Strategic positioning in voluntary and non-profit organizations in the UK : exploring the experiences of British charitable organizations:Exploring the experiences of British Charitable Organizations

This thesis explores the strategic positioning [SP] activities of charitable organizations [COs] within the wider sector of voluntary and non-profit organizations [VNPOs] in the UK. Despite the growing interest in SP for British COs in an increasingly competitive operating environment and changing policy context, there is lack of research in mainstream marketing/strategic management studies on this topic for charities, whilst the specialist literature on VNPOs has neglected the study of SP. The thesis begins with an extended literature review of the concept of positioning in both commercial [for-profit] and charitable organizations. It concludes that the majority of theoretical underpinnings of SP that are prescribed for COs have been derived from the commercial strategy/marketing literature. There is currently a lack of theoretical and conceptual models that can accommodate the particular context of COs and guide strategic positioning practice in them. The research contained in this thesis is intended to fill some of these research gaps. It combines an exploratory postal survey and four cross-sectional case studies to describe the SP activities of a sample of general welfare and social care charities and identifies the key factors that influence their choice of positioning strategies [PSs]. It concludes that charitable organizations have begun to undertake SP to differentiate their organizations from other charities that provide similar services. Their PSs have both generic features, and other characteristics that are unique to them. A combination of external environmental and organizational factors influences their choice of PSs. A theoretical model, which depicts these factors, is developed in this research. It highlights the role of governmental influence, other external environmental forces, the charity’s mission, organizational resources, and influential stakeholders in shaping the charity’s PS. This study concludes by considering the theoretical and managerial implications of the findings on the study of charitable and non-profit organizations.

Non-linear hierarchical visualisation

This thesis applies a hierarchical latent trait model system to a large quantity of data. The motivation for it was lack of viable approaches to analyse High Throughput Screening datasets which maybe include thousands of data points with high dimensions. High Throughput Screening (HTS) is an important tool in the pharmaceutical industry for discovering leads which can be optimised and further developed into candidate drugs. Since the development of new robotic technologies, the ability to test the activities of compounds has considerably increased in recent years. Traditional methods, looking at tables and graphical plots for analysing relationships between measured activities and the structure of compounds, have not been feasible when facing a large HTS dataset. Instead, data visualisation provides a method for analysing such large datasets, especially with high dimensions. So far, a few visualisation techniques for drug design have been developed, but most of them just cope with several properties of compounds at one time. We believe that a latent variable model (LTM) with a non-linear mapping from the latent space to the data space is a preferred choice for visualising a complex high-dimensional data set. As a type of latent variable model, the latent trait model can deal with either continuous data or discrete data, which makes it particularly useful in this domain. In addition, with the aid of differential geometry, we can imagine the distribution of data from magnification factor and curvature plots. Rather than obtaining the useful information just from a single plot, a hierarchical LTM arranges a set of LTMs and their corresponding plots in a tree structure. We model the whole data set with a LTM at the top level, which is broken down into clusters at deeper levels of t.he hierarchy. In this manner, the refined visualisation plots can be displayed in deeper levels and sub-clusters may be found. Hierarchy of LTMs is trained using expectation-maximisation (EM) algorithm to maximise its likelihood with respect to the data sample. Training proceeds interactively in a recursive fashion (top-down). The user subjectively identifies interesting regions on the visualisation plot that they would like to model in a greater detail. At each stage of hierarchical LTM construction, the EM algorithm alternates between the E- and M-step. Another problem that can occur when visualising a large data set is that there may be significant overlaps of data clusters. It is very difficult for the user to judge where centres of regions of interest should be put. We address this problem by employing the minimum message length technique, which can help the user to decide the optimal structure of the model. In this thesis we also demonstrate the applicability of the hierarchy of latent trait models in the field of document data mining.

A predictor of membrane class:discriminating α-helical and β-barrel membrane proteins from non-membranous proteins

Accurate protein structure prediction remains an active objective of research in bioinformatics. Membrane proteins comprise approximately 20% of most genomes. They are, however, poorly tractable targets of experimental structure determination. Their analysis using bioinformatics thus makes an important contribution to their on-going study. Using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we have addressed the alignment-free discrimination of membrane from non-membrane proteins. The method successfully identifies prokaryotic and eukaryotic α-helical membrane proteins at 94.4% accuracy, β-barrel proteins at 72.4% accuracy, and distinguishes assorted non-membranous proteins with 85.9% accuracy. The method here is an important potential advance in the computational analysis of membrane protein structure. It represents a useful tool for the characterisation of membrane proteins with a wide variety of potential applications.