The importance that governments put into open government data policies has increased over the last decade. However, a decreasing speed in this trend is potentially ongoing due to the objectives of these policies not being perceived as completed. Therefore, locating the impacts and measuring their relative value generation aids the understanding of how these objectives can succeed. This study examines the impacts of open government data in Sweden and their potential value generation, focusing on the financial ones. In this study, we developed a measurement model that comprehends six different impacts that generate a value. These impacts are innovation for established firms, innovative start-ups, innovation for public institutions, anti-corruption, and democracy/civil participation. The study has used 24 semi-structured interview findings to develop the model using the grounded theory method. The model was then subsequentially tested and validated by conducting a survey.  We used PLS-SEM as a method of analysis of the 69 responses on the survey from Swedish experts in the field. The results show a positive influence on the open government data financial value generation in the Swedish context, originating from data-driven innovation in established firms. Adding to this, positive impacts on the social value generated from open government data originate from innovative start-ups and product innovation in public institutions. The social value generated was also found to influence the financial value generation. Overall, the results also confirmed that the measurement model assessed is suited for evaluating the value generation of open government data. Thus, the study contributes to policies by visualizing the potential impacts and values that specific policy decisions may yield. Besides, the study contributes to theory thanks to developing a measurement model that could be applied to different contexts. Finally, a unique method that combines model development, context understanding, and model testing is used in the research. This method is considered a contribution due to its potential to be applied to future case study research.

Some employees perceive that supervisors do not accurately reflect employees' performance or effectively differentiate among employees' performances during performance appraisals (PAs). Other employees believe the performance feedback they receive is not valuable for supporting their career development (CD). Employing leader-member exchange (LMX) theory and the distributive and interactional justice dimensions of organizational justice theory as the theoretical framework, this correlational study examined the relationships among LMX and employee-supervisor relationships (ESRs) and the relationships' influence on employees' CD through the mediating effect of employees' perceived efficacy of the PA process. Participants consisted of 44 defense contractor employees in the United States who completed a combination of 4 validated survey instruments (LMX-7, Interactional Justice, Appraisal System Satisfaction, Perceived Career Opportunity) and 1 demographic instrument. Results from the structural equation model, using partial least squares analysis, indicated significant (p < .001) positive relationships between the independent variables of LMX and ESR, the dependent mediating variable PA, and the dependent variable CD. The results indicated that a positive relationship between LMX and ESR will influence employees' CD through the mediating effect of employees' PAs. The implications for positive social change include the potential to improve communications between employees and supervisors, increase organizational performance by increasing employees' job satisfaction, potential benefiting career development for improving employees' families' quality of life, and contributions to the communities.

Measurement science has seen fast growth of data in both volume and complexity in recent years, new algorithms and methodologies have been developed to aid the decision<br>making in measurement sciences, and this process is automated for the liberation of labor. In light of the adversarial approaches shown in digital image processing, Chapter 2 demonstrate how the same attack is possible with spectroscopic data. Chapter 3 takes the question presented in Chapter 2 and optimized the classifier through an iterative approach. The optimized LDA was cross-validated and compared with other standard chemometrics methods, the application was extended to bi-distribution mineral Raman data. Chapter 4 focused on a novel Artificial Neural Network structure design with diffusion measurements; the architecture was tested both with simulated dataset and experimental dataset. Chapter 5 presents the construction of a novel infrared hyperspectral microscope for complex chemical compound classification, with detailed discussion in the segmentation of the images and choice of a classifier to choose.<br>

evaluate disruption risks in the supply chain of petroleum production. This methodology is developed to formalise and facilitate the systematic integration and implementation of various models; such as analytical hierarchy process (AHP) and partial least squares structural equation model (PLS-SEM) and various statistical tests. The methodology is validated with the case of Nigeria’s oil industry. The study revealed the need to provide a responsive approach to managing the influence of geopolitical risk factors affecting supply chain in the petroleum production industry. However, the exploration and production risk, and geopolitical risk were identified as concomitant risk factors that impact performance in Nigeria’s oil industry. The research findings show that behavioural-based mechanisms successfully predict the ability of the petroleum industry to manage supply chain risks. The significant implication for this study is that the current theoretical debate on the supply chain risk management creates the understanding of agency theory as a governing mechanism for supply chain risk in the Nigerian oil industry. The systematic approach results provide an insight and objective information for decisions-making in resolving disruption risk to the petroleum supply chain in Nigeria. Furthermore, this study highlights to stakeholders on how to develop supply chain risk management strategies for mitigating and building resilience in the supply chain in the Nigerian oil industry. The developed systematic method is associated with supply chain risk management and performance measure. The approach facilitates an effective way for the stakeholders to plan according to their risk mitigation strategies. This will consistently help the stakeholders to evaluate supply chain risk and respond to disruptions in supply chain. This capability will allow for efficient management of supply chain and provide the organization with quicker response to customer needs, continuity of supply, lower costs of operations and improve return on investment in the Nigeria oil industry. Therefore, the methodology applied provide a new way for implementing good practice for managing disruption risk in supply chain. Further, the systematic approach provides a simplistic modelling process for disruption risk evaluation for researchers and oil industry professionals. This approach would develop a holistic procedure for monitoring and controlling disruption risk in supply chains practices in Nigeria.

No description available.

Information seeking and information avoidance are the mechanisms humans natural used for coping with uncertainties and adapting to environmental stressors. Uncertainties are rooted in knowledge gaps. In social sciences, the relationship between knowledge gaps and perceived risk have received little attention. A review of the information science literature suggests that few studies have been devoted to the investigation of the role of this relationship in motivating information-seeking behavior. As an effort to address the lack of theory building in the field of information science, this study attempts to construct a model of information seeking under risk (MISR) by examining the relationships among perceived risk, knowledge gap, fear arousal, risk propensity, personal relevance, and deprivation and interest curiosity as antecedents to motivation to seek information. An experimental approach and a scenario-based survey method are employed to design the study. Partial least square structural equation modeling (PLS-SEM) analysis was conducted to test the relationships in the proposed model. Perceived risk was found to be a highly significant predictor of information seeking in moderately high-risk situations. Similarly, personal relevant has a significant negative effect on perceived risk and its interaction with knowledge gap motivates information seeking.

No description available.

<p>  </p> <p>Dicamba (3,6-dichloro-2-methoxybenzoic acid) and 2,4-D (2,4-dichlorophenoxyacetic acid) are two widely used herbicides for broadleaf weed control in soybeans. However, off-target application of dicamba and 2,4-D can cause severe damage to sensitive vegetation and crops. Early detection and assessment of off-target damage caused by these herbicides are necessary to help plant diagnostic labs and state regulatory agencies collect more information of the on-site conditions so to develop solutions to resolve the issue in the future. In 2021, the study was conducted to detect damage to soybean leaves caused by dicamba and 2,4-D by using LeafSpec, an accurate handheld hyperspectral leaf scanner. . High resolution single leaf hyperspectral images of 180 soybean plants in the greenhouse exposed to nine different herbicide treatments were taken 1, 7, 14, 21 and 28 days after herbicide spraying. Pairwise PLS-DA models based on spectral features were able to distinguish leaf damage caused by two different modes of action herbicides, specifically dicamba and 2,4-D, as early as 2 hours after herbicide spraying. In the spatial distribution analysis, texture and morphological features were selected for separating the dosages of herbicide treatments. Compared to the mean spectrum method, new models built upon the spectrum, texture, and morphological features, improved the overall accuracy to over 70% for all evaluation dates. The combined features are able to classify the correct dosage of the right herbicide as early as 7 days after herbicide sprays. Overall, this work has demonstrated the potential of using spectral and spatial features of LeafSpec hyperspectral images for early and accurate detection of dicamba and 2,4-D damage in soybean plants.</p> <p>   </p>

<p>Accommodating changing consumer tastes, nutritional targets, competitive pressures and government regulations is an ongoing task in the food industry. Product development projects tend to have competing goals and more potential solutions than can be examined efficiently. However, existing databases or spreadsheets containing formulas, ingredient properties, and product characteristics can be exploited using latent variable methods to confront difficult formulation issues. Using these methods, a product developer can target specific final product properties and systematically determine new recipes that will best meet the development objectives.</p> <p>Latent variable methods in reformulation are demonstrated for a product line of frozen muffin batters used in the food service industry. A particular attribute is to be minimized while maintaining the taste, texture, and appearance of the original products, but the minimization is difficult because the attribute in question is not well understood. Initially, existing data is used to develop a partial least squares (PLS) model, which identifies areas for further testing. Design of experiments (DOE) in the latent variable space generates new data that is used to augment the model. An optimization algorithm makes use of the updated model to produce recipes for four different products, and a significant reduction of the target attribute is achieved in all cases.</p> <p>Latent variable methods are also applied to a difficult classification problem in oat milling. Process monitoring involves manually classifying and counting the oats and hulls in the product streams of groats; a task that is time-consuming and therefore infrequent. A solution based on near infrared (NIR) imaging and PLS-discriminant analysis (PLS-DA) is investigated and found to be feasible. The PLS-DA model, built using mixed-cultivar samples, effectively separates the oats and groats into two classes. The model is validated using samples of three pure cultivars with varying moistures and growing conditions.</p> / Master of Applied Science (MASc)

<p>This thesis provides new regression methods for the removal of structured noise in datasets. With multivariable data, the variables and the noise can be both temporally correlated (i.e. auto correlated in time) and contemporaneously correlated (i.e. cross-correlated at the same time). In many occasions it is possible to acquire measurements of the noise, or some function of it, during the data collection. Several new constrained latent variable methods (LVM) that are built upon previous LVM regression frameworks are introduced. These methods make use of the additional information available about the noise to decompose a dataset into basis for the noise and signal. The properties of these methods are investigated mathematically, and through both simulation and application to actual biomedical data.</p> <p>In Chapter Two, linear, constrained LVM methods are introduced. The performance of these methods are compared to the other similar LVM methods as well as ordinary PLS throughout several simulation studies. In Chapter Three, a NIPALS type algorithm is developed for the soft constrained PLS method which is also able to account for missing data as well as datasets with large covariance matrices. Chapter Four introduces the nonlinear-kernelized constrained LVM methods. These methods are capable of handling severe nonlinearities in the datasets. The performance of these methods are compared to nonlinear kernel PLS method. In Chapter Five the constrained methods are used to remove ballistocardiographic and muscle artifacts from EEG datasets in combined EEG-fMRI as well as single EEG experiments on patients. The results are shown and compared to the standard noise removal methods used in the field. Finally in Chapter Six, the overall conclusion and scope of the future work is laid out.</p> / Doctor of Philosophy (PhD)