41,276 research outputs found

    Thinking locally: Exploring the importance of a subsidiary-centered model of FDI-related spillovers in Brazil

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    This paper investigates FDI-related spillovers in Brazil for the period 1996-2005. In contrast to most previous recent studies, which have failed to identify any significant effects in emerging economies, we found that horizontal spillovers did arise in Brazil. However, they did not arise simply as a consequence of general FDI-mediated technology transfer from MNC headquarters, as the standard approach presumes. Nor were they associated with expected inter-industry differences in technological intensity, or with differences in domestic firms’ absorptive capability. Instead, spillovers were associated with the existence of particular kinds of localized knowledge-creation activities undertaken by subsidiaries. We discuss the theory and policy implications that emerge from these results.FDI spillovers, subsidiaries, heterogeneity, localized innovation, Brazil, productivity, innovation

    Opportunities for greater Lincolnshire's supply chains: full report

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    A study of the key sector supply chains across Greater Lincolnshire, and identification of barriers and opportuniteis for growth

    Company R&D and University R&D - How Are They Related?

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    At the same time as we can observe strong tendencies of a globalisation of R&D, we also can observe a strong spatial clustering of R&D and related innovative activities. The standard explanation in the literature of the clustering of innovative activities is that such clusters offer external knowledge economies to innovative companies, since they are dependent upon knowledge flows and that knowledge flows are spatially bounded. Obviously, location is crucial in understanding knowledge flows and knowledge production, since knowledge sources have been found to be geographically concentrated. There are two major performers of R&D: industry and universities. It seems rather straight-forward to assume that industrial R&D might be attracted to locate near research universities doing R&D in fields relevant to industry. Already as far back as in the 1960s a number of case studies confirmed the important roles played by Stanford University and MIT for commercial innovation and entrepreneurship. During the years a large number of formal studies have presented evidences of a positive impact of university R&D on firm performance. The question is, does it also work the other way around? Does industrial R&D function as an attractor for university R&D? We may actually think of several reasons why university R&D may grow close to industry R&D. First of all political decision-makers may decide to start or expand university R&D at locations where industry already is doing R&D. Secondly, we can imagine that industry doing R&D in a region might use part of their R&D funds to finance university R&D. Thirdly, universities in regions with industrial R&D might find it easier to attract R&D funds from national and international sources due to co-operation with industry. Obviously, not all types of university R&D attract industrial R&D. There are reasons to believe that, in particular, university R&D in natural, technical and medical sciences attracts industrial R&D but that there are also strong reasons to believe that there are variations between different sectors of industry regarding how dependent their R&D is to be located close to university R&D. The above implies that there are behavioural relationships between industrial R&D and university R&D and vice versa. However, the litrature contains few studies dealing with this problem. Most studies have concentrated on the one-directional effect from university R&D to industrial R&D and the outputs of industrial R&D in most cases measured in terms of the number of patents and neglected the possible mutual interaction. However, if there is a mutual interaction between university and industry R&D, and if there are knowledge externalities involved, then we can develop a dynamic explanation to the clustering of innovative activities based on positive feedback loops. This would imply strong tendencies to path dependency and that policy initiatives to transfer non-innovative regions to innovative regions would have small chances to succeed. The fact that knowledge flows seem to be spatially bounded implies that proximity matters. Most contributions analysing spatial knowledge flows have used very crude measures of proximity. However, there are some authors that have argued that proximity could be measured using accessibility measures. Accessibility measures can be used to model interaction opportunities at different spatial scales: local, intra-regional and inter-regional. The purpose of this paper is to analyse the locational relationship between industry R&D and university R&D in Sweden using a simultaneous equation approach and to analyse existing differences between different science areas and different industry sectors.

    The countryside in urbanized Flanders: towards a flexible definition for a dynamic policy

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    The countryside, the rural area, the open space, … many definitions are used for rural Flanders. Everyone makes its own interpretation of the countryside, considering it as a place for living, working or recreating. The countryside is more than just a geographical area: it is an aggregate of physical, social, economic and cultural functions, strongly interrelated with each other. According to international and European definitions of rural areas there would be almost no rural area in Flanders. These international definitions are all developed to be used for analysis and policy within their specific context. They are not really applicable to Flanders because of the historical specificity of its spatial structure. Flanders is characterized by a giant urbanization pressure on its countryside while internationally rural depopulation is a point of interest. To date, for every single rural policy initiative – like the implementation of the European Rural Development Policy – Flanders used a specifically adapted definition, based on existing data or previously made delineations. To overcome this oversupply of definitions and delineations, the Flemish government funded a research project to obtain a clear and flexible definition of the Flemish countryside and a dynamic method to support Flemish rural policy aims. First, an analysis of the currently used definitions of the countryside in Flanders was made. It is clear that, depending on the perspective or the policy context, another definition of the countryside comes into view. The comparative study showed that, according to the used criteria, the area percentage of Flanders that is rural, varies between 9 and 93 per cent. Second, dynamic sets of criteria were developed, facilitating a flexible definition of the countryside, according to the policy aims concerned. This research part was focused on 6 policy themes, like ‘construction, maintenance and management of local (transport) infrastructures’ and ‘provision of (minimum) services (education, culture, health care, …)’. For each theme a dynamic set of criteria or indicators was constructed. These indicators make it possible to show where a policy theme manifests itself and/or where policy interventions are possible or needed. In this way every set of criteria makes up a new definition of rural Flanders. This method is dynamic; new data or insights can easily be incorporated and new criteria sets can be developed if other policy aims come into view. The developed method can contribute to a more region-oriented and theme-specific rural policy and funding mechanism

    Natural Language Processing in-and-for Design Research

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    We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

    Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

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    Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions
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