9,277 research outputs found

    Factors shaping the evolution of electronic documentation systems

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    The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

    Multi-agent knowledge integration mechanism using particle swarm optimization

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    This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

    Team Learning: A Theoretical Integration and Review

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    With the increasing emphasis on work teams as the primary architecture of organizational structure, scholars have begun to focus attention on team learning, the processes that support it, and the important outcomes that depend on it. Although the literature addressing learning in teams is broad, it is also messy and fraught with conceptual confusion. This chapter presents a theoretical integration and review. The goal is to organize theory and research on team learning, identify actionable frameworks and findings, and emphasize promising targets for future research. We emphasize three theoretical foci in our examination of team learning, treating it as multilevel (individual and team, not individual or team), dynamic (iterative and progressive; a process not an outcome), and emergent (outcomes of team learning can manifest in different ways over time). The integrative theoretical heuristic distinguishes team learning process theories, supporting emergent states, team knowledge representations, and respective influences on team performance and effectiveness. Promising directions for theory development and research are discussed

    Use and effectiveness of decision support systems (DSS): Study of the Saudi private sector

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    In the industrialized world today, management is characterized by extensive use of computers to manage rapid change, information overload, and complex decision-making. Literature suggests that Decision Support Systems, computer packages offering information retrieval, problem-structuring models, decision alternatives, and other types of decision support, are effective extensions of human decision-making and offer substantial benefits to organizations utilizing them. In spite of overwhelmingly positive reviews for DSS, empirical literature has produced inconsistent results regarding DSS effectiveness, and definitions of “effectiveness” and of DSS itself are varied and sometimes contradictory. Distinguishing DSS from MIS (management information systems) and other types of managerial computer support has proven to be an essential part of DSS research. An additional gap in DSS research to date is that little is known about DSS use in developing countries and the potential of DSS to improve decision-making and overall organizational effectiveness. The present empirical study surveyed one member from each of Saudi Arabia\u27s largest corporations to determine to what extent DSS has been incorporated into the companies\u27 decision-making procedures. A second purpose was to determine decision-makers\u27 perceptions of the effectiveness of DSS in terms of their decision processes (time savings, availability of more alternatives, cognitive effort) as well as decision outcomes (decision accuracy and overall quality). The research revealed a high degree of use and enthusiasm for DSS, but revealed gaps in Saudi utilization of the systems. The research identified specific obstacles to more pervasive adaptation and enjoyment of benefits, including a lack of research stemming from researchers\u27 misperceptions of the private sector\u27s interest in and ability to understand Decision Support Systems

    Doctor of Philosophy

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    dissertationThe field of strategic management comprises the scientific exploration of organizational heterogeneity, scope, and performance. Subsequently, the large majority of extant theory builds predictions of organization and industry level outcomes from aggregate constructs (e.g., organizational structure, resources, routines, capabilities, institutions). Emerging interest surrounding the microfoundations of strategy, however, has begun to refocus attention on important antecedent events, specifically individual psychological and cognitive processes driving firm heterogeneity, scope, and performance. Building on the problem-finding problem-solving perspective, this dissertation adopts methodologies from both psychology and neuroscience to examine cognitive processes underlying the generation of novel and valuable solutions. Three studies exploring sources of heterogeneity in solution development are presented. The first investigates how comprehensive problem formulation and time constraints interact to determine the degree of novelty and value of complex and illdefined strategic problems. The second study, leveraging NK landscape logic, develops a theoretical model of how affect operates to enhance the generation of value-creating solutions. Specifically, two separate cognitive mechanisms and their neurological correlates are identified, producing systematic differences in both how knowledge search and recombination unfold and the types of solutions developed. The third and final study develops and tests a set of organizational routines posited to enhance the neurological processes of novel and valuable solution generation by overcoming the constraining effects of mental maps and heuristics. Microfoundational research investigating the cognitive processes of value creation effectively repositions the strategist at the center of strategic management. While early research within the field directly acknowledged and explored the psychological and cognitive foundations of firm performance and competitive advantage, continued focus on aggregate constructs and phenomena has obscured important sources of heterogeneity arising from lower levels of analysis. Building on the problem-finding problem-solving framework, this dissertation increases understanding of the cognitive processes underlying novel and valuable solution generation and lays the foundation for future research investigating models of cognition within the field of strategy

    Toward a Parsimonious Architecture for Intelligent Organizational Information Systems

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    An architecture for intelligent organizational information systems is proposed which consists of three functions: processing, communicating, and memory--any or all of which may be performed by either humans or computers. Processing occurs on a set of communicating processors with access to memory, and is defined as having three sub-functions: sensing, interpreting, and acting. The communicating and memory functions are seen to have certain basic characteristics whether described in terms from human organization or computer organization literature. The architecture may prove a useful guide for future research which begins to consider intelligent organizational information systems with increasingly synergistic roles played by humans and computers

    The organizational memory mismatch approach in the ERP usage stage

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    Enterprise Resource Planning (ERP) systems not only have a broad functional scope promising to support many different business processes. They also embed many different aspects of the company’s organizational memory. Disparities can exist between those memory contents in the ERP system and related contents in other memory media, such as the individuals’ memories, and the organizational structure and culture. Such discrepancies, called memory mismatches here, may cause various instances of ERP under-performance, thus triggering the need for coping behavior in the organization. Coping may take place in the form of organizational change, organizational learning, and software maintenance. This paper provides a theoretical framework for this organizational memory mismatch approach. The approach is applied to the ERP usage stage. It integrates the organizational, technological, and cognitive aspects of ERP systems, while combining and elaborating on the underpinning ERP and IS literature

    Creating Business Intelligence through Machine Learning: An Effective Business Decision Making Tool

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    Growing technological progressions have given rise to many issues concerning the contemporary decision making in business, which is a difficult phenomenon without Business Intelligence/ Machine Learning. The linking of machine learning with business intelligence is not only pivotal for business decision making but also for the business intelligence in totality, owing to the reason that in absence of machine learning, decision making couldn’t take place efficaciously. Machines need to learn, re-learn, and then only they can help your learning process. The below paper seeks to make this concept simple/ easy by removing the ambiguities using a general framework. In order to prove the impact of machine learning on business intelligence, we need to forecast the trends, what is going around the world – business has to stay updated, then only it can be a successful endeavour.  The paper posits the basic theories and definitions of business intelligence and machine learning. To learn from the past and forecast the future trends, many companies are adopting business intelligence tools and systems. Companies have understood the brilliance of enforcing achievements of the goals defined by their business strategies through business intelligence concepts and with the help of machine learning. It describes the insights on the role and requirement of real time BI by examining the business needs. Keywords: Business Intelligence (BI); Machine Learning (ML); Artificial Neural Networks (ANN); Self-Organizing Maps (SOM); Data Mining (DM); Data Warehousing (DW)
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