This thesis is a documentation of research on Geographical Information Technologies (GITs) as decision support tools in Road Infrastructure Maintenance (RIM) in Uganda. The main objective is to develop an operational framework within which the use of geo-information technologies can be enhanced as decision support tools in road infrastructure maintenance works of Uganda. Specifically, the research identifies the gaps and limitations in the use of and access to GITs for RIM and defines an algorithmic framework to accentuate the use of GITs in RIM. The research undertook a participatory multifaceted approach that included a review of documentation both in academia, in form of articles, journals, books, reports and research theses and also reports and documents prepared by the road infrastructure maintenance sector. Participant observations, field visits and measures, interviews and workshops were also triangularly employed to obtain the inherent answers. Content and GIS analyses were made to arrive at the findings that are documented in the papers which are part of the thesis. The gaps to using GITs in RIM have been found to include the lack of standardized datasets to address key nation-wide and local maintenance requirements, challenges on coordinating how geospatial data are acquired and utilized and the collection of duplicate data sets at the local and national levels. Also, the present institutional arrangements do not permit the formation of lasting partnerships and operating under a coordinated GIS infrastructure. The limitations to access of GITs in the sector include; the absence of policies for accessibility and standard use of GITs, lack of infrastructure to support utilization of geographic datasets, unavailability of and limited accessibility to geographic data, lack of geospatial capacity at individual and organizational levels and the digital divide. A nondeterministic algorithmic framework approach to the accentuation of GIT usage in RIM has been suggested. This framework involves strategies on; developing a policy on data collection guidelines emphasizing the use of GPS, satellite imagery and GIS, continuous undertaking of capacity building in the benefits of GIT use and the science involved, establishment of Local Spatial Data Infrastructures (LSDI) for road maintenance data and setting aside yearly budgets for the defined activities. In this framework, the dynamic segmentation data model is considered a superior data storage strategy for road maintenance data within the GIS. Dynamic Segmentation is the process of transforming linearly referenced data (also known as events) that have been stored in a table into features that can be displayed, queried and analyzed on the map through computations. It allows for the location of multiple events stored with linearly referenced attributes without any duplication with route geometry and in effect supports sharing of network infrastructure with different applications

The amount of information generated and maintained by information systems and their users leads to the increasingly important concern of information overload. Personalized systems have thus emerged to help provide more relevant information and services to the user. In particular, recommender systems appeared in the mid 1990's and have since then generated a growing interest in both industry and academia. Besides, context-aware systems have been developed to model, capture and interpret information about the user's situation, generally in dynamic and heterogeneous environments. Decision support systems like Business Intelligence (BI) platforms also face usability challenges as the amount of information available to knowledge workers grows. Remarkably, we observe that only a small part of personalization and recommendation techniques have been used in the context of data warehouses and analysis tools. Therefore, our work aims at exploring synergies of recommender systems and context-aware systems to develop personalization and recommendation scenarios suited in a BI environment. In response to this, we develop in our work an open and modular situation management platform using a graph-based situation model. Besides, dynamic aspects are crucial to deal with context data which is inherently time-dependent. We thus define two types of active components to enable dynamic maintenance of situation graphs, activation rules and operators. In response to events which can describe users' interactions, activation rules - defined using the event-condition-action framework - are evaluated thanks to queries on underlying graphs, to eventually trigger appropriate operators. These platform and framework allow us to develop and support various recommendation and personalization scenarios. Importantly, we design a re-usable personalized query expansion component, using semantics of multi-dimensional models and usage statistics from repositories of BI documents like reports or dashboards. This component is an important part of another experimentation we realized, Text-To-Query. This system dynamically generates multi-dimensional queries to illustrate a text and support the knowledge worker in the analysis or enrichment of documents she is manipulating. Besides, we also illustrate the integration and usage of our graph repository and situation management frameworks in an open and extensible federated search project, to provide background knowledge management and personalization.

This thesis reports the research efforts conducted in order to develop the Dynamic Response Recovery Tool. The DRRT was developed as a decision support tool under a holistic approach considering both emergency management research and transportation studies. The proposed system was assessed by a series of case studies in order to identify its efficiency and suitability for roading organisations. Knowledge developed from two novel research approaches are comprehensively described throughout the thesis. Initially, we report on the observation of three emergency exercises and two real events in New Zealand. This set of activities indicated the complex and dynamic environment in which emergency management takes place as well as organisational settings and management structures implemented to better respond and recover from disasters events. Additionally, a secondary approach was designed to overcome limitations identified in the observation method. In this context, a game-based scenario simulation was developed and conducted with twelve participants. With a focus in resource deployment decisions during emergencies, the game simulated an earthquake scenario in which participants had to allocate physical resources to fix damage created in a road network. Simulations indicated that Naturalistic Decision-making processes were used to respond to the scenario. Thus, resource allocation followed planning priorities defined previously the simulation, which further considered individual experiences and knowledge. Taking advantage from the findings achieved and knowledge developed by the observations and game simulations, the DRRT was designed using the conceptual background identified in the literature review. The DRRT was conceptualised as a logistics sub-system as part of the broad field of Disaster Management. In particular, the DRRT was geared towards supporting decision-making by providing procedural recommendations and identifying optimum physical deployment strategies. In order to assess the proposed system, an Information Technology application was built according to the DRRT’s specifications. A series of eleven individual and three group simulations was performed in order to assess the DRRT. Data collected through the application indicated that the DRRT enhanced decision-making during extreme events. In specific, case study participants using the system at greater levels achieved better decision-making accuracy than those disregarding completely or partially the system. Case studies also indicated that emergency management knowledge was represented by the application and its logistics model provided participants with vital information to optimise resource allocation.

Aided by advances in information technology, decision support systems (DSS) are widely used throughout organizations. These DSS are limited to support of specific structured and semi-structured management tasks for individual decision-makers and use primarily quantitative models. The next step in the evolution of DSS is to support complex, unstructured decision processes using qualitative, creativity enhancement models. The purpose of this research was to design, implement and evaluate an automated system to support complex, unstructured group decision processes. Idea generation and management in organization planning has been chosen as the domain for the system. A DSS architecture has been developed that includes a process management system component in addition to traditional data, dialogue and model management components. A group DSS and knowledge-based management system approach are central features of the system architecture. Software engineering methods were used to design, implement and evaluate the technical feasibility of the prototype system. Action research using participant and structured observation methods was used to study the (1) dynamics of the idea generation process during automated brainstorming, (2) influence of the technology on the idea generation process and (3) satisfaction of the planners with automated brainstorming for idea generation in a group setting. The findings of the research indicate that automated, networked idea generation can assist groups of planners in generating ideas during planning sessions. These ideas were accurately represented and stored and efficiently retrieved using a semantic inheritance network and frame knowledge management system implemented using a specially-designed knowledge representation language developed by the author. Over 100 planners from a variety of organizations used the system. Data indicated that computer brainstorming changed group dynamics, especially group interaction and participation. The anonymity provided by EBS neutralized social inhibitions and, in combination with the capability for parallel idea generation on the computer network, helped equalize participation. Minimal group interaction occurred. Planners using interactive computer brainstorming reported high levels of satisfaction with the process and outcome of the planning session.

Information systems have become an essential part of every business organization's production and management process. It is critical to an enterprise to integrate its organization and information systems. However, the lack of computer-supported tools for modeling organization and information systems has put their integration far beyond our reach. In this research, a metasystem approach that can integrate organization and information system modeling by means of group decision support systems (GDSS) and computer-aided software engineering (CASE) has been proposed. A prototype system, called MetaPlex, has been designed and implemented to demonstrate the feasibility of the proposed approach. The emphasis in design and implementation of MetaPlex has been on making the underlying knowledge representation expressive enough to meet modeling requirements and ensuring that the user interface is easy for managers and users to use. The use of a GDSS makes it possible to capture strategic assumptions and business objectives, as well as structures of an organization, from managers through face-to-face group meetings. The application of the metasystem concepts in generating GDSS tools makes the customization of a GDSS environment possible. Because of GDSS environment driven by a metasystem can be used to acquire information about a target system from multiple experts in a structured format that can be integrated with CASE tools, this approach provides a basis for a seamless integration of GDSS and CASE tools to support both organization and information system modeling.

This dissertation develops the Focus Theory of Group Productivity, describes the use of the theory to guide development of several electronic group support tools, and reports the results of experiments testing whether the tools yield the predicted productivity gains. Focus theory posits that to be productive group members must divide their attention between three cognitive processes: communication, Deliberation, and information access. Communication, Deliberation, and information access are, in turn, constrained by limited attention and fading memory. Finally group members are only willing to engage their attention resources to the extent that the group goal is congruent with their individual goals. Electronic tools can reduce the attention demand of each of the three cognitive processes, and focus participant attention on appropriate problem-solving behaviors. Electronic tools can foster goal congruence under some circumstances. This dissertation describes how Focus Theory guided the development of the several electronic tools to support the needs of real groups experiencing real productivity problems. It reports the results of several laboratory experiments to test the goal-congruence hypothesis of Focus Theory. The first experiment frames social loafing and social comparison as goal congruence issues, showing that subjects using a real-time graph to compare their own performance to that of an average group generated more unique ideas than a group with no basis for comparison. Facilitation techniques boosted the salience of the comparison, further increasing performance. The second study frames affective reward as a goal congruence issue and develops and validates a measure for the construct. The third study frames user interface design in terms of goal congruence and demonstrates the strengths (pointing, selecting, moving, fine motor control) and weaknesses (handwriting recognition) of pen-based interfaces in those terms. The fourth study frames the classroom as a group-productivity setting and demonstrates that group support systems can be used to improve classroom interactions.

Given the increasing problem of fraud, crime, and national security threats, assessing credibility is a recurring research topic in Information Systems and in other disciplines. Decision support systems can help. But the success of the system depends on reliable cues that can distinguish deceptive/truthful behavior and on a proven classification algorithm. This investigation aims to identify linguistic cues that distinguish deceivers from truthtellers; and it aims to demonstrate how the cues can successfully classify deception and truth.Three new datasets were gathered: 202 fraudulent and nonfraudulent financial disclosures (10-Ks), a laboratory experiment that asked twelve questions of participants who answered deceptively to some questions and truthfully to others (Cultural Interviews), and a mock crime experiment where some participants stole a ring from an office and where all participants were interviewed as to their guilt or innocence (Mock Crime). Transcribed participant responses were investigated for distinguishing cues and used for classification testing.Disfluencies (e.g., um, uh, repeated phrases, etc.), hedging words (e.g., perhaps, may, etc.), and interjections (e.g., okay, like, etc.) are theoretically developed as potential cues to deception. Past research provides conflicting evidence regarding disfluency use and deception. Some researchers opine that deception increases cognitive load, which lowers attentional resources, which increases speech errors, and thereby increases disfluency use (i.e., Cognitive-Load Disfluency theory). Other researchers argue against the causal link between disfluencies and speech errors, positing that disfluencies are controllable and that deceivers strategically avoid disfluencies to avoid appearing hesitant or untruthful (i.e., Suppression-Disfluency theory). A series of t-tests, repeated measures GLMs, and nested-model design regressions disconfirm the Suppression-Disfluency theory. Um, uh, and interjections are used at an increased rate by deceivers in spontaneous speech. Reverse order questioning did not increase disfluency use. Fraudulent 10-Ks have a higher mean count of hedging words.Statistical classifiers and machine learning algorithms are demonstrated on the three datasets. A feature reduction by backward Wald stepwise with logistic regression had the highest classification accuracies (69%-87%). Accuracies are compared to professional interviewers and to previously researched classification models. In many cases the new models demonstrated improvements. 10-Ks are classified with 69% overall accuracy.

Large opencast coal mines require a complex infrastructure to fulfill production demand and quality values. The distinct specifications required by each customer are achieved by blending adjustments. There is limited control in variability. With only partial information available, operation controllers blend coal by empirical approximation, trying to keep quality between acceptable ranges in order to avoid penalizations, shipment rejections or even contract suspensions. When a decision support system (DSS) centralized in a control room is used for blending control, crew operators visualize enhanced displays of the different sources of information, obtaining a holistic perspective of operations. Using a simulator to reproduce the blending sequence, crew operators can experiment with diverse what-if scenarios and develop blending strategies for an entire working shift, in which they also incorporate their own expertise and the knowledge obtained after interpreting the simulation results. The research focuses on the empirical analysis of the effectiveness of the DSS by studying the performance of crew users in different operating scenarios produced with a simulator. The development of a methodology for measuring this effectiveness and its impact in the quantification of controlling the variability of blending represents a significant contribution in the area of quality improvement for coal production. The effectiveness of the DSS for controlling the blending and load out processes has been numerically measured after experimenting diverse simulated scenarios, proving that the difference between estimated and actual quality delivered is narrower when using a DSS, in comparison with the BTU variability obtained from historical data. The strategies that produced better results in terms of control of coal quality variability, maximization of infrastructure utilization, time spent in making decisions and the minimization of risk for penalizations and rejections, were scored proportionally to the benefits obtained.

In order to deal with the mounting issue of Municipal Solid Waste (MSW) in a way that is in line with sustainable development and Integrated Solid Waste Management (ISWM) a systems approach is necessary.This approach can practically be integrated into the MSW decision-making process through Life Cycle Thinking(LCT) and environmental systems analysis tools such as Life Cycle Assessment (LCA). This paper is written within the context of the RECO Baltic 21 Tech (RB21T) project which aims to improve waste management practices in 12 countries in the Baltic Sea Region. The main aim of this paper is to investigate the extent to which LCA is used as decision-support in MSW management in Sweden, Estonia and Lithuania. The use of LCA is examined on a national level as well as on a local or regional level based on relevant literature and a set of interviews conducted in each country. According to the results the use of LCA as decision-support in MSW management is very limited in Estonia and Lithuania whilst it is already a well-established tool in Sweden. Most of the LCA efforts in the Baltic States have been conducted in connection with foreign projects and investments,such as RB21T. Although an actual LCA might not always be applied in Sweden, LCT is prevalent in MSW management both on a national and local level. In order for LCA to be better integrated into MSW management this paper argues that there is a need for increased knowledge, data, more user-friendly LCA-tools andstrengthening regional partnerships for further transfer of knowledge between countries.

Executive Information Systems (EIS) grew out ofthe information needs of top executives. The recent literature reports that EIS usage has spread throughout organisations. Web-based technologies are causing a revisit of existing IT implementation models, including those for EIS. These technologies include: Intranet, Internet, Extranet, e-Commerce: Business-to-Business (B2B), e-Comrnerce: Business-to-Consumer (B2C), Wireless Application Protocol (WAP) and other mobile technologies. The author conducts a field study of 31 well-established organisations in KwaZulu/Natal, South Africa, which have EIS experience. A validated survey instrument is administered to an EIS stakeholder in each organisation surveyed. This dissertation reports on (1) an investigation into previous research on IT adoption; (2) that there is little evidence to support that the theoretical usage aspects of the Technology Acceptance Model (TAM) are echoed in EIS implementations in KwaZulu/Natal; and (3) identifies and ranks Web-based technologies in order of their perceived impact on EIS currently and in the future. There is a positive impact level trend for all Web-based technologies on future EIS implementations. The results from this field study could be useful in formulating a set of management perspectives for organisations in South Africa wishing to embark on EIS implementation programs. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2002.