published_or_final_version / abstract / toc / Business / Master / Master of Philosophy

Increasing amounts of data are being produced and consumed on a daily basis. Every mouse movement and click on a website can be analyzed to discover usage patterns and cognitive load (Jenkins et al., 2014), companies mine purchase histories to discover customer shopping patterns (Brin et al., 1997) and historical business transaction information can be used to improve business processes (Ghattas et al., 2014). Using sophisticated algorithms, data can be turned into information that helps guide marketers, policy makers, business managers, and other decision-makers. However, history has shown that increases in the amount and quality of information do not necessarily lead to better decision outcomes (Dawes et al., 1989). Human decision-makers may fail to understand the information, ignore it, or simply not believe it. Methods for effectively conveying information to humans must be studied so that the full value of information systems can be realized. This dissertation uses three studies to explain the factors that make technology persuasive. In the first study, attitudes toward technology measure how beliefs about technology influence the way people process information. Ordering effects are also examined to determine how people view information from decision support systems, and to find the optimal time to present information to decision-makers. In the second study, the persuasive power of text and audio modalities are compared. Additionally, the loss aversion bias is investigated to determine the utility of leveraging this cognitive bias in a technology context. In the third study, Protection Motivation Theory (Rogers, 1983) is used to extend the loss aversion model from study two. The study also investigates how message vividness and user participation through software personalization influence attitudes and behavior. Together, these experiments extend existing theoretical frameworks while giving actionable guidance to information systems practitioners. The studies demonstrate the importance of understanding cognitive biases, attitudes toward technology, and message delivery in a decision support scenario. These investigations are the first step in creating a more comprehensive model of factors that influence the persuasive power of technology.

A pressing need exists for effective decision support systems to facilitate the rapid and accurate screening of large volumes of people. Millions of travelers transit through international borders and secure areas on an annual basis. Humans are exceptionally poor at detecting lying and deception and perform, on average, no better than chance. This research study focuses on the development, design and implementation of a Kiosk for Rapid Assessment of Deception (K-RAD) that integrates questioning with response processing and deception detection. An exploratory pilot study (N=68) and a primary study (N=225) were executed.The K-RAD was designed to have a three-dimensional figure, an "Embodied Conversational Agent" (ECA), deliver the questions through speech. This delivery mechanism was chosen because human subjects have been shown in the past to react emotionally to ECAs during conversational interactions, and emotional arousal is one of the cues to deception. Responses were analyzed for deception cues, focusing on kinesic, linguistic, and vocalic characteristics that can be captured for automated processing and which would be unique to this setting.The results show unique subject behaviors. Subjects exhibited minimal movement and had very little tendency to change posture. Some subjects (6%) referred to the ECA as an authority figure, using "sir" when responding. Subjects positioned themselves at varying distances from the ECA, with significant gender differences. Post-experiment surveys indicated a gender difference in overall stress, with female subjects reporting significantly higher levels, independent of the experimental condition.Postural-based logistic regression created significant classification models for the pilot (59.1% classification accuracy) and primary (57.2% & 62.8% classification accuracies) studies. Movement analysis had varying and conflicting results. A robust deception index with a 68.1% classification accuracy was achievable in the pilot study based on high-frequency movement and arm placement. Primary study deception indices were not significant.The results include a comprehensive set of observations and lessons learned regarding kiosk design, deception technologies, detection effectiveness, and future considerations to take into account when creating a next-generation K-RAD system. Many challenges remain, but the concept is functional, promising, and could revolutionize security screening and deception detection in a variety of settings.

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This research investigates the practical issues surrounding the development and implementation of Decision Support Systems (DSS). The research describes the traditional development approaches analyzing their drawbacks and introduces a new DSS development methodology. The proposed DSS methodology is based upon four modules; needs' analysis, data warehouse (DW), knowledge discovery in database (KDD), and a DSS module. The proposed DSS methodology is applied to and evaluated using the admission and registration functions in Egyptian Universities. The research investigates the organizational requirements that are required to underpin these functions in Egyptian Universities. These requirements have been identified following an in-depth survey of the recruitment process in the Egyptian Universities. This survey employed a multi-part admission and registration DSS questionnaire (ARDSSQ) to identify the required data sources together with the likely users and their information needs. The questionnaire was sent to senior managers within the Egyptian Universities (both private and government) with responsibility for student recruitment, in particular admission and registration. Further, access to a large database has allowed the evaluation of the practical suitability of using a data warehouse structure and knowledge management tools within the decision making framework. 1600 students' records have been analyzed to explore the KDD process, and another 2000 records have been used to build and test the data mining techniques within the KDD process. Moreover, the research has analyzed the key characteristics of data warehouses and explored the advantages and disadvantages of such data structures. This evaluation has been used to build a data warehouse for the Egyptian Universities that handle their admission and registration related archival data. The decision makers' potential benefits of the data warehouse within the student recruitment process will be explored. The design of the proposed admission and registration DSS (ARDSS) will be developed and tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MSSQL Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will be used as a report generation tool. CLUST AN Graphics (5.0) by CLUST AN software will also be used as a clustering package. Finally, the contribution of this research is found in the following areas: A new DSS development methodology; The development and validation of a new research questionnaire (i.e. ARDSSQ); The development of the admission and registration data warehouse; The evaluation and use of cluster analysis proximities and techniques in the KDD process to find knowledge in the students' records; And the development of the ARDSS software that encompasses the advantages of the KDD and DW and submitting these advantages to the senior admission and registration managers in the Egyptian Universities. The ARDSS software could be adjusted for usage in different countries for the same purpose, it is also scalable to handle new decision situations and can be integrated with other systems.

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Thesis (MBus)--University of South Australia, 1998