More and more companies are making online presence by opening online stores and providing customers with company and products information but the overwhelming amount of information also creates information overload for the customers. Customers feel frustrated when given too many choices while companies face the problem of turning browsers into actual buyers. Online recommender systems have been adopted to facilitate customer product search and provide personalized recommendation in the market place. The study will compare the persuasiveness of different online recommender systems and the factors influencing customer preferences. Review of the literature does show that online recommender systems provide customers with more choices, less effort, and better accuracy. Recommender systems using different technologies have been compared for their accuracy and effectiveness. Studies have also compared online recommender systems with human recommendations 4 and recommendations from expert systems. The focus of the comparison in this study is on the recommender systems using different methods to solicit product preference and develop recommendation message. Different from the technology adoption and acceptance models, the persuasive theory used in the study is a new perspective to look at the end user issues in information systems. This study will also evaluate the impact of product complexity and product involvement on recommendation persuasiveness. The goal of the research is to explore whether there are differences in the persuasiveness of recommendation given by different recommender systems as well as the underlying reasons for the differences. Results of this research may help online store designers and ecommerce participants in selecting online recommender systems so as to improve their products target and advertisement efficiency and effectiveness.

A goal-oriented, inverse decision-based design method to find satisficing solutions for multiple football helmet components that all work together to achieve a set of conflicting goals is presented. The efficacy of the method is illustrated with the design of the top region of an American football helmet. The prototype helmet was first constructed and tested with a twin-wire drop tower to study the different components effect on the system response. The inverse design method is used to design the foam liner to dissipate the maximum impact energy, and then the composite shell is designed to reduce the weight. The Concept Exploration Framework and the compromise Decision Support Problem are used to find satisficing solutions to the system-level performance goals under uncertainty. The proposed goal-oriented, inverse decision-based design method is generic and will be used to design additional components, the complete helmet, and ultimately helmets for other sports.

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<p> The constantly changing and dynamic nature of medical knowledge has proven to be challenging for healthcare professionals. Due to reliance on human knowledge the practice of medicine in many cases is subject to errors that endanger patients' health and cause substantial financial loss to both public and governmental health sectors. Computer based clinical guidelines have been developed to help healthcare professionals in practicing medicine. Currently, the decision making steps within most guideline modeling languages are limited to the evaluation of basic logic expressions. On the other hand, data mining analyses aim at building descriptive or predictive mining models that contain valuable knowledge; and researchers in this field have been active to apply data mining techniques on health data. However, this type of knowledge can not be represented using the current guideline specification standards.</p> <p> In this thesis, we focus is on encoding, sharing and finally using the results obtained from a data mining study in the context of clinical care and in particular at the point of care. For this purpose, a knowledge management framework is proposed that addresses the issues of data and knowledge interoperability. Standards are adopted to represent both data and data mining results in an interoperable manner; and then the incorporation of data mining results into guideline-based Clinical Decision Support Systems is elaborated. A prototype tool has been developed as a part of this thesis that serves as the proof of concept which provides an environment for clinical guideline authoring and execution. Finally three real-world clinical case studies are presented.</p> / Thesis / Master of Applied Science (MASc)

Computerized decision aids are powerful tools to assist with decision-making. Decision models are designed to incorporate and analyze available data in order to present a recommended solution to a problem. Business decision makers, including accountants, have much to gain from integrating decision support technology with their own skills and experience. Several studies have determined that there are many instances in which these decision aids perform favorably to human decision-makers. Despite this fact, studies have shown that reliance upon these aids is incomplete, even when they process data in a highly efficient manner. On the other hand, decision aids have limitations. If such a decision support system is not updated to match changing conditions, relying on the aid can lead to suboptimal decision-making. This study uses a laboratory experiment involving a managerial accounting task: prediction of manufacturing overhead costs. In the experimental scenario, a decision support system's recommended solutions become inaccurate due to a shift in environmental conditions. The first research objective is to determine whether subjects rely on the aid's advice before this change and, to their detriment, after the change. The second research objective is to examine whether the feedback environment, the timing of the decision aid's inclusion into the task, or the inherent confidence level of the task participant affect the tendency to rely on the aid in both of these environmental conditions. The results of the study provide evidence that decision-makers rely on decision aids, and are susceptible to over-reliance on them. These findings add to the results of prior studies that only examine a single trial task. Additionally, it is determined that the timing of a decision aid's recommendation can affect the degree to which it is relied upon. Next, there is evidence that feedback environment can help reliance and mitigate over-reliance. There is no evidence that task confidence affects reliance. Lastly, decision aids result in longer amounts of time used to complete the task. / Ph. D.