The Electronic Journal of Knowledge Management aims to publish perspectives on topics relevant to the study, implementation and management of knowledge management
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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  pp101-109

Scott Erickson, Helen Rothberg

© Jun 2014 Volume 12 Issue 2, Special Edition for ICICKM 2013, Editor: Annie Green, pp89 - 162

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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

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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Journal Article

Folksonomies, Collaborative Filtering and e‑Business: is Enterprise 2.0 One Step Forward and Two Steps Back?  pp411-418

Kevin Johnston

© Jan 2008 Volume 5 Issue 4, Editor: Charles Despres, pp347 - 550

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Abstract

Enterprise2.0 is the use of emergent social software tools to improve knowledge sharing and collaboration within and between firms, their customers and partners. This paper proposes that Enterprise2.0 is a double‑edged sword and should be adopted cautiously. Emerging trends in e‑business are specialisation and collaboration, creating a diverse population of organisations, each tightly defined by its core competences, interacting in a constant sequence of transient relationships, each motivated by a particular market opportunity. These dynamic business networks depend on the establishment of appropriate platforms and global standards to enable smooth interaction between the service components, in particular, appropriate metadata such as ontologies. The dynamism of such an interconnected yet free‑ wheeling economy is constrained unless risks relating to investment in a new business relationship are reduced to levels where the risk‑reward ratio favours agility rather than inertia. For its advocates, Enterprise2.0 techniques promise to contribute to the evolution of dynamic, agile, collaborative e‑commerce. However, its egalitarian and permissive nature creates challenges. Folksonomies allow a more customer‑centric view of an organisation's value proposition but may also undermine carefully devised official ontologies. Collaborative filtering may provide a mechanism for mitigating risk but the trust created is dependent upon the perceived credibility of the reviewers. A high profile example of an initiative designed to facilitate dynamic e‑commerce which failed due to unsatisfactory classification of its members and the perceived risk of interacting with unknown reputations is examined. Recent academic research and practical applications that address these conflicts are reviewed.

 

Keywords: Enterprise 2.0, ontology, folksonomy, metadata, collaborative filtering, trust

 

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Journal Issue

Volume 9 Issue 2, ICICKM 2010 special issue / Apr 2011  pp85‑180

Editor: W.B. Lee

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Editorial

Prof. W.B. Lee is Director of the Knowledge Management Research Centre of The Hong Kong Polytechnic University.    Prof. Lee is the editor of the Journal of Information and Knowledge Management Systems, and International Journal of Knowledge and Systems Science. He established the Knowledge Solution Laboratory, the first of its kind in Hong Kong and has pioneered research and practice of knowledge management and knowledge audit in various organizations.  Prof. Lee and his team have launched Asia’s first on‑line MSc. Program in Knowledge Management.  His research interest  includes manufacturing systems, knowledge management, organizational learning and intellectual capital‑based management.

Editorial

The 7th International Conference on Intellectual Capital, Knowledge Management & Organizational Learning (ICICKM 2010) was hosted by the Knowledge Management Research Centre ,The Hong Kong Polytechnic University, Hong Kong, China, the first time in Asia.

The conference is well attended by more than 100 delegates from over 30 countries and regions.  This conference series is unique in the sense that it unifies all the important themes in this multidisciplinary area which can be pursued from either the knowledge management, intellectual capital management or organizational learning perspectives or any combinations of them.  The relationship between these themes is important. It is  only  through  the effective management of our knowledge assets  and the continuous  learning   of   individuals, teams and  organization  that we  are able to build the intellectual capital which is the underlying power driving corporation’s future growth.

Apart from the rich tacit knowledge exchange among delegates during the conference, the conference proceedings give a good record of papers delivered at the conference. Our thanks and appreciation go out to all those who presented papers and participated in the conference. Feedback to date from delegates and participants has been extremely positive. The support from departments within the University and our session Chairs and Keynote speakers is gratefully acknowledged. We also recognize the efforts of both the Executive and Conference Committees for their contribution to the double blind peer review process. Based on the input of the session chairs, we are able to select 10 papers of these to be published in this electronic Journal.  These cover a lot of topics including KM models, strategy, innovation, organizational leaning, and intellectual capital measurement, and provide various new insights to the readers.

Grant started by asking the question if knowledge Management (KM) is just another fab.   Through the lens of management fashion theory and a good review from bibliometric evidence he assures us that KM is unlike other management themes and is an enduring management activity. However, there is a potential conflict between the interests of practitioners and researchers. With different perspectives and prescriptions, Imani furthers the discussion by examining the KM strategy in 18 global companies and finds out how they are linked to the business strategy, which are either formulaic (to support routine activities) or embryonic (to address corporate strategic agenda).  On the other hand, Tan and Nasurdin focus on the influence of KM effectiveness on innovation in 171 large manufacturing firms in Malaysia and find out that the effectiveness of knowledge acquisition has a positive influence on both the technological and administrative (organizational) innovation. 

Another issue of concern to researchers in this conference is on how knowledge management  is linked to business performance and its evaluation. These findings and observations are reinforced in a study conducted by Rabhi in Saudi Arabia on the effect of KM on the Key Performance Indicators (KPIs), including customer satisfaction, business savings and projects completed. Tiago et al. studied the relationship between the knowledge management and eBusiness activities by applying a structural equation model in a large database of KM activities of European and American firms. In a study of performance of a Quality Assurance Department conducted by Chan in an electronic factory, the performance of the quality management processes is related to the intellectual capital involved which is captured from a knowledge audit of the plant.

De Alvarenga Neto and Vieira from their Brazil experience described the four main components of KM Model in a Brazilian research  cooperation, that is, strategy, the environment (from social, information, cognitive and business), tool boxes, and  tangible and intangible outputs, and concluded that  for the model to be useful it should be collaboratively built  among  organization units instead of one from top‑down. Inter‑organizational and organizational learning has been recognized to be important for knowledge creation. Laursen, based on an empirical study of four organizational development projects at four Danish high schools revealdifferent perspectives on the projects set up by the staff and the management and how the perspectives have consequences  on what is actually learned by individuals as well as the whole organization.  As team learning and performance is closely related to the shared mental models of the team members, Zou and Lee explored the shared mental model of eight sigma project teams through collective sensemaking workshops conducted in an electronics factory in China. It was found that a high performance team perceived stronger interrelatedness between key teamwork concepts than average teams did.  An area that has been less studied is the effect of age diversity on knowledge transfer in workplace, which roots from the retirement of baby boom generation in many mature organizations. Wang and Dong undertook a study on some basic questions in intergenerational knowledge transfer such as analysis framework and transfer mechanism from a sociological perspective.  

Despite the diversity of topics they all tend to address on how KM performance is related to business goals, how the effectiveness is evaluated and how organizational learning takes place,  one feature of all these papers is that they all have data to support their cases and cut across various countries and cultures.  I hope this special issue serves as a timely and updated reference for the KM, IC and OL professions.

 

Keywords: Action Research, administrative innovation, BA, bibliometric analysis, data, development projects, educational partnerships, Embrapa, embryonic KM strategy, enabling contexts, , formulaic KM strategy, group quality assurance, human resource management practices, IC value tree, implementation of knowledge , innovation diffusion, innovative teaching, intellectual capital, intellectual capital statement, KM strategy, KM strategy as social practice, know-how, knowledge management effectiveness, knowledge management, , knowledge-based view of organizations, KPI, link between KM and business strategy, Malaysian manufacturing firms , management fashion, metrics, organizational coaching, organizational concepts, organizational learning, practicum, process innovation, product innovation, reflective practitioner, statistics, sustainable, taxonomy, the SET KM model, transfer of training, value added quality management processes, workplace development,

 

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