Towards a Comprehensive Process Model for Transitioning MIS to KMS

Towards a Comprehensive Process Model for Transitioning MIS to KMS

Ricardo Anderson, Gunjan Mansingh
Copyright: © 2016 |Pages: 17
DOI: 10.4018/IJKM.2016010101
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Abstract

Information Systems today are dominated by large amounts of computing infrastructure often mapping business processes to people and data. The conversion of this data into meaningful information is fairly well established, although these systems have not been extensively exploited within developing countries. Even in developed economies, where resources and experience flourish, many still struggle with moving from information management to knowledge management. Given that knowledge is posited as the new organizational wealth, it becomes important to integrate knowledge into improving the business and its operations. In this study, a comprehensive process model that guides the conversion of an existing information system to a knowledge management system is developed and evaluated. This is primarily applicable in the developing country context. The results indicate that the model sufficiently represents and organizes the activities to be carried out to meet the desired outcome of converting an existing information system into a knowledge management system.
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Researchers have provided different definitions and classifications of knowledge. Leonard and Sensipar (1998) define knowledge as relevant, actionable information based partially on experience. O’Dell et al (1998) suggest that knowledge is what members of an organization know about their customers, products, successes and mistakes. The authors adopt for the purpose of this study that knowledge is a ’fluid mix of framed experience, values, contextual information and expert insight that provide a framework for evaluation and incorporating new experiences and information (Davenport and Prusak, 2000). This differs significantly from information and data which are lower according to the data-information-knowledge-wisdom (DIKW) hierarchy (Rowley, 2007). Data is a collection of facts and figures, while information is organized data that provides meaning. Context, experiences, and expert insights can then be added to information to produce knowledge.

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