No description available.

It is twenty-one years since South Africa’s democracy and the majority of South African households can still be classified as poor, despite the various interventions by Government to reduce poverty and inequality. The measurement used to determine the financial status of a household at a given point in time is its net equity in accordance with its Statement of Financial Position, calculated as its assets owned less liabilities owed. This study aimed to identify the selected significant factors that affect a South African household’s net equity value. In order to achieve the aim of this study, a heuristic model consisting of two components was developed. The first component considered which assets and liabilities should be included in determining a household’s net equity and how these assets and liabilities should be valued. The second component identified the selected factors that influence a household’s net equity. The heuristic model was applied to the empirical data using three phases. Firstly, the net equity value was calculated for each household. This was followed by an analysis of the selected factors that significantly influence household net equity. The last phase was performed to determine the effect of the identified selected factors in explaining the difference between households that have above average net equity values and those having below average values. The results of the study indicated that 11 selected factors significantly influence the net equity value in the South African household’s Statement of Financial Position. Seven of those factors significantly explain between 28,3 percent and 38,1 percent of the differences in the net equity value of a household when comparing the households with above average net equity value with those with below average values. This is useful information for policy makers in identifying the selected factors that will most significantly increase the net equity value of a household with a net equity value below the South African average. / Centre for Accounting Studies / M. Com. (Accounting Science)

Artificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including hydrology and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warning Systems (EWS) might not wish to take the risk of implementing solutions perceived as containing unknown elements, despite the computational advantages that ANNs offer. This thesis therefore builds on existing efforts to open the box and develop tools and techniques that visualise, analyse and use ANN weights and biases especially from the viewpoint of neural pathways from inputs to outputs of feedforward networks. In so doing, it aims to demonstrate novel approaches to self-improving predictive model construction for both regression and classification problems. This includes Neural Pathway Strength Feature Selection (NPSFS), which uses ensembles of ANNs trained on differing subsets of data and analysis of the learnt weights to infer degrees of relevance of the input features and so build simplified models with reduced input feature sets. Case studies are carried out for prediction of flooding at multiple nodes in urban drainage networks located in three urban catchments in the UK, which demonstrate rapid, accurate prediction of flooding both for regression and classification. Predictive skill is shown to reduce beyond the time of concentration of each sewer node, when actual rainfall is used as input to the models. Further case studies model and predict statutory bacteria count exceedances for bathing water quality compliance at 5 beaches in Southwest England. An illustrative case study using a forest fires dataset from the UCI machine learning repository is also included. Results from these model ensembles generally exhibit improved performance, when compared with single ANN models. Also ensembles with reduced input feature sets, using NPSFS, demonstrate as good or improved performance when compared with the full feature set models. Conclusions are drawn about a new set of tools and techniques, including NPSFS and visualisation techniques for inspection of ANN weights, the adoption of which it is hoped may lead to improved confidence in the use of ANN for live real-time EWS applications.

It is twenty-one years since South Africa’s democracy and the majority of South African households can still be classified as poor, despite the various interventions by Government to reduce poverty and inequality. The measurement used to determine the financial status of a household at a given point in time is its net equity in accordance with its Statement of Financial Position, calculated as its assets owned less liabilities owed. This study aimed to identify the selected significant factors that affect a South African household’s net equity value. In order to achieve the aim of this study, a heuristic model consisting of two components was developed. The first component considered which assets and liabilities should be included in determining a household’s net equity and how these assets and liabilities should be valued. The second component identified the selected factors that influence a household’s net equity. The heuristic model was applied to the empirical data using three phases. Firstly, the net equity value was calculated for each household. This was followed by an analysis of the selected factors that significantly influence household net equity. The last phase was performed to determine the effect of the identified selected factors in explaining the difference between households that have above average net equity values and those having below average values. The results of the study indicated that 11 selected factors significantly influence the net equity value in the South African household’s Statement of Financial Position. Seven of those factors significantly explain between 28,3 percent and 38,1 percent of the differences in the net equity value of a household when comparing the households with above average net equity value with those with below average values. This is useful information for policy makers in identifying the selected factors that will most significantly increase the net equity value of a household with a net equity value below the South African average. / Taxation / M. Phil. (Accounting Science)

No description available.

Thèse de doctorat présentée en vue de l'obtention du doctorat en psychologie - recherche (Ph.D) / La santé psychologique est une composante essentielle au bon fonctionnement quotidien des individus. Celle-ci est influencée par plusieurs facteurs, dont leur emploi. Un employé possédant une bonne santé psychologique n’est pas seulement fondamental pour son propre bien-être, mais également pour les objectifs de l’organisation qui l’emploie. Effectivement, un employé qui vit beaucoup de bien-être psychologique au travail est plus satisfait de son emploi et est plus productif et efficace à la tâche. A contrario, un employé en détresse psychologique à son travail est à risque de développer plusieurs troubles psychologiques, dont la dépression, le burnout et l’anxiété, menant à plusieurs comportements indésirables et à un roulement du personnel accru. La résilience est un phénomène permettant aux employés de mieux gérer les conséquences toxiques des adversités caractéristiques du milieu organisationnel sur leur santé psychologique en protégeant leur bien-être et en limitant les impacts négatifs sur leur détresse psychologique. Les objectifs de cette thèse sont donc de rapporter les principales lacunes de la littérature scientifique au sujet des liens entre la résilience et la santé psychologique en milieu organisationnel et de proposer un modèle prédictif de la santé psychologique chez les directions et directions adjointes des établissements scolaires francophones. Le premier article est une revue de portée qui cartographie la connaissance scientifique sur les liens statistiques directs et indirects entre la résilience et deux facteurs de santé psychologique : le bien-être et la détresse psychologique. Cet article recueille les résultats d’études sur un large éventail d’emplois et de pays d’origine. Plusieurs biais et lacunes en lien avec la validité externe sont observés. La majorité des études proviennent des pays catégorisés WEIRD, étudient les emplois du secteur de la santé et sont motivées par l’arrivée d’une catastrophe telle que la pandémie au COVID-19. Le deuxième article propose un modèle prédictif du bien-être et de la détresse psychologique chez les directions et directions adjointes d’établissements ii scolaires francophones ontariennes et québécoises. Le modèle stipule que le climat de travail, la résilience et les habiletés politiques sont des inducteurs directs du bien-être et de la détresse. Le modèle proposé est également comparé à un modèle alternatif basé sur la théorie de conservation des ressources. Selon l’analyse acheminatoire, le modèle proposé représente le mieux les données et le climat de travail et la résilience prédisent statistiquement le bien-être et la détresse psychologique. La résilience est également le meilleur prédicteur de la santé psychologique. / Psychological health is an essential component for the proper daily functioning of individuals. This is influenced by several factors, including their work. An employee with good psychological health is not only fundamental for their own well-being, but also for the objectives of the organization that employs them. Indeed, an employee who experiences a lot of psychological well-being at work is more satisfied with their job and is more productive and efficient at work. Conversely, an employee in psychological distress is at risk of developing several psychological disorders, including depression, burnout, and anxiety, leading to several undesirable behaviors and increased staff turnover. Resilience is a phenomenon that allows employees to better manage the toxic consequences of the adversities characteristic of the organizational setting on their psychological health by protecting their well being and limiting the negative impacts on their psychological distress. The objectives of this thesis are therefore to report the gaps in the scientific literature on the links between resilience and psychological health in an organizational setting and to propose a predictive model of psychological health among principals and vice-principals of French-speaking schools. The first article is a scoping review that maps scientific knowledge on the direct and indirect statistical links between resilience and the two factors of psychological health: well-being and psychological distress. This article collects the results of studies on a wide range of jobs and countries of origin. Several shortcomings and biases are observed. The majority of studies come from countries categorized as WEIRD, study jobs in the health sector and are motivated by the arrival of a disaster such as the COVID-19 pandemic. The second article proposes a predictive model of well-being and psychological distress among principals and vice-principals of French-speaking schools from Ontario and Québec. The model states that work climate, resilience and political skills are direct drivers of well-being and distress. The proposed model is also compared to an alternative model based on the resource conservation theory. According to the path analysis, the proposed model best fits the data, and work climate and resilience statistically predict well-being and psychological distress. Resilience is also the best predictor of psychological health.