Strategies for Successful Implementation of KM in a University Setting

Strategies for Successful Implementation of KM in a University Setting

Vittal S. Anantatmula (Western Carolina University, USA) and Shivraj Kanungo (George Washington University, USA)
DOI: 10.4018/978-1-60566-709-6.ch014
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Abstract

Research has identified enabling factors and inhibitors for implementing knowledge management successfully and to accomplish its strategic objectives. However, it is important to understand how these factors interact with each other to improve or inhibit the performance. With this in mind, this chapter presents a model, based on a research study, to determine underlying relations among these factors and develop strategies implementing KM initiatives.
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Introduction

Knowledge accumulated over centuries is often manifested in the form of ethics, culture, as well as technological, social, and economic developments of a society. At the organizational level, its growth in terms of wealth, collaborative working culture, business processes, and productivity are true reflections of its accumulated knowledge. In short, knowledge is linked to progress in practically every aspect of our lives. In the current economy, advances in information technology and communication systems have encouraged, and in some instances, compelled organizations to develop and institutionalize process for the creation, transfer, and management of knowledge.

It is, therefore, not surprising that research recognizes knowledge as a key economic resource. In particular, knowledge creation and subsequent sharing of this new knowledge and innovation are critical for organizations to gain and retain competitive advantage. Clearly, knowledge is considered a critical resource for sustaining competitive advantage.

What is Knowledge?

Before we define knowledge, it is important to understand the most commonly used terms - data, information, and knowledge - which are often used while referring to knowledge.

The term “data” is used to refer to facts. Further, data consist of unprocessed facts (Edwards & Kidd, 2003). Unprocessed facts are organized to generate information; when used by someone to solve a problem, information in turn becomes personal knowledge (Ellis 2003). Data can be transformed into information by organizing and/or processing them to derive meaningful and logical conclusions. Therefore, deriving information from data is associated with a purpose (Edwards & Kidd, 2003).

Knowledge is derived from thinking, and it is a combination of information, experience and insight. Insight, in particular, is developed with the use of tacit knowledge. Deriving knowledge from information also requires human judgment, and is based on context and personal experience.

It is, therefore, logical for information to be considered a subset of knowledge as it denotes understanding of the data. However, this understanding is personal, since one can view unique but different patterns in the information, which become the personal knowledge. When we document such personal knowledge in some form or other, it becomes explicit knowledge, an intellectual asset that can be shared among people with organization.

Additionally, as we move from data to information and then to knowledge, the context and meaning tend to increase. Obviously, data, information, and knowledge fall into a transformation of hierarchy or progression (Edwards & Kidd, 2003).

We can make a distinction between information and knowledge using the basis of how they are created. Information is created by deduction whereas knowledge is created by induction (Fernandes 2000). Further, uncertainty grows as we progress from data to information to knowledge (Berztiss, 2001). Berztiss observed that there is no uncertainty in data, some uncertainty in information, and even more uncertainty in knowledge.

As a resource, knowledge increases its value with the use. Ironically, knowledge tends to remain dormant, and not very useful, until it is reflected in action (Rad & Anantatmula 2005).

Therefore, managing knowledge in organizations is a challenge not only because it is hard to identify but also because it is even more difficult to value and deploy relevant knowledge to gain a competitive advantage in the market place (Dutta, 1997).

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