How inter-firm networks influence the development of agglomerations

Non-market interactions are increasingly regarded as key explanations for spatial concentration. Consistently, both innovation and local knowledge spillovers play a central role in recent theories of agglomeration. According to these theories, exchange of localised knowledge gives firms an innovative advantage which results in better economic performance. However, it has turned out to be difficult to open the black box of economies of scale using empirical tests.\ud Since interactions get considerable attention in recent agglomeration theory, social network methods and theory are promising approaches to research spatial agglomerations. Even more so because simultaneously, there is an increasing emphasis on interfirm ties in the network field.\ud The goal of our research is to explore how interfirm networks influence the development of agglomerations. Firstly we provide a review on network and innovation literature in the field of spatial clusters. Secondly, we discuss measurement issues related to networks and innovation and ways to overcome them. Finally, we present preliminary results of our network study among high tech firms in the Dutch region of Twente

A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

Knowledge Flows and Capability Building in the Indian IT Sector: A Comparative Analysis of Cluster and Non-Cluster Locations

The role of industrial clusters in the industrialization of many emerging economies continues to dominate the debate among policy makers and researchers worldwide. While recent discussions on this debate have focused on knowledge spillovers among participants within clusters, knowledge flows between non local networks and the cluster actors have not been accorded due attention in the literature. Further, the literature does not compare the relative impact of knowledge flows among firms within clusters and firms outside clusters. In this study, we attempt a comparative analysis of the role of knowledge flows in capability formation among firms in the Indian Information Technology sector (IT sector) across cluster and non-cluster locations. The empirical results suggest that at the firm level, leveraging of capabilities to enhance performance and networks to build capabilities is not automatic; structural features of the firms’ location enable this transformation. Moreover, while capabilities affect performance of firms positively only in clusters, economies of scale and some strategies like quality certification used by firms impact performance of firms outside clusters. Interestingly, although economies of scale do not impact the performance of firms within clusters, they do, however affect the capability formation of firms within clusters only. Further, we found that local and national non-customer networks affect capability formation of firms within and outside clusters whereas international customer networks affect capability formation of firms within clusters only. These have implications for how firms can develop appropriate strategies to enhance their performance.Industrial Clustering, Information Technology industry, Networks, Capabilities

Practical service placement approach for microservices architecture

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. To reduce the complexity of service deployment, community micro-clouds have recently emerged as a promising enabler for the delivery of cloud services to community users. By putting services closer to consumers, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of the services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, it requires of aPeer ReviewedPostprint (author's final draft

Scars of early non-employment for low educated youth: evidence and policy lessons from Belgium

This paper investigates whether the early experience of non-employment has a causal impact on workers’ subsequent career. The analysis is based on a sample of low educated youth graduating between 1994 and 2002 in Flanders (Belgium). To correct for selective incidence of non-employment, we instrument early non-employment by the provincial unemployment rate at graduation. Since the instrument is clustered at the province-graduation year level and the number of clusters is small, inference is based on wild bootstrap methods. We find that one percentage point increase in the proportion of time spent in non-employment during the first two and a half years of the career decreases annual earnings from salaried employment six years after graduation by 10% and annual hours worked by 7% (unconditional effects). Thus, any policy that prevents unemployment in the first place will be beneficial. In addition, curative policies at the micro level may be required, depending on the actual cause of the scar

SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions

Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated services to users and meet their quality expectations. Existing resource management systems in data centers are yet to support Service Level Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to realize cloud computing and utility computing. In addition, no work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. This paper presents vision, challenges, and architectural elements of SLA-oriented resource management. The proposed architecture supports integration of marketbased provisioning policies and virtualisation technologies for flexible allocation of resources to applications. The performance results obtained from our working prototype system shows the feasibility and effectiveness of SLA-based resource provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE International Conference on Cloud and Service Computing (CSC 2011, IEEE Press, USA), Hong Kong, China, December 12-14, 201

Logistics outsourcing and 3PL selection: A Case study in an automotive supply chain

Outsourcing logistics functions to third-party logistics (3PL) providers has been a source of competitive advantage for most companies. Companies cite greater flexibility, operational efficiency, improved customer service levels, and a better focus on their core businesses as part of the advantages of engaging the services of 3PL providers. There are few complete and structured methodologies for selecting a 3PL provider. This paper discusses how one such methodology, namely the Analytic Hierarchy Process (AHP), is used in an automotive supply chain for export parts to redesign the logistics operations and to select a global logistics service provider

Parallel detrended fluctuation analysis for fast event detection on massive PMU data

("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment