungarian study focused on firms from high-tech industries and knowledge-intensive services. The German study did not contain data about self-employed firms. The study of the Netherlands used postal codes instead of municipalities as the geographical proxy. Furthermore, the geography and the industry patterns in Norway are different from the other countries studied. The state can be expected to play a more active role in Norway than in the other countries for which similar studies were performed [1: p. 111]. This study broadens the picture from previous studies by including two new elements in the analysis. First, by including the geographical distribution of foreign factors  and , such as foreign direct investment and export incomes (at the county level). Second, by discussing the distribution of research funding among Norwegian counties. Following Leydesdorff et al. , we first combine the theoretical perspective of regional economics  with the Triple-Helix model . Three dimensions are thus distinguished: technology, geography, and organization. These dimensions cannot be reduced to one another, but interactions among them in networks of university–industry–government relations can be expected. The synergy in these interactions can be measured in the Norwegian innovation system and can also be decomposed at different levels of scale . The mutual information among the three dimensions (geography, technology, and organization) can be negative and can then be interpreted as an indicator of reduction of uncertainty or synergy. Lengyel and Leydesdorff  specified the synergetic functions as ‘knowledge exploration’ (between technology and geography), ‘knowledge exploitation’ (between technology and organization) and ‘organization control’ (between organization and geography). Spurious correlations among these interacting subdynamics of a knowledge-based system may reduce the uncertainty that prevails, and this reduction can be measured using the mutual information in three dimensions. Yeung  specified the resulting indicator as a signed information measure. A signed measure can no longer be considered as a Shannon entropy . When this signed information measure is negative, the synergy among the functions reduces uncertainty that prevails at the systems level. The synergy is an attribute to the configuration, and not of the composing subdynamics. It emerges as a virtual knowledge base that feeds back on the composing subdynamics. However, information theory allows for the precise decomposition into components of this knowledge base in terms of bits of information . We study the measure at four geographical levels: the national system (NUTS1),1 seven regions (NUTS2), 19 counties (NUTS3), and 430 municipalities (NUTS5). The results enable us to specify where synergy is highest and whether the respective innovation systems have more regional or national characteristics. Etzkowitz and Leydesdorff [1; p. 111] used Norway as an example for the Triple-Helix I model, where the strong state governs academia and industry. Onsager et al.  reported that the largest city regions in Norway seem to have limited capacity to utilize their resource advantages and potential synergy. Herstad et al.  concluded that firms in the capital region (Oslo) are less engaged in innovative collaboration than firms in the rest of the country, whereas Isaksen and Wiig Aslesen  argued that the knowledge organizations in Oslo do not (yet) function as hubs in a wider innovation system. The relations between innovation, policy, and inter-firm linkages in Norway were also discussed by Nooteboom . He concluded that central government should limit itself to facilitation in the formation of enterprise clusters. An OECD report , analyzing the roles of knowledge institutions in the Trondheim region, concluded that in spite of being Scandinavia's largest independent research institution and technical university, there is a need to ‘broaden the innovation dynamics’ and increasing the absorptive capacity within this region. The existence of fragmentation  and ‘parallel worlds’  within the Norwegian innovation system, can be considered as indications of redundancy rather than synergy. In this study, we address these Triple-Helix issues empirically by using data and information theory. We focus on the geographical decomposition of the configurations. The main research question is to find and explain geographical areas where synergy among the knowledge-based innovation functions is higher than in other areas. From a methodological perspective, it is interesting to study first the complete populations of firms, that is, without focusing on sectors or geographical areas which are a priori defined as relevant systems of innovation. The finely grained geographical mesh of the Norwegian firms allows us to estimate at which geographical levels synergies occur. Additionally, we relate our results to the geographical distribution of government spending on R&D and foreign factors in areas of high or low synergy. Finally, we also reflect and elaborate on some counter-intuitive results.
نتیجه گیری انگلیسی
When analyzing the Norwegian economy in terms of Triple-Helix synergies, we find a similar pattern at different geographical scales. These results suggest that the counties and regions that contribute most to the knowledge base of the Norwegian economy are located on the western coast of Norway and in Nordland. Within the framework of the Triple-Helix theory, these areas seem to have achieved a balance between the three sub-dynamics to a larger extent than other parts of the country. In the northern part of the country government intervention is so substantial that the dynamics of the economy are changed. This can best be seen by the lack of new small companies and the high level of public employees (40%) in these counties. The exception is Tromsø, the main university city in the north, where the number of start-ups is high. One of the reasons may be the government's focus on marine biotechnological research at this university. However, most of the marine industry is located in Vestlandet. Most of the research capacity in Norway is located in Oslo and Trondheim, in areas with weak industrial traditions. The industrial counties on the west coast are characterized by a strong internationally oriented manufacturing industry directed towards maritime, offshore and marine industry. These firms operate in global markets. The knowledge base is synthetic , with a low share of formal higher education. Møre og Romsdal contains the strongest industry cluster in Norway: the maritime cluster. The high-tech clusters, located in other parts of the country are probably too small to influence the synergy at the NUTS3 level significantly. At the NUTS2 level, the highest level of synergy is also found in Vestlandet. This shows that our results are robust against changes in the geographical scale. There are some interesting differences between the geographical influence on the results in the case of Norway or the Netherlands. Whereas in the data from the Netherlands , the geographical uncertainty is correlated with the number of firms in the region (r = 0.76), the Norwegian geographical uncertainty correlates negatively with the number of firms (r = − 0.61). However, this may be due to the fact that the geographical units used in the study from the Netherlands are postcodes in a country which is densely populated, in contrast to the Norwegian data which are based on a high number of municipalities with a varying size in a sparsely populated country. The units in the postcodes are expected to be chosen to ensure an efficient post distribution, with units as equal as possible. The borders of the municipalities in Norway are based on historical and geographic factors. This shows that comparisons between various geographical units should be done carefully. The comparison between public R&D expenditure and the synergy of the knowledge base provides another negative correlation in the case of Norway. In our opinion, these findings confirm the conclusions of Onsager et al.  and OECD  that areas in Norway with high concentrations of knowledge institutions (and hence a high level of higher education) seem to live in ‘separate’ worlds, uncoupled from the needs of the industry. Easy access to public research funding through networks and co-location with research councils and political decision makers makes the transaction costs of engagement with fellow academics lower than those with industry . At the national level, Shelton and Leydesdorff  found that high levels of private R&D funding promote cooperation with industry and results in a larger numbers of patents. A high level of public funded R&D results in an increased number of academic papers. This underpins the findings of Benner and Sandström  that institutionalization of a Triple-Helix model is critically dependent upon the form of research funding. There is also a tendency in the academic literature to fail to see the importance of innovation in ‘low-tech’ industries . Foreign factors, such as high FDI, foreign ownership and global customers, are characteristic for the regions and counties with the highest synergy. This may support what Bathelt et al.  called a ‘local buzz–global pipeline’ effect, that is, a combination of geographically embedded local knowledge with knowledge from global sources, filtered for relevance by global customers. The dominating industry sectors in these littoral counties are medium-tech manufacturing. Easy access to local tacit knowledge and international knowledge spillovers from customers may be more important than codified academic knowledge. Calculation of the inter-group synergy consequently indicates that synergy occurs at the regional, rather than at the national level. Our results support the findings from previous studies showing that medium-tech manufacturing rather than high-tech manufacturing is associated with synergy ,  and . Our results also show the effect of high levels of government intervention in the northern part of the country . In these regions, our measures were dependent on the scale of the aggregation (NUTS2 or NUTS3). However, public R&D funding is directed towards academic institutions in university cities, whereas regional policies are mostly directed towards the northern region and regions with little industry. The highest synergy in the knowledge functions in the Triple-Helix dynamics is to be found in the industrial counties on the west coast, where medium-tech manufacturing is concentrated and foreign factors associated with operating in global markets enhance synergy to a greater extent than expected.