Browsing by Subject "Decision support systems"
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Item A conceptual model and an implementation of adaptive decision support systems(Texas Tech University, 1998-12) Chuang, Ta-tao; Yadav, Surya B.; Bravoco, Ralph R.; Menon, Nirup M.; Zhang, Hong-ChaoAdaptation of decision support systems (DSS) is an important issue in DSS research. Previous research into adaptation of DSS has been focused on individual elements of traditional three-component architectures. The dissertation proposes and validates an integrated conceptual model of adaptive decision support systems (ADSS) which adapts its functions, stmcture, and interfaces in order to support the changing needs of decision makers. The conceptual model is a generic architecture for the development of domain-specific ADSS. Following a unified research methodology, the dissertation specifically addresses the following issues: 1. What is the adaptive behavior of an ADSS? 2. What knowledge and capabilities are needed to embody the identified adaptive behaviors? 3. What architecture is required to support these capabilities? Based on previous research into adaptivity of information systems, five adaptive behaviors of ADSS are identified. Eight different types of knowledge are recognized to support the five adaptive behaviors. The model of reflexive systems and a framework of decision making organization are used as theoretical foundations for stmcturing various components. The ADSS conceptual model consists of three units at two levels: the meta-level and the basic-level. Two units at the basic-level are user interface unit and problem processing unit, which are responsible for communicating with the user and carrying out the task of decision support, respectively. The meta-level unit has an introspection mechanism and a self-knowledge base, which comprise a controlling unit capable of introspecting the system's knowledge and limitations, and determining an appropriate action to adjust the capabilities of basic-level units. The notion of software agents was employed to develop a prototype system in order to examine the feasibility ofthe conceptual model. Software agents were organized in the form of a federate agent-based architecture. The field of real estate was used as the problem domain in developing the prototype. Three types of decision tasks were implemented in the prototype system. A panel of three experts knowledgeable about information systems evaluated the prototype system against five major features. The evaluation results validate the feasibility and usability ofthe conceptual model of ADSS.Item A formal structure for the evaluation of decision support system generators (DSSG): the systems approach(Texas Tech University, 1987-08) Kim, Chung SDecision Support Systems (DSS) are future-oriented decision-support tools used mostly for strategic planning tasks which are ill structured. It follows that most DSS benefits are qualitative and therefore their impacts on organizations are difficult to measure. Various approaches such as cost/benefit analysis, value analysis, and other utility methods have been used to evaluate a DSS, but unfortunately, qualitative benefits are handled in a very arbitrary and subjective manner. This dissertation attempts to construct an ex ante evaluation model for a DSS generator (DSSG) with explicit consideration of all qualitative benefits associated with a DSS. The qualitative benefits are recategorized according to user effectiveness criteria given by Alter (1980) and are incorporated along with system capabilities into a hierarchical structure using the systems approach. The Analytical Hierarchy Process (AHP) developed by Saaty (1980) and Worth Assessment (WA) described by Sage (1977) are used to determine the weights of the evaluation criteria. Then worth scores (Sage, 1977) are assigned to the criteria of system performance in order to determine the DSS Quality Index (DQI), which represents the quality of a DSSG to a given organization. This dissertation also constructs a prototypical system which can support users in their DSS evaluation process. The model and methodology addressed above become a conceptual framework for constructing the prototypical system. The system is designed to provide guidelines for users to construct their own evaluation model and to determine the priorities of the objectives and evaluation criteria in order to assess the worth of a DSSG. This evaluation process and the outputs of the system (the worth scores, the weights of the evaluation criteria, and DQI) will become a tool to monitor the development of an evolving DSS. The DQI allows an organization to assess the value of a DSSG before its implementation. The model is built as a planning mechanism that provides a direction rather than a post-audit examination. Since this model intends to support the judgments of users in the evaluation process, yet not to replace them, it will provide a convincing way of obtaining the worth of a DSSG.Item A user learning based DSS implementation methodology(Texas Tech University, 1990-05) Chatfield, Akemi TakeokaThe behavioral problem of user resistance to change and its role in systems implementation failure has been raised in MIS and related fields. The opportunity cost of unused DSS technology is substantial, because it cannot improve decision performance. Despite this recognition, extant DSS design methodologies have directed a great deal of their focuses toward the technical issues related to the design of DSS technology. These methodologies are deficient from the perspective of managing the behavioral problem and motivating DSS utilization. The purpose of this research is to provide a conceptual understanding of the behavioral problem of user resistance to change, and to identify and develop a means of resolving user resistance to change and hence motivating DSS utilization. This research presents a user-learning-based DSS implementation methodology. The methodology development is built upon prior research in MIS and related fields. A user-learning-based DSS implementation methodology consists of a user-learning model of DSS implementation, a user-learning approach to DSS implementation, a set of implementation steps, and a generic architectural model of knowledge-based user-learning support systems (KULSS). This methodology is applicable to most DSS implementation situations where user resistance to change is observed at the onset of DSS implementation. The methodology facilitates user-cognitive learning to resolve user resistance to change and to develop a felt need for DSS utilization. A set of generic KULSS commands enables the user to identify his actual decision performance, to leam a desired decision performance, and to understand how the actual decision performance differs from the desired decision performance.Item An empirical investigation of the use of expert systems by groups(Texas Tech University, 1989-08) Rajkumar, T. MGroups make a large number of decisions in organizations. Incorporation of expert system concepts within GDSS has been suggested in the past as a means of supporting the decision-making activity of groups. This study aims to find what group factors affect the use of expert systems by groups within a decision-making setting. Specifically the study examined group commitment, satisfaction, past success and cohesion as variables of interest. Competing alternative models, incorporating these variables, are proposed to explain the group use of expert systems. An expert system that supports reasoning by analogy was developed for the business simulation game IMAGINIT. The use of the expert system was found to significantly increase the problem understanding, and the average and ending performance. The expert system was also found to help in the strategic decision-making phase by the groups. A laboratory study was conducted to measure the voluntary use of this expert system. Results show support for one of the models. In this model each of the variables (commitment, satisfaction, cohesion, and past success) were viewed as independently affecting the use of expert systems. Of the variables investigated, past success was the only variable to significantly affect the use of the expert system. Groups that performed better in the past tended to attribute their performance to the usage of the expert system. This attribution resulted in an increased usage of the expert system. The other variables (commitment, satisfaction, and cohesion) did not significantly affect the use of expert systems. Because many of the group variables did not significantly affect the usage of expert systems by groups, a data-driven model was developed to derive the relationships among the group variables, and usage of expert systems. The data suggests that the correlation between the variables (commitment, satisfaction, past success, and cohesion) is caused by a common second-order factor (group behavior). This second-order factor did not significantly affect the use of the expert system.Item Comparing structured systems analysis techniques using automation supports(Texas Tech University, 1993-05) Green, Harrison D.A significant problem facing information systems managers is the choice of a methodology for describing system requirements. There are certain limitations to methodologies in common use. The problem is compounded by the recent introduction of Computer Aided Software Engineering (CASE) Tools. Purchasing such a tool often requires an initial commitment to a methodology. Focusing on the diagraming phase of Requirements Definition, a comparison framework is rigorously developed Two diagraming techniques are selected and tested in an educational setting. Since the experiment was replicated during several semesters, it falls into the category of meta-analysis. Experimental outcomes are sufficiently significant to suggest possible causation. CASE tools provided more support for output quality and ease of learning for the technique that was more rule-oriented.Item Resurrecting legacy code to revitalize software for groundwater research : reproducibility and robustness for the Barton Springs case, Texas(2016-12) Kwon, Nalbeat; Pierce, Suzanne Alise, 1969-; Kreitler, Charles W; Gil, YolandaAdvanced computing is becoming an indispensable part of geosciences, the interdisciplinary nature of which often requires large-scale and data-intensive numerical modeling. Groundwater in Texas is one such area that can greatly benefit from advanced decision support for understanding aquifer systems, uncertainty analysis, and policy making. However, software developed for research is often used for a relatively short period of time before it is abandoned or lost. The unintentional abandonment of software within the fast changing technological landscape makes model simulation results difficult to replicate, hindering widespread reusability and causing significant effort to be lost on redeveloping new software for researchers pursuing similar or adapted studies. These legacy codes are potentially important assets and may be resurrected and moved to an archive for long-term reuse. This research develops and tests methodologies to inform the design of best practices for documenting and preserving reproducible workflows and scientific software. Methodologies were tested with an existing codebase and assets from the Groundwater Decision Support System (GWDSS), originally developed in 2006 for participatory decision making and groundwater management. The original GWDSS provided a hybrid architecture for integrated assessment models by combining a numerical simulation code for groundwater (MODFLOW) with other systems dynamics and optimization components. Prior attempts to resurrect GWDSS were unsuccessful due to problems commonly experienced with scientific software, such as insufficient documentation and backward compatibility issues. This research experimented with two resurrection strategies: 1) Initially, a virtual machine (VM) approach to handle compatibility issues, which found similar obstacles in addition to the lack of provenance that would yield questionable results, and possibly inherent problems with the codebase due to uncurated changes made in the past. 2) Then efforts were redirected to writing a new application that replicates and improves many of the old functionalities of GWDSS, leveraging high-performance computing for batch processing of data while seeking to integrate new web-based technologies for data visualization. Ultimately, research efforts informed design and preparation of an ideal architecture that uses an open source framework and technology stack that enables users to easily access and use distributed data systems.Item Supporting environmental scanning and organizational communication with the processing of text: the use of computer-generated abstracts(Texas Tech University, 1988-05) Morris, Andrew H.This research proposes a model text-based decision support system designed to support the activities of environmental scanning and organizational communication by actively filtering and condensing text. To filter textbased information requires the use of automatic routing schemes; to condense text requires the use of computergenerated abstracts or extracts. A key element in the model system is the ability of the computer to condense text by generating short abstracts of documents. Two approaches to condensing text have been proposed: (1) using natural language processing techniques to construct a knowledge base of the document contents, from which to write an abstract, and (2) employing algorithm based extracting systems to generate extracts of important sentences and phrases. Systems using natural language techniques are still being researched; most are successful only in limited domains. Systems using extracting algorithms have been researched, but have not been applied to the problem of information overload in an organizational decision-making context. These two approaches were tested in a laboratory setting with student subjects.Item The effect of decision aids on decision confidence and decision success: an empirical investigation(Texas Tech University, 1995-08) Bingi, Reddi PrasadThis research investigates the effect of decision aids on decision confidence and the decision outcome. A comprehensive conceptual model of the role of decision aids on decision confidence and decision success was developed combining relevant knowledge from Management Information Systems/Decision Support Systems [MIS/DSS], decision making, motivational psychology, strategic management and organizational behavior literatures. Also, important aspects of the conceptual model were investigated empirically. The impact of the interactive characteristic of decision aids on decision confidence, and the effect of decision confidence on decision success were specifically investigated. A laboratory experiment was conducted with 253 subjects over a period of 5 weeks. The research setting involved subjects improving their reading skills. Based on the initial data, a reading specialist prepared reading plans. Subjects also interacted with a simulated expert system interface that was developed by the author. The simulated expert system delivered the reading plans prepared by the reading specialist. The subjects were trained based on the reading plans given to them, and finally, were tested at the end of the experiment to measure the improvement in their performance. Structural equation modeling was used to test the model. The LISREL VIE (personal computer version) software program was employed in the data analysis. The results showed that decision confidence had a positive effect on implementation effort, and the implementation effort had a positive effect on the outcome of the decision. The interactive characteristic of the decision aid, however, did not show any influence on decision confidence. Many other results found in the current research supported the findings in the extant research. The contributions, limitations and implications of results for researchers and practitioners are discussed.Item Voice input for decision support systems: the use of multiple discriminant analysis for word recognition(Texas Tech University, 1987-08) Parameswaran, JagadeeswaranA Decision Support System (DSS) is characterized to have flexibility, ease of use, interactive capability and the capacity to support managerial decision making in ill-structured situations. The infrastructure of a DSS has been viewed to consist of a database, a modelbase, a user interface, and perhaps, a knowledgebase. Most DSS research has been directed towards the modules of database, modelbase, and knowledgebase. The work relevant to the user interface is limited. There is conclusive evidence, showing that within a problem-solving context, voice interaction is superior to other modes in terms of speed and task efficiency. Since speech recognition is an emerging field only few commercial systems are available currently. About 5% of the recognizers sold so far are still in use. Two major problems are: i) unpredictable performance in terms of recognition accuracy ii) inexpensive systems to compromise on algorithms. This study explores the possibility of a reliable voice input module for a DSS. Specifically, Multiple Discriminant Analysis (MDA), is used in modeling a speaker-trained, isolated word recognition environment. A design framework for MDA based recognizers is proposed. It provides details of alternatives available and guidelines for prototyping. Factors such as the training effort, the number of variables, estimation of covariance matrices, word population separations, computational requirements, ease of implementation in a DSS environment e t c , lead to the choice of a Linear Multiple Discriminant Analysis (LMDA) approach. This study compares the proposed LMDA model to the model based on Dynamic Time Wrapping (DTW) on performance criteria including accuracy, storage, and computational requirements. Part of the same Texas Instruments (TI) - database which was used in evaluating seven popular commercial recognizers was used to compare the substitution error and rejection error. Training size, and order of analysis were controlled and maintained across LMDA and DTW methods. The results validate the previous work with respect to training size, in that performance improved with up to 4 repetitions. With respect to substitution error the better performance of LMDA models is statistically validated. There was no statistically significant difference with respect to rejection error. The results indicate that the LMDA performance in reduced space peaks prior to reaching the full discriminant space. Inclusion of the last few discriminant functions tends to introduce distortion. It is recommended that the LMDA model should be operated in reduced space. The computational requirements of LMDA and DTW methods are compared using analysis of algorithms. Even in full discriminant space, the LMDA approach is superior to the DTW method, with respect to computational requirements. The LMDA approach for user-trained isolated word recognition problem, involves computationally higher training cost and reduced recognition cost. This study is limited to only LMDA based user-trained isolated word recognition systems. The vocabulary size was also small. This research can be extended to a large DSS vocabulary with various interfaces modes such as command-driven or menu-driven.