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Towards an integrated approach to knowledge management: ‘hard’, ‘soft’ and ‘abstract’ issues

Hlupic, Vlatka, Pouloudi, Athanasia, Rzevski, George·2002·Knowledge and Process Management

Abstract

Companies are often unaware of the potential value of corporate information and extent of data held in their systems. It is increasingly argued that analysis of such data and information can be transformed into knowledge that in turn can be used to gain business benefits such as a competitive advantage, minimization of costs, improved quality, responsiveness, or improved service to customers. This paper investigates the benefits of a systematic and interdisciplinary approach to research in knowledge management, particularly in investigating technical (‘hard’), organizational (‘soft’), as well as philosophical (‘abstract’) aspects of the concept. It describes in detail the framework used in research undertaken by the Centre for Knowledge and Business Process Management at Brunel University. We argue that this framework is useful to researchers and practitioners alike as it contributes to a systematic and more effective knowledge management approach. Copyright © 2002 John Wiley & Sons, Ltd.

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Metadata

Authors
Hlupic, V., Pouloudi, A., & Rzevski, G.
Venue
Knowledge and Process Management
Publisher
Wiley
Published
2002-04
Volume
9
Issue
2
Pages
90-102
ISSN
  • 1092-4604 (ISSN-L)
  • 1099-1441
OpenAlex
W2130716482
Cited by
187
References
0
FWCI
9.20
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Towards an integrated approach to knowledge management: ‘hard’, ‘soft’ and ‘abstract’ issues. https://app.cochat.ai/papers/10.1002/kpm.134
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Hlupic, V., Pouloudi, A., & Rzevski, G. (2002). Towards an integrated approach to knowledge management: ‘hard’, ‘soft’ and ‘abstract’ issues. Knowledge and Process Management. https://doi.org/10.1002/kpm.134

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.