The Electronic Journal of Knowledge Management publishes original articles on topics relevant to studying, implementing, measuring and managing knowledge management and intellectual capital.

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Information about the European Conference on Knowledge Management (ECKM) is available here.

For info on the International Conference on Intellectual Capital, Knowledge Management and Organisational Learning (ICICKM), click here
Information about the European Conference on Intangibles and Intellectual Capital (ECIIC) is available here
 

Journal Article

Knowledge Management System Building Blocks  pp137-148

Georg Hüttenegger

© Nov 2003 Volume 1 Issue 2, Editor: Fergal McGrath, pp1 - 226

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Abstract

This paper describes three building blocks of a technological Knowledge Management (KM) system that provides all relevant and practical means of supporting KM and thus differentiates itself from existing KM tools in goal and approach, as they usually deal with a limited range only. The three blocks described within this paper are: a virtual information pool, which utilizes Enterprise Application Integration (EAI), a single and central user interface providing ubiquitous access, and mechanisms to enrich the available data, essentially based on Artificial Intelligence and Data Mining techniques.

 

Keywords: Knowledge Management System, Virtual Information Pool, Ubiquitous Access, Machine Learning Data Mining

 

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Journal Article

Key Performance Indicators Metrics Effect on the Advancement and Sustainability of Knowledge Management  pp149-154

Mohamed Rabhi

© Apr 2011 Volume 9 Issue 2, ICICKM 2010 special issue, Editor: W.B. Lee, pp85 - 180

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Abstract

This paper addresses the relationship between the value of data and KPs as they relate to the sustainability of knowledge management (KM). Numerical data are compelling metrics to persuade executives and management in the organization of the significance of Knowledge Management. External statistics are usually less impactful than internal data. Nonetheless, and in the absence of internal data at the early phases of KM projects, many companies collect published data for comparable industries. In the present case, we compiled information from previous experiences of companies in the same line of business; therefore, management by‑in was secured, and the KM project was, to some extent, successfully implemented. However, there was a need to generate in‑house numbers to support promises and claims of KM benefits, and persuade all KM players from the technician to the organisation president; the ultimate objective is to have a sustainable Knowledge Management project across the organization, with visible, concrete, and quantifiable results. Equipped with the assertion “data is power”, Key Performance Indicators (KPIs) and other metrics were devised and integrated into our KM processes; these measurements are being pulled out systematically, and published to the whole audience. KPIs measured included the effect of KM on (i) customer satisfaction, (ii) business impact (i.e. savings), (iii) number of projects completed on time, (iv) and the number of technical reports generated per unit of research area. Over the past few years, the data we generated shows a considerable increase in customer satisfaction with our research and technical services; significant savings were obtained each year; project timely completion indicator rose to high levels as compared to previous yearly data; the electronic technical and scientific library experienced a build up of valuable know‑how reports. Knowledge re‑use as shown by reliance on internal resources was the standard and routine practice. On the other hand, many other qualitative observations, like effect on health, safety, and the environment are being quantified for inclusion in the KPI reporting. Based on the accumulated data, we believe that numerical values coupled with other tangible solid results will ensure a viable and sustainable KM in our organization. This hypothesis is supported by five year data and trend analysis. It confirms that internally generated statistics is a powerful tool to sway and re‑assure the organization that KM can indeed increase efficiency, enhance customer satisfaction, and drive savings.

 

Keywords: KM, sustainable, metrics, data, KPI, statistics, know how

 

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Journal Article

Relationship between Gross Domestic Product (GDP) and Hidden Wealth over the period 2000‑2008: An International Study  pp259-270

Víctor Raul López Ruiz, Jose Luis Alfaro Navarro, Domingo Nevado Pena

© Sep 2011 Volume 9 Issue 3, ECIC 2011, Editor: Geoff Turner and Clemente Minonne, pp181 - 295

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Abstract

In this paper we show that it is possible to measure the development and management of knowledge in a country using indicators of intellectual capital that consider non visible assets not included in Gross Domestic Product. Using this idea, we obtained a measure of the intellectual capital for 72 countries selected in accordance with the information available for 2000, 2005 and 2008. These measures allows us to verify the hypothesis that knowledge acts as a divergent factor of wealth, that is, that rich countries are richer in knowledge and manage it more efficiently than poor countries. Thus, in a global economy, intellectual capital circulates in the opposite direction to development, that is, from poor to rich countries. In this sense, economic growth in developing countries displays a stronger relationship with intellectual capital. We show how national intellectual capital anticipated the economic crisis before GDP, as real GDP averages increase in all the years considered, whereas national intellectual capital decreased in last year analysed. Moreover, we used a data panel model with common coefficients to emphasize the most influential factor in the recession in order to ascertain the areas where governments must act to overcome a crisis.

 

Keywords: economic growth, intellectual capital, international panel data models, divergent factor

 

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Journal Article

A Form to Collect Incident Reports: Learning From Incidents in the Swedish Armed Forces  pp150-157

Ulrica Pettersson

© May 2013 Volume 11 Issue 2, Editor: Ken Grant, pp116 - 182

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Abstract

In the modern business environment a greater number of organizations act worldwide and regularly meet with new cultures and environments. The change calls for a more rapid learning process than previously, in order to adjust to new situations. In order to

 

Keywords: incident report, experience-based, data collection, incident, acquiring knowledge

 

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Journal Article

Big Data and Knowledge Management: Establishing a Conceptual Foundation  pp108-116

Scott Erickson, Helen Rothberg

© Jun 2014 Volume 12 Issue 2, Special Edition for ICICKM 2013, Editor: Annie Green, pp83 - 154

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Abstract

Abstract: The fields of knowledge management and intellectual capital have always distinguished between data, information, and knowledge. One of the basic concepts of the field is that knowledge goes beyond a mere collection of data or information, incl uding know‑how based on some degree of reflection. Another core idea is that intellectual capital, as a field, deals with valuable organizational assets which, while not formal enough to rate a designation as intellectual property, still deserve the atte ntion of managers. Intellectual capital is valuable enough to be identified, managed, and protected, perhaps granting competitive advantage in the marketplace. So what do we make of current trends related to big data, business intelligence, business anal ytics, cloud computing, and related topics? Organizations are finding value in basic data and information as well. How does this trend square with the way we conceptualize intellectual capital and value it? This paper will work through the accepted lite rature concerning knowledge management (KM) and intellectual capital (IC) to develop a view of big data that fits with existing theory. As noted, knowledge management and intellectual capital have both recognized data and information though generally as non‑value precursors of valuable knowledge assets. In establishing the conceptual foundation of big data as an additional valuable knowledge asset (or at least a valuable asset closely related to knowledge), we can begin to make a case for applying intellectual capital metrics and knowledge management tools to data assets. We can, so to speak, bring big data and business analytics into the KM/IC fold. In developing this theoretical foundation, familiar concepts such as tacit and explicit knowledge , learning, and others can be deployed to increase our understanding. As a result, we believe we can help the field better understand the idea of big data and how it relates to knowledge assets as well as provide a justification for bringing proven knowl edge management strategies and tools to bear on bi

 

Keywords: Keywords: knowledge management, intellectual capital, data, information, big data, business analytics

 

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Journal Article

Mining Knowledge of the Patient Record: The Bayesian Classification to Predict and Detect Anomalies in Breast CancerŽ  pp128-139

Souad Demigha

© Aug 2016 Volume 14 Issue 3, Editor: Vincent Ribiere, pp113 - 190

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Abstract

Abstract: knowledge management, data mining, and text mining techniques have been adopted in various successful biomedical applications in recent years. Data Mining (DM) is the most important subfields in knowledge management (KM). It has been proven that data mining can enhance the KM process with better knowledge. In this paper, we investigate the application of DM techniques for mining knowledge of the patient record. The patient record represents documents of the patients examinations and treatments. Data Mining is the process of miningŽ or extracting information from a data set and transform it into an understandable structure for further use. We propose a methodology for mining medical knowledge based on the Bayesian Classification to predict and detect anomalies in breast cancer. We use the Naïve Bayes Algorithm to develop this methodology. We illustrate the knowledge mining process by real examples of medical field. We investigate through these illustrations how knowledge is better mined and thus, reused when applying concepts and techniques of Data Mining. On the other hand, we investigate the potential contribution of the Naive Bayesian Classification methodology as a reliable support in computer‑aided diagnosis of such events, using the well‑known Wisconsin Prognostic Breast Cancer dataset. Finally, we will demonstrate the suitability and ability of the Naive Bayes methodology in Classification/Prediction problems in breast cancer.

 

Keywords: Keywords: Patient Record, Data Mining, Bayesian Classification, Naïve Bayes Algorithm, Breast cancer prediction

 

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Journal Article

Engaging Layers of Intangibles Across Intelligent Learning Ecosystems for Competitive Advantage  pp36-47

Helen Rothberg, Scott Erickson

© Mar 2018 Volume 16 Issue 1, Editor: John Dumay, pp1 - 72

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Abstract

The Intelligent Learning Ecosystem (ILE) integrates all forms of intangible assets, recognizing not only tacit and explicit knowledge, but also big data and analytics/intelligence within and across organizations. The ILE structure provides a system for dynamic learning through the synthesis and analysis of intangible assets, creating decision‑impacting intelligence across the organization and its partners. Here we extend our understanding of how this ecosystem works by also considering the learning dynamics of individuals and teams. As such, the ILE not only facilitates organizational and partner learning but also leverages the positive impact of intangibles management on employee development, team sophistication and company competitiveness. Consequently, this paper studies the place of knowledge assets in a wider conceptual framework. By managing that wider range of intangible inputs with a structure designed not only to exchange existing knowledge or data but also to create new learning and insights, decision‑makers can accomplish several things. Initially, the range of potentially valuable inputs is increased, bringing in a more diverse set of intangibles that might have more relevance in specific industries or companies. Secondly, the structures can be designed not only to exchange knowledge or big data but to bring it all together, along with all other available intangibles, for analysis. As a result, new learning can take place as cross‑functional teams derive insights from the inputs. Finally, such a structure can work not only within a single enterprise but across its wider network of collaborators. The resulting intelligence learning ecosystems bring an even wider range of inputs, diverse perspectives, and opportunities for new learning to all the partners. By looking more widely at these possibilities, knowledge assets can be employed even more productively than when considered only in traditional knowledge management systems.

 

Keywords: knowledge management, big data, intelligence, learning organizations, intelligent learning ecosystem, teams

 

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Journal Article

Data Mining as a Technique for Knowledge Management in Business Process Redesign  pp33-44

Olusegun Folorunso, Adewale O. Ogunde

© Jan 2005 Volume 2 Issue 1, Editor: Charles Despres, pp1 - 90

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Abstract

Business Process Redesign (BPR) is undertaken to achieve order‑of‑magnitude improvements over 'old' form of the organisation. Practitioners in the academia and business world have developed a number of methodologies to support this competitive restructuring that forms the current focus of concern, many of which have not been successful. This paper suggests the use of Data Mining (DM) as a technique to support the process of redesigning a business by extracting the much‑needed knowledge hidden in large volumes of data maintained by the organization through the DM models.

 

Keywords: Data Mining, Knowledge Management, Business Process Redesign, Business reengineering, Artificial Neural Networks

 

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