Handbook on Knowledge Management

With a plethora of commercial Knowledge Management (KM) tools and portals on the market, it has been difficult to understand the similarities and differences between these products and their role(s) in supporting various knowledge processes. This paper presents several frameworks to categorise, better appreciate the power of these tools, and relate them to common types of KM applications. These frameworks are based on the origin, characteristics, problem solving capabilities, alignment with business processes, and control (i.e., centralised versus localised) of KM Systems (KMS). The majority of commercial KM software are enterprise-wide software packages; tools that support knowledge processes at the individual level (i.e., Personal KM (PKM) tools) are seriously inadequate. Tools that foster virtual collaborations across organisational boundaries are becoming popular. For the latter, it is felt that Peer-to-Peer (P2P) computing will have a significant impact on KM at the group level in three aspects — file sharing, collaboration, and search. Criteria for the evaluation of tools and portals are outlined. KM tools are increasingly being absorbed into Portal products that host, among others, E-Business and intranet services. Emerging business models for the deployment of (technical) KM systems are also discussed. By identifying the dominant fields of KM, Artificial Intelligence (AI) and Information Retrieval (IR), it is possible to develop a broader perspective of the applicable technologies available for KM and align appropriate tools/gadgets to support various applications.

Assimilation is a critical issue for knowledge management. Knowledge may be gathered, created or converted, but if it is not assimilated, the organization will not be able to take action on that knowledge or actualize all of its potential value. As a result, unless knowledge is assimilated it will have limited use and impact on an organization. Accordingly, organizations are concerned with how to facilitate assimilation. This chapter provides an analysis of some key technologies for assimilation, focusing on knowledge storage, massaging, structuring, integration, filtering, and navigation.

Technology for knowledge management has so far focused on the management of knowledge containers. We present an approach that is oriented towards managing knowledge contents instead by identifying knowledge items at various levels of formality. This is done by providing various types of meta data that are tied to ontologies for conceptual interlinkage. Knowledge items are embedded into knowledge processes, which are supported by a suite of ontology-based tools. In order to handle this sort of rich knowledge process, we introduce a meta process that puts special emphasis on constructing and maintaining the ontology when introducing knowledge management systems. In order to elucidate our approach, we describe a case study about the building of CHAR, the Corporate History AnalyzeR.

Supporting Knowledge Management (KM) users’ ability to find the knowledge they need and connect with expertise is the essence of a KM system. To be effective, the KM system needs to make Knowledge Assets (KA) easy to find and retrieve. The users need to be able to intuitively access stored knowledge and connect to experts. Searching and locating is the process that makes this discovery of information possible. In this chapter we outline strategies, not coding techniques, that aid in initiating effective repository searches, expertise locators, and general techniques for connecting the information and knowledge available throughout the enterprise (in other applications or storage media).

Knowledge Management is a new field that seeks to exploit the combined knowledge, expertise, and experience of an organization’s people to improve its productivity, efficiency, innovation, effectiveness, and value. Information Technologies play a key role in achieving these goals but are only a small component of an overall system that must seamlessly integrate the supporting technology with people-based business processes. This holistic system must be able to acquire, assess, organize, and disseminate knowledge artifacts and facilitate critical human interactions while handling the systemic uncertainty within the data, information, and knowledge repositories. Current state-of-theart technology cannot adequately automate the required system functions with common levels of uncertainty and information volume. This chapter discusses the basic types of Information Technologies used in Knowledge Management Systems and their functional performance capabilities and limitations for acquiring and sharing knowledge assets.

This chapter concentrates on the technology for disseminating knowledge. Most of the technology in use, ranging from the telephone, to the World Wide Web, e-mail, groupware, intelligent agents, and information portals, was originally developed for other purposes and adapted for transfer use. The discussion is organized around a 3-dimensional model of the technology, the method of retrieval (search, organize, interface, feedback), and the method of dissemination for knowledge sharing (pull, push, point). In addition to describing specific digital technologies for disseminating knowledge, the chapter considers implementation issues, presents case examples, and examines non-computer-based dissemination methods. The chapter concludes by examining what technologies can and cannot do.

Organizations recognize the importance of enterprise knowledge management, but what has not been realized is the enormous potential offered by peer-to-peer (P2P) networking in managing organizational knowledge. This chapter explores how P2P can confer superior knowledge management capabilities by improved search capabilities, content sensitive addressing, and the community aspect of knowledge transfer in a P2P network. While P2P offers tremendous potential, it should be recognized that this technology is severely limited by the holdup problem. This article explores the free rider problem and suggests ways to align the incentives of participants in a P2P network.

This chapter focuses on a technology for knowledge derivation, On-line Analytical Processing (OLAP). Decision makers frequently need high-level knowledge in order to make their decisions. What they often have is an abundance of raw data from transaction processing databases. OLAP is one technology that transforms raw data into higher-level knowledge useful to decision makers, thereby deriving new knowledge. The chapter first lays out a theoretical framework for knowledge derivation in a decision support context and then locates OLAP technologies within that framework. How OLAP technologies can work is described in detail. Some of the current products on the market are categorized and presented along the dimensions of data storage and data manipulation. Results of a recent survey of OLAP adoption are discussed. Two case studies are then presented demonstrating OLAP usefulness in a business decision context. The chapter ends with a discussion of current trends in the OLAP market and some predictions based upon the theoretical framework discussed in the early part of the chapter.

With the advances of information technology and widespread diffusion of databases systems in organizations, large volumes of data are generated and collected by organizations. This dramatic expansion of data has generated an urgent need for new analysis techniques that can intelligently and automatically transform the processed data into useful information and knowledge. As a result, knowledge discovery and data mining have increased in importance and economic value. Knowledge discovery refers to the overall process of discovering useful knowledge from data, while data mining refers to the extraction of patterns from data. This chapter provides a reasonably comprehensive review of knowledge discovery and its associated data mining techniques. Based on the kinds of knowledge that can be discovered in databases, data mining techniques can be broadly structured into several categories, including classification, clustering, dependency analysis, data visualization, and text mining. Representative data mining techniques for each category are depicted in this chapter.

An expanded paradigm is needed to explain how competitive advantage is gained and held. Firms resorting to ‘resource-based strategy’ attempt to accumulate valuable technology assets and employ an aggressive intellectual property stance. However, winners in the global marketplace have been frms demonstrating timely responsiveness and rapid and flexible product innovation, along with the management capability to effectively coordinate and redeploy internal and external competences. This source of competitive advantage, ‘dynamic capabilities’, emphasizes two aspects. First, it refers to the shifting character of the environment; second, it emphasizes the key role of strategic management in appropriately adapting, integrating, and re-configuring internal and external organizational skills, resources, and functional competences toward a changing environment. Only recently have researchers begun to focus on the specifics of developing firm-specific capabilities and the manner in which competences are renewed to respond to shifts in the business environment. The dynamic capabilities approach provides a coherent framework to integrate existing conceptual and empirical knowledge, and facilitate prescription. This chapter argues that the competitive advantage of firms stems from dynamic capabilities rooted in high performance routines operating inside the frm, embedded in the firm’s processes, and conditioned by its history. It offers dynamic capabilities as an emerging paradigm of the modern business firm that draws on multiple disciplines and advances, with the help of industry studies in the USA and elsewhere.

Today, there is a growing recognition by researchers and practitioners about the importance of managing knowledge as a critical source for competitive advantage. Various assertions about competitiveness through knowledge management (KM) are consistent with results of empirical studies and lessons learned on the knowledge highways and byways. In spite of these macro-level contentions and success stories, there has been little investigation of a systematic means for studying connections between KM activity and competitiveness. This chapter advances a knowledge chain model that identifies and characterizes KM activities an organization can focus on to achieve competitiveness. The model is analogous to Porter’ s value chain and is grounded in a descriptive KM framework developed via a Delphi-study involving international KM experts. It is comprised of five primary activities that an organization’ s knowledge processors perform in manipulating knowledge resources, plus four secondary activities that support and guide their performance. Each activity is discussed in detail, including examples. Evidence is provided from the literature illustrating each activity’ s role in adding value to an organization to increase its competitiveness through improved productivity, agility, reputation, and innovation. In conclusion, we present some observations about avenues for future research to extend, test, and apply the model in business practices.

This chapter outlines six strategies for knowledge management and furnishes a quiz to help assess an organization’s current state of knowledge management. It identifies landmarks on the journey toward achieving knowledge management outcomes: value proposition, culture, infrastructure, technology, tactical approaches, and measurement. These are described in general and in detail as they relate to knowledge management. The chapter closes with guidelines for launching this journey.

Competitive productivity — doing more (of the appropriate thing) with less — is a basic factor for the continued survival of any organization or nation. Knowledgeable people are at the center of delivering this “more,” particularly by improving their personal productivity (nanoproductivity). However, improving economic or financial productivity depends more upon competitive and market contexts than merely increasing workers personal productivity. Performance productivity, in turn, can be enhanced by providing better knowledge and, to a lesser extent, by changed organizational processes, changed reward systems, and an appropriate mix of technologies. Other changes are required in culture and access to the information that people need at the time that they need it, in the form that they need it. Knowledge becomes the basic enabler of the nature and quality of all actions and hence the key to improved productivity. Many organizations have recognized this relationship and pursue the management of knowledge (combining activities related to knowledge contents, people, processes, and information and technology) — systematically and deliberately. While there are success stories with valid learning points much can also be learnt from situations where knowledge management (KM) has under-delivered. First generation KM focused on information rather than on knowledge and too often failed to produce expected results. Second generation systematic KM is more successful but is difficult and involves multidisciplinary expertise in areas often foreign to managers and business professionals. Major issues exist. Most enterprises have provided employees with training to acquire concrete operational knowledge and skills to perform routine work rather than the in-depth understanding required for independent work and innovation, which ultimately lie at the heart of both performance and economic productivity.

Agility is characterized as deriving from a balance of both the physical ability to act (response ability) and the intellectual ability to understand what to act upon (knowledge management). The relationship of knowledge management to response ability is discussed, and knowledge management’ s role is characterized as consisting of two parts: a top-down directed component based on knowledge portfolio management and a bottomup grass-roots component based on collaborative learning. Learning is considered central to all concepts, and the subjects of organizational learning, learning styles, collaborative learning modes, and communities of practice are put in perspective. Finally, a model of knowledge management is abstracted from a successful real world example for reuse in corporate environments that would pursue the benefits and operating modalities of agile enterprise. The arguments and conclusions are the results of a ten-year research effort into highly adaptable enterprise systems.

Shortly after the astronauts of Apollo 17 reached the moon, the world awakened to a new perspective of bringing a vision into reality. It required more collaboration and faith than anyone previously dared to dream. Results were wondrous — beyond expectations. Similarly, executives today are caught in a quandary. They can continue to utilize the triedand-true methodologies (unsuited for today’s economic environment) or they can experiment with the unknown and venture forth with management initiatives that project innovation, creativity, and responsible risk. This chapter contrasts classical business planning with knowledge-innovation strategy, providing a map for migrating from the former to the latter.

Over the last decade, there has been a growing recognition that intangible corporate assets have become the most valuable and fastest growing part of our economy. Today, it is clear that intellectual assets and their effective management may in fact, be the only form of sustainable competitive advantage (Tanazsi and Duffy, 2000). Thus, many companies want to increase their measurement of non-financial performance because it is considered strategic to overall firm performance (Bontis, 2000). Unfortunately, our awareness of and interest in the measurement of how well organizations are doing in this area far outstrips the practice. Practitioners and academics alike have expressed both frustration and dissatisfaction with the current state of our ability to measure almost any sort of intangible (Bontis, 2000). As a result, today’s knowledge managers are struggling to find ways to help their organizations understand and identify the true contribution of knowledge and other intellectual assets to a firm’ s success. To help make sense of the current state of both the theory and the practice of valuing the impact of knowledge management, the authors convened a focus group of knowledge managers from a wide variety of firms including: consulting, telecommunications, finance, healthcare, manufacturing and government. Members were asked to describe their problems with intellectual capital measurement and to discuss what metrics they use in their own organizations to monitor and document the impact of knowledge management. The findings of the focus group have been combined with a thorough review of the current academic and practitioner literature to provide a broad overview of the state of our understanding of knowledge and the knowledge management function in organizations.

This chapter presents a practical framework for measuring the value of investments in KM initiatives. Because the value of KM depends on each organization’s goals and people, it is not a “cookbook” of standard procedures, but rather an aid to help you identify and apply appropriate metrics for your initiative.

Knowledge management (KM) is currently in favor in organizations. As a result, today many companies are struggling to figure out just what KM means for them and their future. They are asking themselves such questions as, what is the value of knowledge? how can we best tap into company knowledge? what is the right form of KM for our organization? To begin to learn about what is actually being done with KM in organizations and to discover some of the issues knowledge managers are wrestling with today, the Queen’s Management Research Centre for Knowledge-Based Enterprises convened a Knowledge Management Forum of over twenty-five practitioners from a variety of North American companies. They described the state of KM in their organizations and discussed how they would like it to develop. This chapter presents an analysis and summary of this forum. It looks first at the variety of ways in which companies are approaching KM today, including: customer relationship management, continuous process improvement, communities of practice, and creating knowledge products. This diversity of approaches demonstrates how broadly and deeply KM is reaching into organizations. A second section examines the KM function itself — what it does and how it goes about doing it. A third documents experienced knowledge managers’ recommendations to others seeking to optimize the impact of KM in their own companies. We conclude that organizations are experimenting widely with KM and, as a result, the function is highly variable and rapidly changing.

Based on an examination of best-practice organizations, five stages common to successful KM implementation are identified. Together, they form the APQC Road Map to Knowledge Management Results. A quiz is provided to help an organization assess its current stage of KM development. Each stage is described and illustrated with examples from best-practice organizations.

Many knowledge management initiatives fail because they are not aligned with business strategies and the indicators that are used to measure success of the company and its operations. Most initiatives are primarily solution-oriented, too much focused on IT-tools or other one-dimensional solutions. Business owners lack insight about how knowledge management can benefit their operations and therefore do not demonstrate commitment to support these initiatives. This chapter describes the Knowledge Strategy Process, which is based on experiences of the authors in aligning knowledge management and business strategies in the context of companies such as Siemens AG.

The Department of the Navy is a recognized leader in the implementation of knowledge management. The DON’ s multifaceted change strategy is discussed in terms of twelve elements: creating a shared vision; building the business case; demonstrating leadership commitment; facilitating a common understanding; setting limits; sharing new ideas, words, and behaviors; identifying the strategic approach; developing the infrastructure; measuring and incentivizing; providing tools; promoting learning; and visioning an even greater future. In summary, a model of the change strategy which creates growth of knowledge and sharing is presented as the theoretical force behind this change strategy.

In 1897, Herbert H. Dow put Thomas Griswold, the Chief Engineer, in charge of reviewing new inventions for The Dow Chemical Company. In the 21st century, Dow has > 50 Intellectual Capital Managers and > 60 Intellectual Property Attorneys working with the businesses to create value from intangibles. Three key elements for this successful journey are highlighted: culture, trust, and strategy. To continue creating sustainable value growth from intangibles, the importance of having senior management support, showing real monetary results from managing intellectual capital, and investing in dedicated resources will be illustrated and discussed.

Ford Motor Company is a global car manufacturer with over 360.000 employees, and 110 manufacturing facilities in over 30 countries. For more company information visit www.ford.com. Best Practice Replication (eBPR) is Ford Motor Company’s proprietary process, with an accompanying intranet website, that collects, distributes, and tracks the value of replicating better ways of doing business across the enterprise. This initiative has proven it can deliver value from knowledge, over a five year period US$ 1.3 billion in projected value has been added to the business, and US$ 886 million in actual benefits have been recorded. This global based process is managed within Ford’s IT and eBusiness Infrastructure organization. Acknowledgment of Ford’s Best Practice Replication Process has come from sources outside the company. The process has been licensed to Nabisco, Royal Dutch Shell, and Kraft Foods.

Cisco uses a blend of knowledge management and learning management concepts, via a global and comprehensive e-learning infrastructure, to manage its intellectual capital. Knowledge management initiatives at Cisco are not exclusive to e-learning, nor are they explicitly defined as knowledge management. At Cisco, e-learning is the preferred term describing the Internet-enabling of information, training, communication, and collaboration. The end goal of Cisco’s e-learning initiative is measurable business impact. The examples covered in this chapter offer insight into how knowledge management and e-learning blend together to impact Cisco’s business results in a positive and sustainable way.

Knowledge traditionally has been the foundation of Swiss Re’s culture and a crucial component of its organizational strategy. Swiss Re defines Knowledge Management as “understanding and enhancing the information flow and decision making of the organization to leverage both for customer, shareholder, and employee value.” This chapter looks at the three strategies that comprise Swiss Re’s knowledge management initiative, using alignment, technology, and law as levers to further strengthen the firm’s knowledge assets. To illuminate further the way knowledge is used in the abstract business of reinsurance, Swiss Re’s knowledge management practices are contextualized within the prototypical knowledge management displayed by Thomas Edison.

The military is a knowledge enterprise like many businesses, governmental agencies, and universities. However, the immense size, geographical dispersion, and hazardous environment associated with the military makes it an extreme case even among large, global knowledge enterprises. Because knowledge is rarely distributed evenly through large organizations, the need for effective knowledge flow is crucial, and is particularly acute in the military. Informed by recent advances in knowledge-flow theory, this chapter describes results from an immersive case study of knowledge flow through a military joint task force operation. Results include a novel framework for classifying and visualizing diverse knowledge flows, along with a unique view into the use of knowledge for competitive advantage in a military context.

A knowledge management (KM) system is the technology platform and infrastructure that an organization employs to share its knowledge. A successful KM system is one that is easily accessible by all knowledge workers in the organization. It should fit seamlessly into current (or proposed) workflow processes of the end users and is based on a sound understanding of the organization’s culture and technology infrastructure. The knowledge assets (KAs) held by the system need to be readily accessible to the user at anytime and any place. The most important measure of success for a KM system is use. The return on investment for KM is derived through the behaviors of the knowledge workers. The KM system’s value is in the enablement of those behaviors. This chapter focuses on technological aspects of a KM system, particularly the one built and used by Microsoft Consulting Services.

This chapter provides a broad overview of university-based and other KM training worldwide, with particular emphasis on courses that can assist students in qualifying for Chief Knowledge Officer and other jobs in organizations that have KM programs. First, an overview of KM teaching categories is presented, showing the breadth of approaches that are possible. Second, we provide sample data on graduate KM courses at universities in the US and abroad, concentrating on schools of business and engineering, the two units where KM teaching predominates. Third, we summarize the current offerings of non-university KM training courses and KM certification programs. Finally, we outline the roles, qualifications, and responsibilities of jobs in the knowledge-based organization.

Organizations and business webs or networks behave as complex adaptive systems. Yet, many business modeling techniques fail to incorporate systems thinking or address the role of knowledge and intangibles in creating value. Intangibles such as knowledge play three important roles in business: as assets, as currencies, and as deliverables. Reframing enterprises as value networks can reveal both tangible and intangible value creating activities. Value networks are webs of relationships that generate tangible and intangible value through complex dynamic exchanges between two or more individuals, groups, or organizations. A simple technique to model and analyze value networks is demonstrated by examples. These examples illustrate that successful value networks operate on systems principles and an ethic of high integrity and trust.

In response to an environment of rapid change, increasing complexity and great uncertainty, the organization of the future must become an adaptive organic business. The intelligent complex adaptive system (ICAS) serves as a model for this organization that will enter into a symbiotic relationship with its cooperative enterprise, virtual alliances and external environment, while simultaneously retaining unity of purpose and effective identification and selection of incoming threats and opportunities. Eight characteristics, emergent in nature, are needed to succeed in the forthcoming, highly competitive, complex world where perceiving, interpreting and responding effectively become complicated and challenging in and of themselves. The characteristics needed to meet this challenge are presented and discussed. They are: organizational intelligence, unity and shared purpose, optimum complexity, selectivity, knowledge centricity, flow, permeable boundaries and multi-dimensionality. How these are brought into reality is the challenge of every manager and leader of the future.

Knowledge management is evolving from an inward-looking focus on enterprise effectiveness to an outward-looking one where opportunities are sought to commercialize an organization’s knowledge assets. Despite the set-backs in the dot.com economy, the Internet continues to mature as an important medium for marketing and delivering knowledge. This chapter reviews the process of how knowledge is commercialized, and introduces ten principles of Internet marketing. Particular attention is given to online knowledge marketplaces and the challenges they pose for buyers and sellers alike.

Just like knowledge management, electronic business has rapidly risen to become a major phenomenon over the past decade. Although they have blazed mostly independent trails with little interaction, there is a convergence on the horizon. This chapter examines the foundations for understanding this convergence of e-business and knowledge management. It presents a five-fold taxonomy that accommodates various e-business, e-commerce, and c-commerce perspectives. Based on observed relationships among the taxonomy’s categories, a single integrated perspective on e-business/c-commerce/e-commerce is advanced, leading to a KM characterization of e-business/c-commerce/e-commerce that explicitly recognizes knowledge, plus processes and technologies for managing it. We contend that the convergence inherent in this knowledge-oriented perspective of e-business is beneficial in furnishing a common, organized, and unified foundation for understanding and managing future electronic organizations in the knowledge economy.

Knowledge management has had a difficult gestation, but now is emerging as an impor- tant element of enterprise strategies. It is enabled by technology tools, but encompasses far more than technology. Key tools in the near future will include expertise location, just-in-time knowledge delivery and the portal. But knowledge management faces a number of challenges, and to the extent that is successfully establishes itself, the label itself is likely to fade away. Ultimately, though, a knowledge approach is necessary for the modern business and particularly in today’s economy.