21 resultados para intangible assets

em CentAUR: Central Archive University of Reading - UK


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Measuring poverty has occupied a lot of space in the development discourse. Over the years a number of approaches have been offered to capture the experience of what it means to be poor. However, latterly such approaches often ignore core assets. Indeed, the comparative impact of livestock vs. other core assets such as land and education on poverty has not been well explored. Therefore, the authors created an 'asset impact model' to examine changes to both tangible and intangible assets at the household level, with a particular focus on gender and ethnicity among communities residing in the Bolivian Altiplano. The simple model illustrates that for indigenous women, a 20 per cent increase in the livestock herd has the same impact on household income as increasing the education levels by 20 per cent and household land ownership by 5 per cent. The study illustrates the potential role of a productive, tangible asset, i.e. livestock, on poverty reduction in the short term. The policy implications of supporting asset-focused measures of poverty are discussed.

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There are a number of challenges associated with managing knowledge and information in construction organizations delivering major capital assets. These include the ever-increasing volumes of information, losing people because of retirement or competitors, the continuously changing nature of information, lack of methods on eliciting useful knowledge, development of new information technologies and changes in management and innovation practices. Existing tools and methodologies for valuing intangible assets in fields such as engineering, project management and financial, accounting, do not address fully the issues associated with the valuation of information and knowledge. Information is rarely recorded in a way that a document can be valued, when either produced or subsequently retrieved and re-used. In addition there is a wealth of tacit personal knowledge which, if codified into documentary information, may prove to be very valuable to operators of the finished asset or future designers. This paper addresses the problem of information overload and identifies the differences between data, information and knowledge. An exploratory study was conducted with a leading construction consultant examining three perspectives (business, project management and document management) by structured interviews and specifically how to value information in practical terms. Major challenges in information management are identified. An through-life Information Evaluation methodology (IEM) is presented to reduce information overload and to make the information more valuable in the future.

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The linkage between corporate commitment to environmental, social and governance (ESG) issues and investment performance has generated a substantial body of research outside the real estate sector. Nevertheless, the relationship between the environmental performance and financial performance of companies is still not well understood as studies have found mixed and contradicting results. Drawing upon the KLD database which contains ratings on seven ESG dimensions from 2003-2009, this paper investigates the relationship between the ESG rating and the financial performance of a sample of US real estate firms. Since the primary transmission channel from ESG activities to financial performance may be better reflected by a firm's intangible assets, we model both Tobin's q and the total annual return in a panel framework with time and sector specific fixed effects. Our results are largely consistent with the existing literature finding a positive relationship between CFP and CSP. Further, the time scale of the lagged effects seems plausible.

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Since the advent of the internet in every day life in the 1990s, the barriers to producing, distributing and consuming multimedia data such as videos, music, ebooks, etc. have steadily been lowered for most computer users so that almost everyone with internet access can join the online communities who both produce, consume and of course also share media artefacts. Along with this trend, the violation of personal data privacy and copyright has increased with illegal file sharing being rampant across many online communities particularly for certain music genres and amongst the younger age groups. This has had a devastating effect on the traditional media distribution market; in most cases leaving the distribution companies and the content owner with huge financial losses. To prove that a copyright violation has occurred one can deploy fingerprinting mechanisms to uniquely identify the property. However this is currently based on only uni-modal approaches. In this paper we describe some of the design challenges and architectural approaches to multi-modal fingerprinting currently being examined for evaluation studies within a PhD research programme on optimisation of multi-modal fingerprinting architectures. Accordingly we outline the available modalities that are being integrated through this research programme which aims to establish the optimal architecture for multi-modal media security protection over the internet as the online distribution environment for both legal and illegal distribution of media products.

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Digital Economy is one of the crucial elements promoted by the Digital Britain Report June 2009 and its Implementation Plan August 2009 in order to maintain and further the UK’s position as one of the world’s leading digital knowledge economies. Therefore, the application of Digital Technologies is high in the agenda. As pervasive digital technologies become more widely available, it becomes increasingly important to understand the legal implications of digital assets produced via digital technologies in collaborative design communication. Architects and engineers depend on intellectual property law to protect their original works. Copyright protection is automatic once a tangible medium of expression in any form of an innovative material, conforming the Copyright Designs and Patents Act 1988, is created. Although copyright law provides automatic protection to all original architectural plans, the limitation is that it only protects the expression of ideas but not the ideas themselves. The purpose of this research is to explore how effective the UK’s copyright law regime is for protecting the rights and interests of architects and engineers in their works as digital assets. The UK’s copyright law is ripe for modernisation not only to protect the rights of designers but also to further UK’s position in digital economy.

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Fingerprinting is a well known approach for identifying multimedia data without having the original data present but what amounts to its essence or ”DNA”. Current approaches show insufficient deployment of three types of knowledge that could be brought to bear in providing a finger printing framework that remains effective, efficient and can accommodate both the whole as well as elemental protection at appropriate levels of abstraction to suit various Foci of Interest (FoI) in an image or cross media artefact. Thus our proposed framework aims to deliver selective composite fingerprinting that remains responsive to the requirements for protection of whole or parts of an image which may be of particularly interest and be especially vulnerable to attempts at rights violation. This is powerfully aided by leveraging both multi-modal information as well as a rich spectrum of collateral context knowledge including both image-level collaterals as well as the inevitably needed market intelligence knowledge such as customers’ social networks interests profiling which we can deploy as a crucial component of our Fingerprinting Collateral Knowledge. This is used in selecting the special FoIs within an image or other media content that have to be selectively and collaterally protected.

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Fingerprinting is a well known approach for identifying multimedia data without having the original data present but instead what amounts to its essence or 'DNA'. Current approaches show insufficient deployment of various types of knowledge that could be brought to bear in providing a fingerprinting framework that remains effective, efficient and can accommodate both the whole as well as elemental protection at appropriate levels of abstraction to suit various Zones of Interest (ZoI) in an image or cross media artefact. The proposed framework aims to deliver selective composite fingerprinting that is powerfully aided by leveraging both multi-modal information as well as a rich spectrum of collateral context knowledge including both image-level collaterals and also the inevitably needed market intelligence knowledge such as customers' social networks interests profiling which we can deploy as a crucial component of our fingerprinting collateral knowledge.

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Much prior research on the structure and performance of UK real estate portfolios has relied on aggregated measures for sector and region. For these groupings to have validity, the performance of individual properties within each group should be similar. This paper analyses a sample of 1,200 properties using multiple discriminant analysis and cluster analysis techniques. It is shown that conventional property type and spatial classifications do not capture the variation in return behaviour at the individual building level. The major feature is heterogeneity - but there may be distinctions between growth and income properties and between single and multi-let properties that could help refine portfolio structures.

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The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models.