773 resultados para Household economy
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The chapter examines the impact of international statbuilding efforts on political and economic dynamics in Kosovo from 1999 to 2011
The impact of information and communications technology on commercial real estate in the new economy
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Purpose – This paper seeks to critically review the conceptual frameworks that have been developed for assessing the impact of information and communications technology (ICT) on real estate. Design/methodology/approach – The research is based on a critical review of existing literature and draws from examples of previous empirical research in the field. Findings – The paper suggests that a “socio-technical framework” is more appropriate to examine ICT impact in real estate than other “deterministic” frameworks. Therefore, ICT is an important part of the new economy, but must be seen in the context of a number of other social and economic factors. Research limitations/implications – The research is based on a qualitative assessment of existing frameworks, and by using examples from commercial real estate, assesses the extent to which a “socio-technical” framework can aid understanding of ICT impact. Practical implications – The paper is important in highlighting a number of the main issues in conceptualising ICT impact in real estate and also critically examines the emergence of a new economy in the information society within the general context of real estate. The paper also highlights research gaps in the field. Originality/value – The paper deconstructs the myths of the “death of real estate” and “productivity increase means jobs loss”, in relation to office real estate. Finally, it examines some of the ways in which ICT is impacting on real estate and suggests the most important components for a future research agenda in the field of ICT and real estate impact, and will be of value to property investors, facilities managers, developers, financiers, and others.
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One central question in the formal linguistic study of adult multilingual morphosyntax (i.e., L3/Ln acquisition) involves determining the role(s) the L1 and/or the L2 play(s) at the L3 initial state (e.g., Bardel & Falk, Second Language Research 23: 459–484, 2007; Falk & Bardel, Second Language Research: forthcoming; Flynn et al., The International Journal of Multilingualism 8: 3–16, 2004; Rothman, Second Language Research: forthcoming; Rothman & Cabrelli, On the initial state of L3 (Ln) acquisition: Selective or absolute transfer?: 2007; Rothman & Cabrelli Amaro, Second Language Research 26: 219–289, 2010). The present article adds to this general program, testing Rothman's (Second Language Research: forthcoming) model for L3 initial state transfer, which when relevant in light of specific language pairings, maintains that typological proximity between the languages is the most deterministic variable determining the selection of syntactic transfer. Herein, I present empirical evidence from the later part of the beginning stages of L3 Brazilian Portuguese (BP) by native speakers of English and Spanish, who have attained an advanced level of proficiency in either English or Spanish as an L2. Examining the related domains of syntactic word order and relative clause attachment preference in L3 BP, the data clearly indicate that Spanish is transferred for both experimental groups irrespective of whether it was the L1 or L2. These results are expected by Rothman's (Second Language Research: forthcoming) model, but not necessarily predicted by other current hypotheses of multilingual syntactic transfer; the implications of this are discussed.
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This paper explores a segmentation of micro and small enterprises (MSEs) in developing countries within the formal/informal economy nexus that has wide-ranging implications for the targeting of base-of-the-pyramid initiatives and entrepreneurship theory. This proposed segmentation emerges from the analysis of a sample of Kenyan MSEs utilising current and prior business models; the antecedent influences shaping the business model; barriers to entry associated with knowledge, capital and skills; the degree of innovation or imitation evident in the business model linked to the nature of opportunity recognition; and their relationship with the formal institutional business environment.
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We present an efficient graph-based algorithm for quantifying the similarity of household-level energy use profiles, using a notion of similarity that allows for small time–shifts when comparing profiles. Experimental results on a real smart meter data set demonstrate that in cases of practical interest our technique is far faster than the existing method for computing the same similarity measure. Having a fast algorithm for measuring profile similarity improves the efficiency of tasks such as clustering of customers and cross-validation of forecasting methods using historical data. Furthermore, we apply a generalisation of our algorithm to produce substantially better household-level energy use forecasts from historical smart meter data.
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As low carbon technologies become more pervasive, distribution network operators are looking to support the expected changes in the demands on the low voltage networks through the smarter control of storage devices. Accurate forecasts of demand at the single household-level, or of small aggregations of households, can improve the peak demand reduction brought about through such devices by helping to plan the appropriate charging and discharging cycles. However, before such methods can be developed, validation measures are required which can assess the accuracy and usefulness of forecasts of volatile and noisy household-level demand. In this paper we introduce a new forecast verification error measure that reduces the so called “double penalty” effect, incurred by forecasts whose features are displaced in space or time, compared to traditional point-wise metrics, such as Mean Absolute Error and p-norms in general. The measure that we propose is based on finding a restricted permutation of the original forecast that minimises the point wise error, according to a given metric. We illustrate the advantages of our error measure using half-hourly domestic household electrical energy usage data recorded by smart meters and discuss the effect of the permutation restriction.
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What are the microfoundations of dynamic capabilities that sustain competitive advantage in a highly volatile environment, such as a transition economy? We explore the detailed nature of these dynamic capabilities along with their antecedents by tracing the sequence of their development based on a longitudinal case study of an organization subject to an external context of radical transition — the Russian oil company, Yukos. Our rich qualitative data indicate two distinct types of dynamic capabilities that are pivotal for organizational transformation. Adaptation dynamic capabilities relate to routines of resource exploitation and deployment, which are supported by acquisition, internalization and dissemination of extant knowledge, as well as resource reconfiguration, divestment and integration. Innovation dynamic capabilities relate to the creation of completely new capabilities via exploration and path-creation processes, which are supported by search, experimentation and risk taking, as well as project selection, funding and implementation. Second, we find that sequencing the two types of dynamic capabilities, helped the organization both to secure short-term competitive advantage, and to create the basis for long-term competitive advantage. These dynamic capability constructs advance theoretical understanding of what dynamic capabilities are, whilst their sequencing explains how firms create, leverage and enhance them over time.
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In this article, we make two important contributions to the literature on clusters. First, we provide a broader theory of cluster connectivity that has hitherto focused on organization-based pipelines and MNE subsidiaries, by including linkages in the form of personal relationships. Second, we use the lens of social network theory to derive a number of testable propositions. We propose that global linkages with decentralized network structures have the highest potential for local spillovers. In the emerging economy context, our theory implies that clusters linked to the global economy by decentralized pipelines have potential for in-depth catch-up, focused in industry and technology scope. In contrast, clusters linked through decentralized personal relationships have potential for in-breadth catch-up over a range of related industries and technologies. We illustrate our theoretical propositions by contrasting two emerging economy case studies: Bollywood, the Indian filmed entertainment cluster in Mumbai and the Indian software cluster in Bangalore.
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We present projections of winter storm-induced insured losses in the German residential building sector for the 21st century. With this aim, two structurally most independent downscaling methods and one hybrid downscaling method are applied to a 3-member ensemble of ECHAM5/MPI-OM1 A1B scenario simulations. One method uses dynamical downscaling of intense winter storm events in the global model, and a transfer function to relate regional wind speeds to losses. The second method is based on a reshuffling of present day weather situations and sequences taking into account the change of their frequencies according to the linear temperature trends of the global runs. The third method uses statistical-dynamical downscaling, considering frequency changes of the occurrence of storm-prone weather patterns, and translation into loss by using empirical statistical distributions. The A1B scenario ensemble was downscaled by all three methods until 2070, and by the (statistical-) dynamical methods until 2100. Furthermore, all methods assume a constant statistical relationship between meteorology and insured losses and no developments other than climate change, such as in constructions or claims management. The study utilizes data provided by the German Insurance Association encompassing 24 years and with district-scale resolution. Compared to 1971–2000, the downscaling methods indicate an increase of 10-year return values (i.e. loss ratios per return period) of 6–35 % for 2011–2040, of 20–30 % for 2041–2070, and of 40–55 % for 2071–2100, respectively. Convolving various sources of uncertainty in one confidence statement (data-, loss model-, storm realization-, and Pareto fit-uncertainty), the return-level confidence interval for a return period of 15 years expands by more than a factor of two. Finally, we suggest how practitioners can deal with alternative scenarios or possible natural excursions of observed losses.