Walter R. J. Baets

Organizational Learning and Knowledge Technologies in a Dynamic Environment (eBook, PDF)

• Format: PDF

Produktbeschreibung

I am not a born academic. I deliberately joined the ranks of academia at a fairly late stage as a natural progression from my professional career as an executive in what in those days was known as "decision support". My career had begun in the telecom industry before the days of deregulation in what one would call strategic planning and then I moved on to decision support in the field of banking, developing trading room software and risk management systems. As I developed decision support systems for real applications, the more I realized how very dependent these systems are on decision design. I began to question a number of basic business assumptions. I felt increasingly the need to review the way decision support systems were conceived at the time since they not only limited what one could do with computers, but also limited the decision-making capacity of executives. I thus decided to take time out from my professional obligations in order to be able to investigate the 'whys and wherefores' behind decision-making. I experienced yet another disappointment at the beginning of my academic career as I noted the academic research style prevailing in most Business Schools. The academic community was adhering to a type of research methodology based on a single view of the way humans think.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 275
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781461557739
• Artikelnr.: 44181643

Inhaltsangabe

1. The Chaos We Can Observe in Management.- 1.1. Economic phenomena.- 1.2. Information society.- 1.3. Information Technology (IT) evolution: from individual to group IT.- 1.4. Gap between technology and application.- 1.5. Speed of change.- 2. What Can We Learn from Dynamic Behavior of Systems.- 2.1. Dynamic non-linear systems behavior.- 2.2. Far from equilibrium behavior.- 2.3. Computer sciences and knowledge creation: experiments with artificial life.- 2.4. Self-organizing systems.- 2.5. Organizations as neural networks.- 2.6. Artificial Intelligence and genetic programming.- 2.7. Implicit learning.- 2.8. Organizational learning.- 2.9. Tacit knowledge.- 3. About Knowledge, Perceptions and Learning: Managing the Mental Map of a Company.- 3.1. What do we know of managerial processes?.- 3.2. Knowledge and experience.- 3.3. Learning and mental models.- 3.4. Measurement and mapping of mind sets: a connectionist approach.- 3.5. Knowledge is not rule based, but parallel, or how connectionism is positioned against rule based systems.- 3.6. The corporate mental map: what is important ?.- 4. Artificial Intelligence for Knowledge Management and Learning.- 4.1. Artificial Neural Networks.- 4.2. Fuzzy logic.- 4.3. Intelligent systems.- 5. Experiments with Neural Networks.- 5.1. Market response models for fast moving consumer goods.- 5.2. Brand imaging.- 5.3. Introduction strategies in new foreign markets.- 6. Experiments with Complexity and Dynamic Systems.- 6.1. Perception mapping in banking.- 6.2. Client profiles for a more aggressive and better informed front-office.- 6.3. Risk management.- 6.4. Quality management.- 6.5. Dynamic thinking.- 7. IT Support for Organizational Learning: the Organization of Knowledge Networks.- 7.1. The necessary and sufficient conditions for designing and using knowledge networks.- 7.2. The necessary condition for knowledge networks: a learning culture.- 7.3. The sufficient condition for knowledge networks: the integration of Information and Knowledge Technologies.- 7.4. From organized Information Technology to a non-structured emergent knowledge environment: the role of Intranet technologies.- 7.5. The corporate mission: sustainable development via knowledge networks.- 8. A Roadmap of Management in a Dynamic Environment.- Epilogue: Sailing by(E).- Annex: an Epistemological Voyage through Connectionism and Positivism.- (co-authored by Gert van der Linden).- A.1. Science and scientific action.- A.2. The tradition of IS research.- A.3. The ontological and epistemological foundations of neural networks.- A.4. Some comparative properties of artificial neural networks.- Further Readings.- Author Index.