Introduction: The term Business Intelligence arose in the mid-1990s and is a growing share of the IT market. The need of Business Intelligence emerges from an increasing competition and a constantly changing and more complex business climate. Problem discussion: There are only few examples of research dealing with data-driven decision processes. How data are incorporated in decision making processes is crucial for the future use of decision support systems. The literature stress that managers must use more analytics and rationality to make better and more appropriate decisions. However, previously studies have indicated that intuition still plays a major role in decision making, even in organizations using Business Intelligence. With this background the following research question is presented: To what extent are Business Intelligence systems used to support decisions in organizations? Purpose: The purpose of this study is to describe and observe Business Intelligence from a decision making perspective. Method: The primary source of data is personal interviews and one observation study, which implies a qualitative method. The respondents are an organization in the start-up phase, IT-consultants and suppliers and current Business Intelligence users. An abductive approach is applied, and the analyses of data is done simultaneously as the examination of literature and previously made studies. Findings: The system is mainly used for producing reports and as a provider of information. More information and more detailed information are accessible due to the Business Intelligence system. The information itself is valued highly, it is assumed that if the decision maker has enough of information, an appropriate decision will be made. Intuition is still frequently used among the users, yet the Business Intelligence system can to some extent neutralize the user. This is due to that the system is used to confirm and follow up the intuition.

Thesis Erasmus University Rotterdam. / ook verschenen in gedrukte versie. With bibliogr., with a summary in Dutch.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 163-177).

Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. Lisa K. Spainhour, Florida State University, College of Engineering, Dept. of Civil Engineering. Title and description from dissertation home page (viewed 6/16/04). Includes bibliographical references.

Infrastructure networks that provide basic services such as transportation, telecommunications, electricity distribution, and water supply and drainage are critical for the smooth functioning of a nation’s economy and its society. To provide efficient and uninterrupted services, these infrastructure networks need to be periodically inspected, upgraded, and maintained. However, infrastructure networks are expensive to operate and maintain; many infrastructure service providers allocate more than half of their total capital investments to network maintenance and improvement. With increasing customer expectations, intensifying global competition, and challenging financial environments, the infrastructure service providers need to develop models that can optimize all of the different factors that must be taken into consideration when making important decisions related to infrastructure network inspection and maintenance. This dissertation, which consists of three essays, focuses on some of the key decision issues associated with inspection and maintenance of these large infrastructure networks. Specifically, the first two essays, respectively, address a project management problem to maintain and expand a large-scale network and a periodic network inspection problem. The third essay, motivated by the computational challenges of the first two problems, addresses the network reduction and approximation problem within the same context. These problems are deterministic optimization problems over large-scale networks, which are very difficult to solve, and have not been extensively studied in the literature. In this dissertation, we introduce new optimization models for each problem, develop theoretical and algorithmic strategies that exploit problem structures to effectively solve the problems, and implement and test these methods on actual problems using data provided by an infrastructure service provider. / text

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

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.

No description available.

No description available.

No description available.