Number of found records: 61
|
MALHOTRA, Yogesh |
|
Expert systems for knowledge management: crossing the chasm between information processing and sense making.
|
|
Expert Systems with application, 2001,vol.20, pp.7-16. |
|
PDF |
|
Based on insights from research in information systems, information science, business strategy and organization science, this paper develops the bases for advancing the paradigm of AI and expert systems technologies to account for two related issues: (a) dynamic radical discontinuous change impacting organizational performance; and (b) human sense-making processes that can complement the machine learning capabilities for designing and implementing more effective knowledge management systems. (AU) |
|
expert systems; artificial intelligence; knowledge management; imformation systems; business strategy; information processing |
Assessment |
|
|
|
|
MALHOTRA, Yogesh |
|
Knowledge management and new organization forms a framework for bussiness model innovation
|
|
Information Resources Management Journal, 2000, vol.13, n.1, pp.5-14. |
|
PDF |
|
The concept of knowledge management is not new in information systems practice and research. However, radical changes in the business environment have suggested limitations of the traditional information-processing view of knowledge management. Specifically, it is being realized that the programmed nature of heuristics underlying such systems may be inadequate for coping with the demands imposed by the new business environments. New business environments are characterized not only by rapid pace of change, but also discontinuous nature of such change. The new business environment, characterized by dynamically discontinuous change, requires a re-conceptualization of knowledge management as it has been understood in information systems practice and research. One such conceptualization is proposed in the form of a sense-making model of knowledge management for new business environments. Application of this framework will facilitate business model innovation necessary for sustainable competitive advantage in the new business environment characterized by dynamic, discontinuous and radical pace of change. (AU) |
|
Knowledge Management Systems; Business Model Innovation; E-Business Models; Information; Systems Practice and Research |
Assessment |
|
|
|
|
MALHOTRA, Yogesh |
|
From information management to knowledge management: Beyond the Hi-Tech Hidebound Systems.
|
|
K. Srikantaiah & M.E.D. Koenig (Eds.), Knowledge Management for the Information Professional. Medford, N.Y.: Information Today, 2000, pp. 37-61. |
|
PDF |
|
Most extant knowledge management systems are constrained by their overly rational, static and acontextual view of knowledge. Effectiveness of such systems is constrained by the rapid and discontinuous change that characterizes new organizational environments. The prevailing knowledge management paradigm limits itself by its emphasis on convergence and consensus-oriented processing of information. Strategy experts have underscored that the focus of organizational knowledge management should shift from 'prediction of future' (that cannot be computed) to 'anticipation of surprise'. Such systems may be anabled by leveraging the divergent interpretations of information based upon the meaning-making capability of human beings. By underscoring the need for synergy between innovation and crativity of humans and the advanced capabulities of new information technologies, this article advances current thinking about knowledge management. (AU) |
|
knowledge management systems; information management; organizational knowledge |
Assessment |
|
|
|
|
MALHOTRA, Yogesh |
|
Why knowledge management systems fail? Enablers and contraints of knowledge management in human enterprises
|
|
K. Srikantaiah & M.E.D. Koenig (Eds.), Knowledge Management Lessons Learned: What works and What Doesn’t. Medford, N.Y.: Information Today, 2003. |
|
PDF |
|
Drawing upon lessons learned from the biggest failure of knowledge management in recent world history and the debacle of the 'new economy' enterprises, this chapter explains why knowledge management systems (KMS) fail and how risk of such failures may be minimized. The key thesis is that enablers of KMS designed for the 'knowledge factory' engineering paradigm often unravel and become constraints in adapting and evolving such systems for business environments characterized by high uncertainty and radical discontinuous change. Design of KMS should ensure that adaptation and innovation of business performance outcomes occurs in alignment with changing dynamics of the business environment. Simultaneously, conceiving multiple future trajectories of the information technology and human inputs embedded in the KMS can diminish the risk of rapid obsolescence of such systems. Envisioning business models not only in terms of knowledge harvesting processes for seeking optimization and efficiencies, but in combination with ongoing knowledge creation processes would ensure that organizations not only succeed in doing the thing right in the short term but also in doing the right thing in the long term. Embedding both these aspects in enterprise business models as simultaneous and parallel sets of knowledge processes instead of treating them in isolation would facilitate ongoing innovation of business value propositions and customer value propositions. (AU) |
|
Design of Successful Knowledge Management Systems; Enablers and Constraints of Knowledge Management; Adaptive Systems for Radical Discontinuous Change; Knowledge Harvesting and Knowledge Creation; Information Processing and Sense Making; Strategic; Social; and; Knowledge Management; Business Value Propositions and Customer Value Propositions; Failure; Business Model Innovation; |
Assessment |
|
|
|
|
