843 resultados para case study research


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This paper addresses the problem of optimally locating intermodal freight terminals in Serbia. To solve this problem and determine the effects of the resulting scenarios, two modeling approaches were combined. The first approach is based on multiple-assignment hub-network design, and the second is based on simulation. The multiple-assignment p-hub network location model was used to determine the optimal location of intermodal terminals. Simulation was used as a tool to estimate intermodal transport flow volumes, due to the unreliability and unavailability of specific statistical data, and as a method for quantitatively analyzing the economic, time, and environmental effects of different scenarios of intermodal terminal development. The results presented here represent a summary, with some extension, of the research realized in the IMOD-X project (Intermodal Solutions for Competitive Transport in Serbia).

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This research aims to use the multivariate geochemical dataset, generated by the Tellus project, to investigate the appropriate use of transformation methods to maintain the integrity of geochemical data and inherent constrained behaviour in multivariate relationships. The widely used normal score transform is compared with the use of a stepwise conditional transform technique. The Tellus Project, managed by GSNI and funded by the Department of Enterprise Trade and Development and the EU’s Building Sustainable Prosperity Fund, involves the most comprehensive geological mapping project ever undertaken in Northern Ireland. Previous study has demonstrated spatial variability in the Tellus data but geostatistical analysis and interpretation of the datasets requires use of an appropriate methodology that reproduces the inherently complex multivariate relations. Previous investigation of the Tellus geochemical data has included use of Gaussian-based techniques. However, earth science variables are rarely Gaussian, hence transformation of data is integral to the approach. The multivariate geochemical dataset generated by the Tellus project provides an opportunity to investigate the appropriate use of transformation methods, as required for Gaussian-based geostatistical analysis. In particular, the stepwise conditional transform is investigated and developed for the geochemical datasets obtained as part of the Tellus project. The transform is applied to four variables in a bivariate nested fashion due to the limited availability of data. Simulation of these transformed variables is then carried out, along with a corresponding back transformation to original units. Results show that the stepwise transform is successful in reproducing both univariate statistics and the complex bivariate relations exhibited by the data. Greater fidelity to multivariate relationships will improve uncertainty models, which are required for consequent geological, environmental and economic inferences.

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There is renewed interest in the state's role in the economic sphere but a lack of research on the viability and employment effects of alternative economic models, in particular from a ‘liberal market economy’ perspective. This article addresses this gap in the human resource management literature by undertaking a detailed case study of industrial policy in the Irish pharmaceutical sector. The proactive and resource-intensive industrial policy adopted by the Irish government and development agencies is found to have underpinned a significant strategic upgrading in this sector of the Irish economy. In turn this has facilitated the growth of high-wage, high-skill jobs. The findings highlight the potential for an active industrial policy to promote employment upgrading in liberal market economies.

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The current body of literature regarding social inclusion and the arts tends to focus
on two areas: the lack of clear or common understanding of the terminology involved
(GLLAM, 2000) and the difficulty in measuring impact (Newman 2001). Further, much
of the literature traces the historical evolution of social inclusion policy within the arts
from a political and social perspective (Belfiore & Bennett, 2007), whilst others
examine the situation in the context of the museum as an institution more generally
(Sandell, 2002b). Such studies are essential; however they only touch on the
importance of understanding the context of social inclusion programmes. As each
individual’s experience of exclusion (or inclusion) is argued to be different (Newman
et al., 2005) and any experience is also process-based (SEU 2001), there is a need
for more thorough examination of the processes underpinning project delivery
(Butterfoss, 2006), particularly within a field that has its own issues of exclusion, such
as the arts (Bourdieu & Darbel, 1991). This paper presents case study findings of a
programme of contemporary arts participation for adults with learning difficulties
based at an arts centre in Liverpool. By focusing on practice, the paper applies
Wenger’s (1998) social theory of learning in order to assert that rather than search
for measurable impacts, examining the delivery of programmes within their individual
contexts will provide the basis for a more reflective practice and thus more effective
policy making.

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Purpose
This article aims to analyze the role of performance management systems (PMS) in supporting public value strategies.

Design/methodology/approach
This article draws on the public value dynamic model by Horner and Hutton (2010). It presents the results of a case study of implementation of a PMS model, the ‘Value Pyramid’ (VP).

Findings
The results stress the need for an improved conceptualization of PMS within public value strategy. Through experimentation using the VP, the case site was able to measure and visualize what it considered public value and reflect on the internal/external causes of both creation and destruction of public value.

Research limitations/implication
This article is limited to just one case study, although in-depth and longitudinal.

Originality/value
This article is one of the first attempting to understand the role of PMS within the public value strategy framework, answering the call of Benington and Moore (2010) to consider public value from an accounting perspective.

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Explanations for the causes of famine and food insecurity often reside at a high level of aggregation or abstraction. Popular models within famine studies have often emphasised the role of prime movers such as population stress, or the political-economic structure of access channels, as key determinants of food security. Explanation typically resides at the macro level, obscuring the presence of substantial within-country differences in the manner in which such stressors operate. This study offers an alternative approach to analyse the uneven nature of food security, drawing on the Great Irish famine of 1845–1852. Ireland is often viewed as a classical case of Malthusian stress, whereby population outstripped food supply under a pre-famine demographic regime of expanded fertility. Many have also pointed to Ireland's integration with capitalist markets through its colonial relationship with the British state, and country-wide system of landlordism, as key determinants of local agricultural activity. Such models are misguided, ignoring both substantial complexities in regional demography, and the continuity of non-capitalistic, communal modes of land management long into the nineteenth century. Drawing on resilience ecology and complexity theory, this paper subjects a set of aggregate data on pre-famine Ireland to an optimisation clustering procedure, in order to discern the potential presence of distinctive social–ecological regimes. Based on measures of demography, social structure, geography, and land tenure, this typology reveals substantial internal variation in regional social–ecological structure, and vastly differing levels of distress during the peak famine months. This exercise calls into question the validity of accounts which emphasise uniformity of structure, by revealing a variety of regional regimes, which profoundly mediated local conditions of food security. Future research should therefore consider the potential presence of internal variations in resilience and risk exposure, rather than seeking to characterise cases based on singular macro-dynamics and stressors alone.

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Research Highlights and Abstract: Using Northern Ireland as a case study, this article provides the first nationally representative and systematic study of victims' views on how to deal with the past; Focusing specifically on Northern Ireland, it both investigates and provides a comprehensive account of the marked divisions between the various religious groupings-Protestants, Catholics and the non-affiliated-in terms of a range of truth recovery mechanisms to deal with legacy of its violent past; It empirically investigates and validates two key predictors-perceptions of victimhood and general attitudes towards the past-in determining the source of these divisions It outlines the implications of our findings for other societies emerging from conflict. Truth recovery mechanisms have become a cornerstone of peacebuilding efforts in societies emerging from conflict. Yet, to date, the view of victims in post-conflict societies concerning such arrangements remains highly anecdotal and often second-hand in nature. Mindful of this omission and using Northern Ireland as a case study, this article investigates the views of victims towards a range of mechanisms to deal with the legacy of Northern Ireland's violent past. Based on the 2011 Northern Ireland Social and Political Attitudes Survey, the results suggest some marked divisions in relation to this issue, with victims within the Catholic community being significantly more supportive of such initiatives than either Protestants or those with no religion. Moreover, while perceptions of victimhood emerge as the key predictor of attitudes among Protestants and the non-affiliated, general opinions on how to deal with the past are the key determinant of views among members of the Catholic community

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In highly heterogeneous aquifer systems, conceptualization of regional groundwater flow models frequently results in the generalization or negligence of aquifer heterogeneities, both of which may result in erroneous model outputs. The calculation of equivalence related to hydrogeological parameters and applied to upscaling provides a means of accounting for measurement scale information but at regional scale. In this study, the Permo-Triassic Lagan Valley strategic aquifer in Northern Ireland is observed to be heterogeneous, if not discontinuous, due to subvertical trending low-permeability Tertiary dolerite dykes. Interpretation of ground and aerial magnetic surveys produces a deterministic solution to dyke locations. By measuring relative permeabilities of both the dykes and the sedimentary host rock, equivalent directional permeabilities, that determine anisotropy calculated as a function of dyke density, are obtained. This provides parameters for larger scale equivalent blocks, which can be directly imported to numerical groundwater flow models. Different conceptual models with different degrees of upscaling are numerically tested and results compared to regional flow observations. Simulation results show that the upscaled permeabilities from geophysical data allow one to properly account for the observed spatial variations of groundwater flow, without requiring artificial distribution of aquifer properties. It is also found that an intermediate degree of upscaling, between accounting for mapped field-scale dykes and accounting for one regional anisotropy value (maximum upscaling) provides results the closest to the observations at the regional scale.

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Background: Patient reported outcome measures (PROMs) are used to evaluate lifestyle interventions but littleis known about differences between patients returning valid and invalid responses, or of potential for bias inevaluations. We aimed to examine the characteristics of patients who returned valid responses to lifestylequestionnaires compared to those whose responses were invalid for evaluating lifestyle change. 
Methods: We conducted a secondary data analysis from the SPHERE Study, a trial of an intervention to improveoutcomes for patients with coronary heart disease in primary care. Postal questionnaires were used to assessphysical activity (Godin) and diet (DINE) among study participants at baseline and 18 month follow-up. Three binaryresponse variables were generated for analysis: (1) valid Godin score; (2) valid DINE Fibre score; and (3) validDINE Total Fat score. Multivariate analysis comprised generalised estimating equation regression to examine theassociation of patients’ characteristics with their return of valid responses at both timepoints. 
Results: Overall, 92.1% of participants (832/903) returned questionnaires at both baseline and 18 months. Relativelyfewer valid Godin scores were returned by those who left school aged <15 years (36.5%) than aged 18 and over(50.5%), manual workers (39.5%) than non-manual (49.5%) and those with an elevated cholesterol (>5 mmol)(34.7%) than those with a lower cholesterol (44.4%) but multivariate analysis identified that only school leaving age(p = 0.047) was of statistical significance.Relatively fewer valid DINE scores were returned by manual than non-manual workers (fibre: 80.8% v 86.8%;fat: 71.2% v 80.0%), smokers (fibre: 72.6% v 84.7%; fat: 67.5% v 76.9%), patients with diabetes (fibre: 75.9% v 82.9%;fat: 66.9% v 75.8%) and those with cholesterol >5 mmol (fat: 68.2% v 76.2%) but multivariate analysis showedstatistical significance only for smoking (fibre: p = 0.013; fat: p = 0.045), diabetes (fibre: p = 0.039; fat: p = 0.047), andcholesterol (fat: p = 0.039). 
Conclusions: Our findings illustrate the importance of detailed reporting of research methods, with clearinformation about response rates, respondents and valid outcome data. Outcome measures which are relevant to astudy population should be chosen carefully. The impact of methods of outcome measurement and valid responserates in evaluating healthcare requires further study.

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In order to address road safety effectively, it is essential to understand all the factors, which
attribute to the occurrence of a road collision. This is achieved through road safety
assessment measures, which are primarily based on historical crash data. Recent advances
in uncertain reasoning technology have led to the development of robust machine learning
techniques, which are suitable for investigating road traffic collision data. These techniques
include supervised learning (e.g. SVM) and unsupervised learning (e.g. Cluster Analysis).
This study extends upon previous research work, carried out in Coll et al. [3], which
proposed a non-linear aggregation framework for identifying temporal and spatial hotspots.
The results from Coll et al. [3] identified Lisburn area as the hotspot, in terms of road safety,
in Northern Ireland. This study aims to use Cluster Analysis, to investigate and highlight any
hidden patterns associated with collisions that occurred in Lisburn area, which in turn, will
provide more clarity in the causation factors so that appropriate countermeasures can be put
in place.

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This co-authored essay outlines and analyses the AHRC 'Creative Interruptions' Project, 'Where do I belong?', a theatre-as-research project conducted with Afghan refugees in London under the guidance of PI Sarita Malik, Brunel University London.