Decision makers require the right information at the right time, in the right place and in the right format so that they can make good decisions. Although business intelligence (BI) has the potential to improve decision making, there is little empirical evidence of how well this has been achieved. The purpose of this dissertation is to examine the quality of decisions made using BI. The research question it addresses is what are the key antecedents of decision quality for users of business intelligence systems? The theoretical support for the model is developed based on the literature review that draws on decision support systems (DSS), group decision support systems (GDSS), and BI. Grounded on this literature review, the antecedents of decision quality are operationalized in this dissertation through independent variables such as the problem space complexity, the level of BI usage, the BI user experience, and information quality. The dependent variable is operationalized as decision quality and it captures the self-satisfaction with a decision made by users in a BI environment. The research model was tested using a survey of BI users whose names were provided by a marketing company. This research suggests that BI user experience is a more complex …

To prevent loss of lives during seasonal disasters, relief agencies distribute critical supplies and provide lifesaving services to the affected populations. Despite agencies' efforts, frequently occuring disasters increase the cost of relief operations. The purpose of our study is to minimize the cost of relief operations, considering that such disasters cause random demand. To achieve this, we have formulated a series of models, which are distinct from the current studies in three ways. First, to the best of our knowledge, we are the first ones to capture both perishable and durable products together. Second, we have aggregated multiple products in a different way than current studies do. This unique aggregation requires less data than that of other types of aggregation. Finally, our models are compatible with the practical data generated by FEMA. Our models offer insights on the impacts of various parameters on optimum cost and order size. The analyses of correlation of demand and quality of information offer interesting insights; for instance, under certain cases, the quality of information does not influence cost. Our study has considered both risk averse and risk neutral approaches and provided insights. The insights obtained from our models are expected to help agencies reduce …