Thesis (Ph. D.)--Syracuse University, 2009. / "Publication number: AAT 3381579 ."

Thesis (Ed.D.)--University of Central Florida, 2009. / Adviser: William C. Bozeman. Includes bibliographical references (p. 315-321).

Thesis (M. Soc. Sc.)--University of Hong Kong, 1980.

Not available / text

The advances in library net working technology has brought an inexpensive way of distributing and sharing information within the organization as well as libraries located in remote areas.The advantages of Intranet, Extranet and Internet being numerous, includes streamlining of the information processing and management, facilitating information dissemination and enriching communications and collaborations. Attempts have been made to discuss opportunities provided by these three advanced networks enabling librarians and information professionals in efficient collection development, management and serving users with value added information at ease.

Artificial Intelligence Lab, Department of MIS, University of Arizona / The strategic planning process is dynamic and complex. Including a Group Support System (GSS) in the problem-solving process can improve the content quality of the strategic plan by allowing increased participation by more members of the organization. However, it can also add to the complexity of the problem by increasing the quantity of textual information that can result from group activity. Added complexity increases cognitive overload and frustrations of those participants negotiating the contents of the strategic plan. This article takes a multi-agent view of the strategic planning process. It considers group participants as multiple agents concerned with the content quality of the strategic plan. The facilitator agent is responsible for guiding groups in the strategic plan construction process as well as for solving process problems such as cognitive overload. We introduce an AI Concept Categorizer agent, a software tool that supports the facilitator in addressing the process problem of cognitive overload associated with convergent group activities by synthesizing group textual output into conceptual clusters. The implementation of this tool reduces frustrations which groups encounter in the process of classifying textual output and provides more time for discussion of the concepts themselves. Because of the large amount of convergent activity necessary for strategic planning, the addition of the AI Concept Categorizer to the strategic planning process should increase the quality of the strategic plan and the buy-in of the participants in the strategic planning process.

Artificial Intelligence Lab, Department of MIS, University of Arizona / In this research we adopted an artificial intelligence (AI) approach to designing an automatic concept classification tool for electronic brainstorming output. The role of AI techniques such as machine learning and neural networks computing in groupware development can be significant. Through extensive content analysis, concept space generation, and neural network-based concept classification, our system can generate a tentative list of the important ideas and topics represented in meeting comments. Participants then can examine the systemâ s suggested list and the underlying comments. They can also revise or augment the list to produce their final consensus list. Allowing the system to act as an â intelligentâ aide for idea organization can alleviate some of the burdens of convergent tasks.

<p> Automated workflows are used to assist petroleum engineers in maximizing the productivity of oil reservoirs. However, for a workflow to be successful, end users must adopt the workflow. The purpose of this quantitative, ex post facto, correlational study was to determine the relationship between acceptance of automated workflow technology and attitudes toward automated workflow adoption. Participants included a cluster sample of 100 randomly selected petroleum engineers from five randomly chosen companies in the oil industry in the Middle East. Predictor variables were measured with a version of the Technology Acceptance Model (TAM) modified to address the issue of workflow adoption. The outcome variable was user attitude to automated workflow adoption, as measured with the Attitude subscale of the Theory of Reasoned Action (TRA) scale. A single multiple linear regression model was computed to answer all research questions. The results for the overall model were significant, adjusted <i>R²</i> = .43, <i>F</i>(6, 93) = 13.28, <i>p </i>&lt; .001. After correcting for the number of years of engineering experience of the participants, attitudes toward using automated workflows were predicted by perceived ease of use, &beta; = 0.30, <i>p</i> = .01; perceived usefulness, &beta; = 0.24, <i>p</i> = .03; and computer self-efficacy, &beta; = 0.20,<i>p</i> = .02. The level of automation, &beta; = 0.12,<i> p</i> = .17, and perceived level of support, &beta; = 0.08, <i> p</i> = .32, did not predict attitudes. The number of years of engineering experience was unrelated to attitudes to automated workflow adoption. These findings indicated three factors that have an effect on adoption of automated workflows: perceived ease of use, perceived usefulness, and the level of computer self-efficacy of the end-user. Areas for future research included confirmation of the reliability of the Level of Automation and the Perceived Level of Support subscales designed for this study. There is also a need for future research regarding how the level of automation and level of support affect the adoption of other technologies.</p>

The realization that knowledge constitutes a key organizational resource and should be managed effectively gave rise to the field of knowledge management (KM). Since then, the field has experienced tremendous growth as measured by the number of research papers, books, conferences, and consulting services. The lack of a theoretical foundation, however, has resulted in the proliferation of KM definitions and models with little underlying coherence among them. This research represents an attempt to address this shortcoming by developing and investigating a theoretically grounded model for KM. The focus of the research is twofold. First, a conceptual model is developed that combines tenets of evolutionary theory, organizational learning and organizational memory (OM) into a single integrated model. The model proposes that organizational knowledge evolves through four recursive stages. At each stage, a process is required to manage the evolving knowledge, namely, knowledge scanning process, knowledge evaluation process, knowledge transfer process, and knowledge application process. The model further proposes that, as it evolves, knowledge is enabled by and embedded in OM infrastructures namely, individuals, roles, business logic, artifacts and culture. Second, the conceptual model is then examined within an organizational setting to gain an understanding of how each of the knowledge processes and OM infrastructures function inter-dependently to contribute to the management of knowledge. Using a case study methodology, an investigation is conducted within the context of new service development in three different lines of business (LOB) (referred to as TM, PD and MM) of a major logistics company. The research shows that the LOBs with enhanced KM capability (i.e., TM and PD) share three dominant aspects: articulable (i.e., their knowledge processes and OM infrastructures are well-defined and well understood at each stage of knowledge evolution); supportive (i.e., their knowledge processes and OM infrastructures at each stage are aligned with the goals of the respective stages); and equifocused (i.e., they are attentive to all the stages of knowledge evolution). In contrast to KM at these two LOBs, KM at MM is lacking in all three key aspects. / Thesis (Ph.D, Management) -- Queen's University, 2008-05-29 13:03:39.738

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