Application of Multi-Dimensional Metric Model, Database, and WAM for KM System Evaluation

Application of Multi-Dimensional Metric Model, Database, and WAM for KM System Evaluation

D. Venkata Subramanian (Department of Computer Science and Engineering, B.S. Abdur Rahman University, Chennai, Tamilnadu, India) and Angelina Geetha (Department of Computer Science and Engineering, B.S. Abdur Rahman University, Chennai, Tamilnadu, India)
Copyright: © 2012 |Pages: 21
DOI: 10.4018/jkm.2012100101
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Abstract

A Knowledge Management (KM) System plays a crucial role in every industry as well as in Higher Learning Institutions. Based on the earlier research works, the authors have identified the gaps, and challenges in order to develop a comprehensive KM System framework, Hybrid Evaluation method which are helpful to assess any given KM system. The primary goal of this research paper is to propose the methodology for ranking and rating of the KM system using Multi-dimensional Metric Model, Metric Database and Weighted Arithmetic Mean (WAM) method. This paper first provides the background for KM System Technology Framework and then enlists some of the significant research works, carried out in the past two decades especially on the KM Success Factors and Models, which support and validate their success. Secondly, this paper describes and illustrates how a Multi-dimensional Metric Model and Metric Database can be formed. Finally, this paper discusses how the KM Systems are evaluated and ranked for their effectiveness through the proposed evaluation methodology.
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Introduction

The philosopher Plato defined knowledge as “justified true belief.” Sir Francis Bacon’s quoted “Knowledge is Power” (Meditations Sacrae, 1597). O’Leary (1998) describes Knowledge as something that humans acquire from processed information, by using data which includes their experience, values, insights, and contextual information and helps them evaluate and incorporate new experiences and information. Davenport and Prusdak (1998) defined knowledge as a fluid mixture of experience, values of contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. In most organizations, Knowledge has been embedded in documents and repositories, in organizational routines, in processes, practices, and norms. Knowledge is viewed as a justification of personal belief that increases an individual’s capacity to take certain action. Knowledge resides in the user and not in the collection of information as quoted by Churchman (1971).

Alavi and Leider (1999) defines Knowledge management (KM) as a process for systematically acquiring, organizing, and communicating knowledge of employees so that other employees may make use of it to be more effective and productive in their work. Knowledge Management (KM) and Knowledge Engineering (KE) have become very important concepts in recent years. KE is an information technology configuration control management tool used to exploit intellectual and technology assets for the benefit of the organization. KE can also be defined as the aspect of systems engineering which addresses uncertain process requirements by emphasizing the acquisition of knowledge about a process, product or technology, and representing this knowledge in a Knowledge-Based System. Knowledge Management (KM) comprises a range of practices used by organizations for reuse, awareness and learning. KM also aims to provide an innovative methodology for creating and modifying to promote knowledge creation and sharing. Subramanian and Geetha (2011), pointed out that the underlying psychological, cultural, social, security and economical issues that are considered to be important when thinking about Knowledge Sharing and Knowledge Management.

Knowledge Management System (KMS), defined by Alavi and Leidner (2001), as an IT-based system developed to support and enhance the processes of knowledge creation, storage/retrieval, transfer and application. The KM framework further advanced by Van der Spek and Spijkervet (1997) identifies a cycle of four KM stages. Fennessy (2002) avers that, the roles, values and norms of the knowledge workers will cause an impact on the development and implementation of any solution that is arrived at by the organization. There are different architectures and techniques have been proposed to design and implement KMS. Some of the techniques which are found to be useful are, Intelligent Agents based system proposed by Van Elst, Dignum, and Abecker (2003), multi-agent-based architecture developed by Gandon (2000) and KMS technology Framework proposed by Subramanian and Geetha (2011). Maier (2002) indicates that IT solution will not be suitable for all KM initiatives and hence expands the supporting enabler as ICT system that supported the functions of knowledge creation, construction, identification, capturing, acquisition, selection, valuation, organization, linking, structuring, formalization, visualization, distribution, retention, maintenance, refinement, evolution, access, search, and application.

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