<p>In spite of numerous benefits that are suggested for consumers’ utilizing integrated personal health record (PHR) systems, research has shown that these systems are not yet popular or well known to consumers. Therefore, research is needed to understand what would rise adoption rates for these systems. Hence, the main objective of this dissertation is to develop and empirically validate a theoretical model for explaining consumers’ intention to use integrated PHR systems.</p> <p>In developing the theoretical model of this dissertation, theories of information systems adoption were integrated with Self-Determination Theory (SDT), which is a well established theory from the Psychology literature that explains the mechanism through which individuals become more self-determined, i.e., motivated to take more active (rather than passive) roles in undertaking different behaviours. Taking such an active role by consumers, in the context of personal health management, is suggested to be necessary for realizing the full benefits of integrated PHR systems.</p> <p>The proposed theoretical model was validated using the PLS approach to structural equation modeling, on data collected from a cross-sectional survey involving 159 participants with no prior experience in using PHR systems. A stratified random sampling was employed to draw a representative sample of the Canadian population. The results show that consumers with higher levels of self-determination in managing their health are more likely to adopt integrated PHR systems since they have more positive perceptions regarding the use of such systems. Further, such self-determination is fueled by autonomy support from consumers’ physicians as well as consumers’ personality trait of autonomy orientation.</p> <p>This study advances the theoretical understanding of integrated PHR system adoption, and it contributes to practice by providing insightful implications for designing, promotion, and facilitating the use of integrated PHR systems among consumers.</p> / Doctor of Philosophy (PhD)

Sustainability of the built environment is one of the most significant challenges facing the construction industry, and presents significant opportunities to affect change. The absence of quantifiable and holistic sustainability measures for the built environment has hindered their application. As a result, a sustainability performance metric (SPM) framework was conceptually formulated by employing sustainability objectives and function statements a-priori to identify the correlated sustainability indicators that need to be captured equally, with respect to the environment, the economy, and society. Projection to Latent Structures (PLS), a latent variable method, was adopted to mathematically formulate the metric. Detached single-family housing was used to demonstrate the application of SPM. Datasets were generated using Athena Impact Estimator, EnergyPlus, Building Information Modelling (BIM), Socioeconomic Input/Output models, among others. Results revealed that a holistic metric, such as the SPM is necessary to obtain a sustainable design, where qualitative or univariate considerations may result in the contrary. A building envelope coefficient of performance (BECOP) metric based on an idealized system was also developed to measure the energy efficiency of the building envelope. Results revealed the inefficiencies in the current building envelope construction technologies and the missed opportunities for saving energy. Furthermore, a decision-making tool, which was formulated using the PLS utilities, was shown to be effective and necessary for early stages of the design for energy efficiency. / Thesis / Doctor of Science (PhD) / Sustainability of the built environment is a significant challenge facing the industry, and presents opportunities to affect changes. The absence of holistic sustainability measures has hindered their application. As a result, a sustainability performance metric (SPM) framework was formulated by employing sustainability objectives and function statements a-priori to identify the indicators that need to be captured. Projection to Latent Structures was adopted to mathematically formulate the metric. A housing prototype was used to demonstrate the application of the SPM utilizing a bespoke dataset. Results revealed that holistic metric, such as the SPM is necessary for achieving sustainable designs. A building envelope coefficient of performance metric was also developed to measure the energy efficiency of the building envelope. Results revealed the inefficiencies in the current building envelope technologies and identified missed opportunities. Furthermore, a decision-making tool was formulated and shown to be effective and necessary for design for energy efficiency.

Lean Management and Lean Six Sigma (LM/LSS) are improvement methodologies that have been utilized to achieve better performance outcomes at organizational and operational levels. Although there has been evidence of breakthrough improvement across diverse organizational settings, LM/LSS remains an early-stage improvement methodology in public sector organizations, specifically within local government organizations (LGOs). Some LGOs have benefited from LM/LSS and reported significant improvements, such as reducing process time by up to 90% and increasing financial savings by up to 57%. While the success of LM/LSS can lead to satisfactory outcomes, the risk of failure can also result in a tremendous waste of financial and non-financial resources. Evidence from the literature indicates that the failure to achieve the expected outcomes is likely due to the lack of attention paid to critical success factors (CSFs) that are crucial for LM/LSS success. Furthermore, research in this research area regarding characterizing and statistically examining the CSFs associated with LM/LSS in such organizational settings has been limited. Hence, the aim of this research is to provide a comprehensive investigation of the success factors for LM/LSS in LGOs. The initial stage of this dissertation involved analyzing the scientific literature to identify and characterize the CSFs associated with LM/LSS in LGOs through a systematic literature review (SLR). This effort identified a total of 47 unique factors, which were grouped into 5 categories, including organization, process, workforce knowledge, communications, task design, and team design. The next stage of this investigation focused on identifying a more focused set of CSFs. This involved evaluating the strength of the effect (or importance) of the factors using two integrated approaches: meta-synthesis and expert assessment. This process concluded with a total of 29 factors being selected for the empirical field study. The final stage included designing and implementing an online survey questionnaire to solicit LGOs' experience on the presence of factors during the development and/or implementation of LM/LSS and their impact on social-technical system outcomes. Once the survey was concluded, an exploratory factor analysis (EFA) was conducted to identify the underlying latent variables, followed by using a partial least square-structural equation model (PLS-SEM) to determine the significance of the factors on outcomes. The EFA identified three endogenous and five exogenous latent variables. The results of the PLS-SEM model identified four significant positive relationships. Based on the results from the structural paths, the antecedent Improvement Readiness (IR) and Change Awareness (CA) were significant and had a positive influence on Transformation Success (TS). For the outcome Deployment Success (DS), Sustainable Improvement Infrastructure (SII) was the only significant exogenous variable and had the highest positive impact among all significant predictor constructs. Furthermore, Measurement-Based Improvement (MBI) was significant and positively influenced Improvement Project Success (IPS). Findings from this dissertation could serve as a foundation for researchers looking to further advance the maturity of this research area based on the evidence presented in this work. Additionally, this work could be used as guidelines for practitioners in developing implementation processes by considering the essential factors to maximize the success of LM/LSS implementation. Given the diversity of functional areas and processes within LGO contexts, it is also possible that other public sector organizations could benefit from these findings. / Doctor of Philosophy / Lean Management and Lean Six Sigma (LM/LSS) is an improvement methodology that is used by businesses and organizations to improve how they work and achieve better results. LM/LSS has been especially helpful in various organizations; however, the implementation of this improvement methodology has been limited by many challenges for public sector organizations, especially local government organizations (LGOs). The overall aim of this dissertation is to improve the success of LM/LSS implementation within the context of LGOs. More specifically, this dissertation systematically studied the critical success factors associated with LM/LSS success. Different research approaches, including research formulation, development, and testing techniques, were conducted to achieve the aim of this dissertation. Publications related to LM/LSS in LGOs have been rigorously analyzed to identify a comprehensive list of CSFs. To identify the most important factors, a meta-synthesis evaluation and expert survey assessment have been conducted. Following the refinement of the factors, a large-scale field study using a survey questionnaire has been designed and distributed to LGOs. Once the survey concluded, statistical methods that included Exploratory Factor Analysis (EFA) and Partial Least Squares-Structural Equation Modeling (PLS-SEM) were conducted. The former was used to identify the underlying latent variables, while the latter was conducted to examine the influence of the factors on social and technical outcomes. This dissertation could be used as a reference guideline helping practitioners to increase the success of LM/LSS implementation in LGOs. This dissertation can also guide scholars to potential research avenues that could advance this research area.

Yes / Automatic facial age progression (AFAP) has been an active area of research in recent years. This is due to its numerous applications which include searching for missing. This study presents a new method of AFAP. Here, we use an Active Appearance Model (AAM) to extract facial features from available images. An ageing function is then modelled using Sparse Partial Least Squares Regression (sPLS). Thereafter, the ageing function is used to render new faces at different ages. To test the accuracy of our algorithm, extensive evaluation is conducted using a database of 500 face images with known ages. Furthermore, the algorithm is used to progress Ben Needham’s facial image that was taken when he was 21 months old to the ages of 6, 14 and 22 years. The algorithm presented in this paper could potentially be used to enhance the search for missing people worldwide.

BACKGROUND:In a globalized word, prevention of infectious diseases is a major challenge. Rapid detection of viable virus particles in water and other environmental samples is essential to public health risk assessment, homeland security and environmental protection. Current virus detection methods, especially assessing viral infectivity, are complex and time-consuming, making point-of-care detection a challenge. Faster, more sensitive, highly specific methods are needed to quantify potentially hazardous viral pathogens and to determine if suspected materials contain viable viral particles. Fourier transform infrared (FTIR) spectroscopy combined with cellular-based sensing, may offer a precise way to detect specific viruses. This approach utilizes infrared light to monitor changes in molecular components of cells by tracking changes in absorbance patterns produced following virus infection. In this work poliovirus (PV1) was used to evaluate the utility of FTIR spectroscopy with cell culture for rapid detection of infective virus particles.RESULTS:Buffalo green monkey kidney (BGMK) cells infected with different virus titers were studied at 1 - 12 hours post-infection (h.p.i.). A partial least squares (PLS) regression method was used to analyze and model cellular responses to different infection titers and times post-infection. The model performs best at 8 h.p.i., resulting in an estimated root mean square error of cross validation (RMSECV) of 17 plaque forming units (PFU)/ml when using low titers of infection of 10 and 100 PFU/ml. Higher titers, from 103 to 106 PFU/ml, could also be reliably detected.CONCLUSIONS:This approach to poliovirus detection and quantification using FTIR spectroscopy and cell culture could potentially be extended to compare biochemical cell responses to infection with different viruses. This virus detection method could feasibly be adapted to an automated scheme for use in areas such as water safety monitoring and medical diagnostics.

The purpose of this research was to identify key determinants of service provider effectiveness and how it impacts outsourced security success. As environments have become more robust and dynamic, many organizations have made the decision to leverage external security expertise and have outsourced many of their information technology security functions to Managed Security Service Providers (MSSPs). Information Systems Outsourcing, at its core, is when a customer chooses to outsource certain information technology functions or services to a service provider and engages in a legally binding agreement. While legal contracts govern many aspects of an outsourcing arrangement, it cannot serve as the sole source of determining the outcome of a project. Organizations are viewing outsourcing success as an attainment of net benefits achieved through the use of a service provider. The effectiveness of the service provider has an impact on a company’s ability to meet business objectives and adhere to service level agreements. Many empirical studies have focused on outsourcing success, but few have focused on service provider effectiveness, which can serve as a catalyst to outsourcing success. For this research, Agency Theory (AT) was proposed as a foundation for developing the research model which included key areas of focus in information asymmetry, the outsourcing contract, moral hazard, trust, service provider effectiveness, and security outsourcing success. Agency Theory helped uncover several hypotheses deemed germane to service provider effectiveness and provided insight into helping understand the principal-agent paradigm that exists with security outsourcing. Confirmatory Factor Analysis (CFA) and Partial Least Squares-Structured Equation Modeling (PLS-SEM) were used with SmartPLS to analyze the data and provided clarity and validation for the research model and helped uncover key determinants of service provider effectiveness. The statistical results showed support for information asymmetry, contract, and trust, all of which were mediated through service provider effectiveness. The results also showed that service provider effectiveness is directly correlated to increasing security outsourcing success. This concluded that the research model showed significant results to support 4 of the 5 hypotheses proposed and helped uncover key findings on how security outsourcing success can be impacted. This research served as an original contribution to information security while viewing outsourcing success from the perspective of the client, security services, and customer expectations.

<p>The Sub-Saharan Africa (SSA) region is faced with limited medical personnel and healthcare services to address the many healthcare problems of the region. Poor health indicators reflect the overall decline in socio-economic development. Shortages of access to health services in the region is further complicated by the concentration of health services in urban areas, the region’s multiple medical problems (over 70% of HIV/AIDS cases in the world); and the brain drain phenomenon – it is estimated one-third of African physicians emigrate to North America and Europe. The result is that the SSA region is left with about 10 physicians, and 20 beds, per 100,000 patients. Telemedicine has been found to offer socio-economic benefits, reduce costs, and improve access to healthcare service providers by patients, but previous attempts to move various information technologies from developers in the industrial world to the developing world have failed because of a clear neglect of infrastructural and cultural factors that influence such transfers. The objective of this study is to address key factors that challenge the introduction of telemedicine technology into the health sector in SSA in particular, and by extension, other developing countries with similar socio-economic structures.</p><p>This research offers a distinctive perspective, focusing on visually-based clinical applications in the SSA region, and considerable attention to the national infrastructure and cultural impact of telemedicine transfer (social and value) outcomes. Two research models and its associated hypotheses are proposed and empirically tested using quantitative data collected from SSA physicians and other health professionals. The study also contributes to the ongoing debate on the potential of telemedicine in improving access and reducing costs. This research can help to understand the socio-economic impact of telemedicine outcomes in a comprehensive way. The finding from the survey shows the rapid advances in telemedicine technology specifically, visual clinical applications may become an essential healthcare tool in the near future within SSA countries.</p>

Lean management is becoming the standard for systematic productivity improvement, but the majority of implementations fail to sustain. Hence, the critical success factors for lean were the focus of this work. Literature review showed that the causality for lean success was not empirically developed beyond case study contextualisation. A multifaceted work was developed with contextualisation studies, survey of lean knowledge (758 responses), and a comprehensive case-study questionnaire (1253 responses from 44 countries). The statistical methods included exploratory factor analysis and path analysis by structural equation modelling (SEM). The first questionnaire revealed two different understandings of lean, and the second explored the underlying causality for lean success, including contingency for business size and product variety. Many contributions to the body of knowledge issued from this work. First of all, there was a methodological contribution, pioneering explorative structural modelling of full scope lean implementation. Second, SEMs of the lean knowledge-based view showed the profound positive effects of management knowledge on the primary factors for lean success. These factors were shown to be leadership and employee development. Third, the most beneficial lean methods were highlighted for specific scenarios. Fourth, the negligible and negative effects of a consultant-based approach to lean were uncovered. The results showed that the majority of consultants did not aid the long-term performance and sustainability of lean but significantly hindered it, except where masterful consultants acted as coaches. Fifth, a shortage of lean knowledge was observed in New Zealand; their participants averaged only half of what the USA�s did. Sixth, as culture has been emphasised in current literature, the present danger of overly focusing on it was discussed. Seventh was a conceptual contribution integrating lean and risk management, and a practical application with a risk analysis. This developed a risk matrix for the assessment and prioritisation of implementation components. Eighth, some adjustments to government lean strategies were proposed. And finally, the work integrated the findings in a tangible stage process model for implementation in SMEs. The dissemination of this knowledge has the potential to enhance productivity and commercial success of industries in New Zealand and abroad through successful lean implementations. Lean is not a weak methodology but it has been misunderstood and misapplied.

Este trabalho apresenta um estudo comparativo do poder de predição de quatro métodos de regressão adequados para situações nas quais os dados, dispostos na matriz de planejamento, apresentam sérios problemas de multicolinearidade e/ou de alta dimensionalidade, em que o número de covariáveis é maior do que o número de observações. No presente trabalho, os métodos abordados são: regressão por componentes principais, regressão por mínimos quadrados parciais, regressão ridge e LASSO. O trabalho engloba simulações, em que o poder preditivo de cada uma das técnicas é avaliado para diferentes cenários definidos por número de covariáveis, tamanho de amostra e quantidade e intensidade de coeficientes (efeitos) significativos, destacando as principais diferenças entre os métodos e possibilitando a criação de um guia para que o usuário possa escolher qual metodologia usar com base em algum conhecimento prévio que o mesmo possa ter. Uma aplicação em dados reais (não simulados) também é abordada. / This paper presents a comparative study of the predictive power of four suitable regression methods for situations in which data, arranged in the planning matrix, are very poorly multicolinearity and / or highdimensionality, wherein the number of covariatesis greater the number of observations. In this study, the methods discussed are: principal component regression,partial least squares regression,ridge regression and LASSO. The work includes simulations, where in the predictive power of each of the techniques is evaluated for different scenarios defined by the number of covariates, sample size and quantity and intensity ratios (effects) significant, high lighting the main dffierences between the methods and allowing for the creating a guide for the user to choose which method to use based on some prior knowledge that it may have. An applicationon real data (not simulated) is also addressed.

[pt] Muitos problemas da área de aprendizagem automática tem por objetivo modelar a complexa relação existente num sisitema , entre variáveis de entrada X e de saída Y na ausência de um modelo teórico. A regressão por mínimos quadrados parciais PLS ( Partial Least Squares) constitui um método linear para resolução deste tipo de problema , voltado para o caso de um grande número de variáveis de entrada quando comparado com número de amostras. Nesta tese , apresentamos uma variante do algoritmo clássico PLS para o tratamento de grandes conjuntos de dados , mantendo um bom poder preditivo. Dentre os principais resultados destacamos um versão paralela PPLS (Parallel PLS ) exata para o caso de apenas um variável de saída e um versão rápida e aproximada DPLS (DIRECT PLS) para o caso de mais de uma variável de saída. Por outro lado ,apresentamos também variantes para o aumento da qualidade de predição graças à formulação não linear. São elas o LPLS ( Lifted PLS ), algoritmo para o caso de apenas uma variável de saída, baseado na teoria de funções de núcleo ( kernel functions ), uma formulação kernel para o DPLS e um algoritmo multi-kernel MKPLS capaz de uma modelagemmais compacta e maior poder preditivo, graças ao uso de vários núcleos na geração do modelo. / [en] The purpose of many problems in the machine learning field isto model the complex relationship in a system between the input X and output Y variables when no theoretical model is available. The Partial Least Squares (PLS)is one linear method for this kind of problem, for the case of many input variables when compared to the number of samples. In this thesis we present versions of the classical PLS algorithm designed for large data sets while keeping a good predictive power. Among the main results we highlight PPLS (Parallel PLS), a parallel version for the case of only one output variable, and DPLS ( Direct PLS), a fast and approximate version, for the case fo more than one output variable. On the other hand, we also present some variants of the regression algorithm that can enhance the predictive quality based on a non -linear formulation. We indroduce LPLS (Lifted PLS), for the case of only one dependent variable based on the theory of kernel functions, KDPLS, a non-linear formulation for DPLS, and MKPLS, a multi-kernel algorithm that can result in a more compact model and a better prediction quality, thankas to the use of several kernels for the model bulding.