<p> Knowledge management is one of the current "buzzwords" gaining popularity on an almost-daily basis within the business world. Much attention has been paid to the theory and justification of knowledge management (KM) as an effective business and organizational practice. However, much less attention has been paid to the more specific issues of effective <u>implementation</u> of knowledge management, or to the potential financial benefit or payoff that could potentially result from an effective system implementation. As the concept of KM becomes more generally accepted, knowledge management systems (KMS) are becoming more prevalent. A KMS is often considered simply another information system to be designed, built, and supported by the IT department. In actual implementation, many KM system development efforts are not successful. There is frequently a perception that strict adherence to development processes produces an excessive time lag, rigor, and formality which will "disrupt" the desired free flow of knowledge. Professor Michael Stankosky of GWU has posited a more flexible variation of the usual systems engineering (SE) approach, tailored specifically to the KM domain and known as Enterprise Management Engineering<sup>©</sup> (EME). This approach takes the four major pillars of KM as identified by GWU research in this area—Leadership, Organization, Technology, and Learning—and adapts eighteen key SE steps to accommodate the more flexible and imprecise nature of "knowledge". </p><p> Anecdotal study of successful KMS developments has shown that many of the more formal processes imposed by systems engineering (such as defining strategic objectives before beginning system development) serve a useful purpose. Consequently, an integrated systems engineering process tailored specifically to the KM domain should lead to more successful implementations of KM systems. If this is so, organizations that have followed some or all of the steps in this process will have designed and deployed more "successful" KMS than those organizations that have not done so. To support and refine this approach, a survey was developed to determine the usage of the 18 steps identified in EME. These results were then analyzed against a objective financial measurement of organizational KM to determine whether a correlation exists. This study is intended to test the validity of the efficacy of the EME approach to KM implementation. </p><p> For the financial measurement data, the subject list of organizations for this study used a measure of intangible valuation developed by Professor Baruch Lev of NYU called Knowledge Capital Earnings <sup>©</sup> (KCE). This is the amount of earnings that a company with good "knowledge" has left over once its earnings based on tangible financial and physical assets have been subtracted from overall earnings. KCE can then be used to determine the Knowledge Capital (KC) of an organization. This in turn provides two quantitative measures (one relative, one absolute) that can be used to define a successful knowledge company. </p><p> For this study, Lev's research from 2001 was updated, using more recent financial data. Several of these organizations completed a survey instrument based upon the 18 points of the EME approach. The results for the 18 steps were compared against each other and against each organization's KC scores. The results show that there <u>is</u> a significant correlation between EME and the relative KC measurement, and select EME steps do correlate significantly with a high KC value. Although this study, being the first validation effort, does not show provable <u>causation</u>, it does demonstrate a quantifiable <u>correlation</u> and association between EME and successful KM implementation. This in turn should contribute to the slim body of objective knowledge on the design, deployment, and measurement of KM systems.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:3614805 |
| Date | 18 June 2014 |
| Creators | Mac Garrigle, Ellen F. |
| Publisher | The George Washington University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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