Description\n \n\n\n\n\n\n\n\n\n\n\n\nKnowledge Management\nin Theory and Practice\nSecond Edition\nKimiz Dalkir\nforeword by Jay Liebowitz\nKnowledge Management in Theory and Practice\nKnowledge Management in Theory and Practice\nSecond Edition\nKimiz Dalkir\nforeword by Jay Liebowitz\nThe MIT Press\nCambridge, Massachusetts\nLondon, England\n© 2011 Massachusetts Institute of Technology\nAll rights reserved. No part of this book may be reproduced in any form by any electronic or\nmechanical means (including photocopying, recording, or information storage and retrieval)\nwithout permission in writing from the publisher.\nFor information about special quantity discounts, please e-mail email@example.com\nThis book was set in Stone Sans and Stone by Toppan Best-set Premedia Limited. Printed and\nbound in the United States of America.\nLibrary of Congress Cataloging-in-Publication Data\nDalkir, Kimiz.\nKnowledge management in theory and practice / Kimiz Dalkir ; foreword by Jay Liebowitz.\n— 2nd ed.\np. cm.\nIncludes bibliographical references and index.\nISBN 978-0-262-01508-0 (hardcover : alk. paper)\n1. Knowledge management. I. Title.\nHD30.2.D354 2011\n658.4’038—dc22\n2010026273\n10\n9\n8 7\n6 5\n4 3 2\n1\nContents\nForeword: Can Knowledge Management Survive?\nJay Liebowitz\n1\nxiii\nIntroduction to Knowledge Management\nLearning Objectives\nIntroduction\n1\n1\n2\nWhat Is Knowledge Management?\n5\nMultidisciplinary Nature of KM\n8\nThe Two Major Types of Knowledge: Tacit and Explicit\nConcept Analysis Technique 11\n9\nHistory of Knowledge Management 15\nFrom Physical Assets to Knowledge Assets 19\nOrganizational Perspectives on Knowledge Management\nLibrary and Information Science (LIS) Perspectives on KM\nWhy Is KM Important Today?\n22\nKM for Individuals, Communities, and Organizations\nKey Points\n26\nDiscussion Points\nReferences\n2\n27\n27\nThe Knowledge Management Cycle\nLearning Objectives\nIntroduction\n31\n32\nMajor Approaches to the KM Cycle 33\nThe Meyer and Zack KM Cycle 33\nThe Bukowitz and Williams KM Cycle\n38\nThe McElroy KM Cycle 42\nThe Wiig KM Cycle\n45\nAn Integrated KM Cycle\n51\nStrategic Implications of the KM Cycle\n54\n31\n25\n21\n22\nvi\nContents\nPractical Considerations for Managing Knowledge\nKey Points\n57\nDiscussion Points\nReferences\n3\n57\n58\nKnowledge Management Models\nLearning Objectives\nIntroduction\n57\n59\n59\n59\nMajor Theoretical KM Models\n62\nThe Von Krogh and Roos Model of Organizational Epistemology 62\nThe Nonaka and Takeuchi Knowledge Spiral Model\n64\nThe Choo Sense-Making KM Model 73\nThe Wiig Model for Building and Using Knowledge\n76\nThe Boisot I-Space KM Model 82\nComplex Adaptive System Models of KM 85\nThe European Foundation for Quality Management (EFQM) KM Model\nThe inukshuk KM Model\n90\nStrategic Implications of KM Models\n92\nPractical Implications of KM Models\n92\nKey Points\n93\nDiscussion Points\nReferences\n4\n93\n95\nKnowledge Capture and Codification\nLearning Objectives\nIntroduction\n89\n97\n97\n98\nTacit Knowledge Capture\n101\nTacit Knowledge Capture at the Individual and Group Levels\nTacit Knowledge Capture at the Organizational Level\n118\n102\nExplicit Knowledge Codification 121\nCognitive Maps 121\nDecision Trees\n123\nKnowledge Taxonomies 124\nThe Relationships among Knowledge Management, Competitive Intelligence, Business Intelligence,\nand Strategic Intelligence\n131\nStrategic Implications of Knowledge Capture and Codification\n133\nPractical Implications of Knowledge Capture and Codification\n134\nKey Points\n135\nDiscussion Points\nReferences\n136\n135\nContents\n5\nvii\nKnowledge Sharing and Communities of Practice\nLearning Objectives\nIntroduction\n141\n141\n142\nThe Social Nature of Knowledge\n147\nSociograms and Social Network Analysis\nCommunity Yellow Pages 152\n149\nKnowledge-Sharing Communities 154\nTypes of Communities 158\nRoles and Responsibilities in CoPs\n160\nKnowledge Sharing in Virtual CoPs 163\nObstacles to Knowledge Sharing\nThe Undernet 169\n168\nOrganizational Learning and Social Capital\n170\nMeasuring the Value of Social Capital\n171\nStrategic Implications of Knowledge Sharing\n173\nPractical Implications of Knowledge Sharing\n175\nKey Points\n175\nDiscussion Points\nReferences\n6\n176\n177\nKnowledge Application\nLearning Objectives\nIntroduction\n183\n183\n184\nKnowledge Application at the Individual Level 187\nCharacteristics of Individual Knowledge Workers\n187\nBloom’s Taxonomy of Learning Objectives\n191\nTask Analysis and Modeling 200\nKnowledge Application at the Group and Organizational Levels\nKnowledge Reuse\n211\nKnowledge Repositories 213\nE-Learning and Knowledge Management Application\n214\nStrategic Implications of Knowledge Application\n216\nPractical Implications of Knowledge Application\n217\nKey Points\n218\nDiscussion Points\nNote\n219\nReferences\n219\n218\n207\nviii\n7\nContents\nThe Role of Organizational Culture\nLearning Objectives\nIntroduction\n223\n223\n224\nDifferent Types of Cultures\n227\nOrganizational Culture Analysis\n229\nCulture at the Foundation of KM 232\nThe Effects of Culture on Individuals 235\nOrganizational Maturity Models\nKM Maturity Models\n239\nCoP Maturity Models\n244\n238\nTransformation to a Knowledge-Sharing Culture\nImpact of a Merger on Culture 256\nImpact of Virtualization on Culture 258\n246\nStrategic Implications of Organizational Culture\n258\nPractical Implications of Organizational Culture\n259\nKey Points\n262\nDiscussion Points\nReferences\n8\n262\n263\nKnowledge Management Tools\nLearning Objectives\nIntroduction\n267\n267\n268\nKnowledge Capture and Creation Tools 270\nContent Creation Tools\n270\nData Mining and Knowledge Discovery\n271\nBlogs\n274\nMashups 275\nContent Management Tools 276\nFolksonomies and Social Tagging/Bookmarking\n277\nPersonal Knowledge Management (PKM) 279\nKnowledge Sharing and Dissemination Tools\n280\nGroupware and Collaboration Tools 281\nWikis 285\nSocial Networking, Web 2.0, and KM 2.0 288\nNetworking Technologies\n292\nKnowledge Acquisition and Application Tools\nIntelligent Filtering Tools 298\nAdaptive Technologies\n302\n297\nStrategic Implications of KM Tools and Techniques\n303\nPractical Implications of KM Tools and Techniques\n304\nContents\nKey Points\nix\n304\nDiscussion Points\nReferences\n9\n305\n306\nKnowledge Management Strategy\nLearning Objectives\nIntroduction\n311\n311\n311\nDeveloping a Knowledge Management Strategy\nKnowledge Audit\n318\nGap Analysis\n322\nThe KM Strategy Road Map\n325\n316\nBalancing Innovation and Organizational Structure\nTypes of Knowledge Assets Produced\nKey Points\n336\nDiscussion Points\nReferences\n10\n337\n338\nThe Value of Knowledge Management\nLearning Objectives\nIntroduction\n339\n362\n362\nAdditional Resources\n11\n359\n360\nDiscussion Points\nReferences\n339\n339\nKM Return on Investment (ROI) and Metrics 343\nThe Benchmarking Method\n345\nThe Balanced Scorecard Method\n351\nThe House of Quality Method 354\nThe Results-Based Assessment Framework\n356\nMeasuring the Success of Communities of Practice\nKey Points\n328\n333\n364\nOrganizational Learning and Organizational Memory\nLearning Objectives\nIntroduction\n365\n365\n365\nHow Do Organizations Learn and Remember?\n368\nFrameworks to Assess Organizational Learning and Organizational Memory\nThe Management of Organizational Memory\n370\nOrganizational Learning\n377\nThe Lessons Learned Process 378\nOrganizational Learning and Organizational Memory Models\n379\n369\nx\nContents\nA Three-Tiered Approach to Knowledge Continuity\nKey Points\n390\nDiscussion Points\nReferences\n12\n391\n392\nThe KM Team\nLearning Objectives\nIntroduction\n385\n397\n397\n398\nMajor Categories of KM Roles\n402\nSenior Management Roles 403\nKM Roles and Responsibilities within Organizations\n410\nThe KM Profession 412\nThe Ethics of KM 413\nKey Points\n419\nDiscussion Points\nNote\nReferences\n13\n420\n421\n421\nFuture Challenges for KM\nLearning Objectives\nIntroduction\n423\n423\n424\nPolitical Issues Regarding Internet Search Engines\n425\nThe Politics of Organizational Context and Culture\n427\nShift to Knowledge-Based Assets\n429\nIntellectual Property Issues 433\nHow to Provide Incentives for Knowledge Sharing\nFuture Challenges for KM\nKM Research\nA Postmodern KM\n446\nConcluding Thought\nKey Points\n14\n447\n448\nDiscussion Points\nReferences\n449\n450\nKM Resources\n453\nThe Classics 453\nKM for Specific Disciplines\nInternational KM\n455\nKM Journals\n440\n442\n455\nKey Conferences\n456\n454\n435\nContents\nxi\nKey Web Sites\n457\nKM Glossaries\n457\nKM Case Studies and Examples\nKM Case Studies 458\nKM Examples\n459\nKM Wikis\n459\nKM Blogs\n459\nVisual Resources 460\nYouTube\n460\nOther Visual Resources\n460\nSome Useful Tools 460\nOther Visual Mapping Tools\nNote\n460\nGlossary\n461\nIndex 477\n458\n460\nForeword: Can Knowledge Management Survive?\nThe title of this foreword, “Can Knowledge Management Survive?” is perhaps rather\nstrange for this second edition of this leading textbook on knowledge management\n(KM). However, as the KM field has taught us to be “reflective practitioners,” this\nquestion is worth pondering.\nKnowledge management has been around for twenty years or more, in terms of its\ngrowth as a discipline. Even though the roots of knowledge management go back far\nbeyond that, is knowledge management generally accepted within organizations, and\nis KM a lasting field or discipline?\nTo answer the first question, we can review some anecdotal evidence that suggests\nKM is more widely accepted within certain industries than others. Over the years,\nthe pharmaceutical, energy, aerospace, manufacturing, and legal industries have\nperhaps been some of the leaders in KM organizational adoption. In looking toward\nthe future, the public health and health care fields are certainly well positioned to\nleverage knowledge throughout the world. And as the graying workforce ensues and\nthe baby boomers retire, knowledge retention will continue to play a key role in\nmany sectors, such as in government, nuclear energy, education, and others. So, KM\nhas permeated many organizations and has the propensity to propagate to others.\nHowever, there are still many organizations that equate KM to be IT (information\ntechnology), and do not fully grasp the concept of building and nurturing a knowledge sharing culture for promoting innovation. Many organizations do not have KM\nseamlessly woven within their fabric, and many organizations do not recognize or\nreward their employees for knowledge sharing activities. It is getting harder to find\nthe title of a “chief knowledge officer” or a “knowledge management director” in\norganizations, suggesting two possibilities. The first is that KM is indeed embedded\nwithin the organization’s culture so there is no need to single it out. The second\nproposition is that KM has lost its appeal and importance, so there is no need to\nhave a CKO or equivalent position, especially in these difficult economic times.\nxiv\nForeword\nProbably, both propositions are true, depending perhaps on the type and nature of\nthe organization.\nSo, returning to the first question about KM being widely accepted within today’s\norganizations, the jury is still out. It may be simply an awareness issue in order to\nshow the value-added benefits of KM initiatives. Or it may be that KM was the “management fad of the day” and we are ready to move on. I believe that KM can have\ntremendous value to organizations by stimulating creativity and innovation, building\nthe institutional memory of the firm, enabling agility and adaptability, promoting a\nsense of community and belonging, improving organizational internal and external\neffectiveness, and contributing toward succession planning and workforce development. KM should be one of the key pillars underpinning a human capital strategy for\nthe organization. As with anything else, some organizations are leaders and some are\nlaggards. Those who recognize the importance of KM to the organization’s overarching\nvision, mission, and strategy should hopefully be in the winning side of the equation\nin the years ahead.\nLet us now address the second question posed, “is KM a lasting field?” In other\nwords, does KM have endurance to stand on its own in the forthcoming years? This\nrelates back to whether KM is more an art than a science. KM is certainly both, and\nas the KM field has developed over the years, an active KM community of both practitioners and researchers has emerged. There are already well over ten international\njournals specifically devoted to knowledge management. Worldwide KM conferences\nabound, and individuals can take university coursework in knowledge management,\nas well as being certified in knowledge management by KM-related professional societies and other organizations. There are funded research projects in knowledge management worldwide, both from basic and applied perspectives. In addition, there are\nmany KM-related communities of practice established worldwide. So certainly there\nis an active group of practitioners and researchers who are trying to put more rigor\nbehind KM to accentuate the “science” over the “art” in order to give the KM field\nlasting legs.\nOn the other hand, there is the “art” side of KM. Like many fields that draw from\na multidisciplinary approach, especially from the social sciences, there is art along\nwith the science. Whether KM contributes to “return on vision” versus “return on\ninvestment” indicates some of the difficulty in quantifying KM returns. There certainly\nis a “touchy-feely” side to KM, but there is a sound methodological perspective to KM,\ntoo.\nHere again, the jury is still out on whether the KM field will last. So what needs to\nbe done? This is where textbooks such as Knowledge Management in Theory and Practice\nCan Knowledge Management Survive?\nxv\nplay an important role. This textbook, in its second edition, marries the theory and\npractice of knowledge management; namely, it provides the underlying methodologies for knowledge management design, development, and implementation, as well\nas applying these methodologies and techniques in various cases and vignettes sprinkled throughout the book. It addresses my first question of having knowledge management being more widely accepted in organizations by discussing how KM has been\nutilized in various industry sectors and organizational settings. The book also emphasizes the “science” behind the “art” in order to address my second question regarding\nproviding more rigor behind KM so that the field will endure in the years ahead.\nProfessor Dalkir, a leading KM researcher, educator, and practitioner, uses her\ninsights and experience to highlight the important areas of knowledge management\nin her book. People, culture, process, and technology are key components of knowledge management, and the book provides valuable lessons learned in each area. This\nbook is well-suited as a reference text for KM practitioners, as well as a textbook for\nKM-related courses.\nThis book, and others, is needed to continue to take the mystique out of KM and\nprovide the tangible value-added benefits that CEOs and organizations demand. Professor Dalkir should be commended on this new edition, which will hopefully propel\nothers to be believers in the power of knowledge management. As this happens, the\nanswers to my two KM questions will be quite obvious! Enjoy!\nJay Liebowitz, D.Sc.\nProfessor, Carey Business School\nJohns Hopkins University\n1 Introduction to Knowledge Management\nA light bulb in the socket is worth two in the pocket.\n—Bill Wolf (1950–2001)\nThis chapter provides an introduction to the study of knowledge management (KM).\nA brief history of knowledge management concepts is outlined, noting that much of\nKM existed before the actual term came into popular use. The lack of consensus over\nwhat constitutes a good definition of KM is addressed and the concept analysis technique is described as a means of clarifying the conceptual confusion that still persists\nover what KM is or is not. The multidisciplinary roots of KM are enumerated together\nwith their contributions to the discipline. The two major forms of knowledge, tacit\nand explicit, are compared and contrasted. The importance of KM today for individuals, for communities of practice, and for organizations are described together\nwith the emerging KM roles and responsibilities needed to ensure successful KM\nimplementations.\nLearning Objectives\n1. Use a framework and a clear language for knowledge management concepts.\n2. Define key knowledge management concepts such as intellectual capital, organizational learning and memory, knowledge taxonomy, and communities of practice\nusing concept analysis.\n3. Provide an overview of the history of knowledge management and identify key\nmilestones.\n4. Describe the key roles and responsibilities required for knowledge management\napplications.\n2\nChapter 1\nIntroduction\nThe ability to manage knowledge is crucial in today’s knowledge economy. The creation and diffusion of knowledge have become increasingly important factors in\ncompetitiveness. More and more, knowledge is being thought of as a valuable commodity that is embedded in products (especially high-technology products) and\nembedded in the tacit knowledge of highly mobile employees. While knowledge is\nincreasingly being viewed as a commodity or intellectual asset, there are some paradoxical characteristics of knowledge that are radically different from other valuable\ncommodities. These knowledge characteristics include the following:\n•\nUsing knowledge does not consume it.\n•\nTransferring knowledge does not result in losing it.\n•\nKnowledge is abundant, but the ability to use it is scarce.\n•\nMuch of an organization’s valuable knowledge walks out the door at the end of the\nday.\nThe advent of the Internet, the World Wide Web, has made unlimited sources of\nknowledge available to us all. Pundits are heralding the dawn of the Knowledge Age\nsupplanting the Industrial Era. Forty-five years ago, nearly half of all workers in\nindustrialized countries were making or helping to make things. By the year 2000,\nonly 20 percent of workers were devoted to industrial work—the rest was knowledge\nwork (Drucker 1994; Barth 2000). Davenport (2005, p. 5) says about knowledge\nworkers that “at a minimum, they comprise a quarter of the U.S. workforce, and at\na maximum about half.” Labor-intensive manufacturing with a large pool of relatively\ncheap, relatively homogenous labor and hierarchical management has given way to\nknowledge-based organizations. There are fewer people who need to do more work.\nOrganizational hierarchies are being put aside as knowledge work calls for more collaboration. A firm only gains sustainable advances from what it collectively knows,\nhow efficiently it uses what it knows, and how quickly it acquires and uses new\nknowledge (Davenport and Prusak 1998). An organization in the Knowledge Age is\none that learns, remembers, and acts based on the best available information, knowledge, and know-how.\nAll of these developments have created a strong need for a deliberate and systematic\napproach to cultivating and sharing a company’s knowledge base—one populated\nwith valid and valuable lessons learned and best practices. In other words, in order to\nbe successful in today’s challenging organizational environment, companies need to\nlearn from their past errors and not reinvent the wheel. Organizational knowledge is\nIntroduction to Knowledge Management\n3\nnot intended to replace individual knowledge but to complement it by making it\nstronger, more coherent, and more broadly applied. Knowledge management represents a deliberate and systematic approach to ensure the full utilization of the\norganization’s knowledge base, coupled with the potential of individual skills, competencies, thoughts, innovations, and ideas to create a more efficient and effective\norganization.\nIncreasingly, companies will differentiate themselves on the basis of what they know. A relevant\nvariation on Sidney Winter’s definition of a business firm as an organization that knows how to do\nthings would define a business firm that thrives over the next decade as an organization that knows\nhow to do new things well and quickly. (Davenport and Prusak 1998, 13)\nKnowledge management was initially defined as the process of applying a systematic approach to the capture, structuring, management, and dissemination of knowledge throughout an organization to work faster, reuse best practices, and reduce costly\nrework from project to project (Nonaka and Takeuchi, 1995; Pasternack and Viscio\n1998; Pfeffer and Sutton, 1999; Ruggles and Holtshouse, 1999). KM is often characterized by a pack rat approach to content: “save it, it may prove useful some time in the\nfuture.” Many documents tend to be warehoused, sophisticated search engines are\nthen used to try to retrieve some of this content, and fairly large-scale and costly KM\nsystems are built. Knowledge management solutions have proven to be most successful\nin the capture, storage, and subsequent dissemination of knowledge that has been\nrendered explicit—particularly lessons learned and best practices.\nThe focus of intellectual capital management (ICM), on the other hand, is on those\npieces of knowledge that are of business value to the organization—referred to as intellectual capital or assets. Stewart (1997) defines intellectual capital as “organized knowledge that can be used to produce wealth.” While some of these assets are more visible\n(e.g., patents, intellectual property), the majority consists of know-how, know-why,\nexperience, and expertise that tends to reside within the head of one or a few employees (Klein 1998; Stewart 1997). ICM is characterized less by content—because content\nis filtered and judged, and only the best ideas re inventoried (the top ten for example).\nICM content tends to be more representative of the real thinking of individuals (contextual information, opinions, stories) because of its focus on actionable knowledge\nand know-how. The outcome is less costly endeavors and a focus on learning (at the\nindividual, community, and organizational levels) rather than on the building of\nsystems.\nA good definition of knowledge management would incorporate both the capturing\nand storing of knowledge perspective, together with the valuing of intellectual assets.\nFor example:\n4\nChapter 1\nKnowledge management is the deliberate and systematic coordination of an organization’s\npeople, technology, processes, and organizational structure in order to add value through reuse\nand innovation. This is achieved through the promotion of creating, sharing, and applying\nknowledge as well as through the feeding of valuable lessons learned and best practices into\ncorporate memory in order to foster continued organizational learning.\nWhen asked, most executives will state that their greatest asset is the knowledge\nheld by their employees. “When employees walk out the door, they take valuable\norganizational knowledge with them” (Lesser and Prusak 2001, 1). Managers also\ninvariably add that they have no idea how to manage this knowledge! Using the intellectual capital or asset approach, it is essential to identify knowledge that is of value\nand is also at risk of being lost to the organization through retirement, turnover, and\ncompetition.. As Lesser and Prusak (2001, 1) note: “The most knowledgeable employees often leave first.” In addition, the selective or value-based knowledge management\napproach should be a three-tiered one, that is, it should also be applied to three organizational levels: the individual, the group or community, and the organization itself.\nThe best way to retain valuable knowledge is to identify intellectual assets and then\nensure legacy materials are produced and subsequently stored in such a way as to make\ntheir future retrieval and reuse as easy as possible (Stewart 2000). These tangible byproducts need to flow from individual to individual, between members of a community of practice and, of course, back to the organization itself, in the form of lessons\nlearned, best practices, and corporate memory.\nMany knowledge management efforts have been largely concerned with capturing,\ncodifying, and sharing the knowledge held by people in organizations. Although there\nis still a lack of consensus over what constitutes a good definition of KM (see next\nsection), there is widespread agreement as to the goals of an organization that undertakes KM. Nickols (2000) summarizes this as follows: “the basic aim of knowledge\nmanagement is to leverage knowledge to the organization’s advantage.” Some of\nmanagement’s motives are obvious: the loss of skilled people through turnover, pressure to avoid reinventing the wheel, pressure for organization-wide innovations in\nprocesses as well as products, managing risk, and the accelerating rate with which new\nknowledge is being created. Some typical knowledge management objectives would\nbe to:\n•\nFacilitate a smooth transition from those retiring to their successors who are recruited\nto fill their positions\n•\nMinimize loss of corporate memory due to attrition and retirement\n•\nIdentify critical resources and critical areas of knowledge so that the corporation\nknows what it knows and does well—and why\nIntroduction to Knowledge Management\n•\n5\nBuild up a toolkit of methods that can be used with individuals, with groups, and\nwith the organization to stem the potential loss of intellectual capital\nWhat Is Knowledge Management?\nAn informal survey conducted by the author identified over a hundred published\ndefinitions of knowledge management and of these, at least seventy-two could be\nconsidered to be very good! Carla O’Dell has gathered over sixty definitions and has\ndeveloped a preliminary classification scheme for the definitions on her KM blog (see\nhttp://blog.simslearningconnections.com/?p=279) and what this indicates is that KM\nis a multidisciplinary field of study that covers a lot of ground. This should not be\nsurprising as applying knowledge to work is integral to most business activities.\nHowever, the field of KM does suffer from the “Three Blind Men and an Elephant”\nsyndrome. In fact, there are likely more than three distinct perspectives on KM, and\neach leads to a different extrapolation and a different definition.\nHere are a few sample definitions of knowledge management from the business\nperspective:\nStrategies and processes designed to identify, capture, structure, value, leverage, and share an\norganization’s intellectual assets to enhance its performance and competitiveness. It is based on\ntwo critical activities: (1) capture and documentation of individual explicit and tacit knowledge,\nand (2) its dissemination within the organization. (The Business Dictionary, http://www.businessdictionary.com/definition/knowledge-management.html)\nKnowledge management is a collaborative and integrated approach to the creation, capture,\norganization, access, and use of an enterprise’s intellectual assets. (Grey 1996)\nKnowledge management is the process by which we manage human centered assets . . . the\nfunction of knowledge management is to guard and grow knowledge owned by individuals, and\nwhere possible, transfer the asset into a form where it can be more readily shared by other\nemployees in the company. (Brooking 1999, 154)\nFurther definitions come from the intellectual or knowledge asset perspective:\nKnowledge management consists of “leveraging intellectual assets to enhance organizational\nperformance.” (Stankosky 2008)\nKnowledge management develops systems and processes to acquire and share intellectual assets.\nIt increases the generation of useful, actionable, and meaningful information, and seeks to\nincrease both individual and team learning. In addition, it can maximize the value of an organization’s intellectual base across diverse functions and disparate locations. Knowledge management maintains that successful businesses are a collection not of products but of distinctive\nknowledge bases. This intellectual capital is the key that will give the company a competitive\n6\nChapter 1\nadvantage with its targeted customers. Knowledge management seeks to accumulate intellectual\ncapital that will create unique core competencies and lead to superior results. (Rigby 2009)\nA definition from the cognitive science or knowledge science perspective:\nKnowledge—the insights, understandings, and practical know-how that we all possess—is the\nfundamental resource that allows us to function intelligently. Over time, considerable knowledge\nis also transformed to other manifestations—such as books, technology, practices, and traditions—within organizations of all kinds and in society in general. These transformations result\nin cumulated [sic] expertise and, when used appropriately, increased effectiveness. Knowledge is\none, if not THE, principal factor that makes personal, organizational, and societal intelligent\nbehavior possible. (Wiig 1993)\nTwo diametrically opposed schools of thought arise from the library and information science perspective: the first sees very little distinction between information\nmanagement and knowledge management, as shown by these two definitions:\nKM is predominantly seen as information management by another name (semantic drift).\n(Davenport and Cronin 2000, 1)\nKnowledge management is one of those concepts that librarians take time to assimilate, only to\nreflect ultimately “on why other communities try to colonize our domains.” (Hobohm 2004, 7)\nThe second school of thought, however, does make a distinction between the management of information resources and the management of knowledge resources.\nKnowledge management “is understanding the organization’s information flows and implementing organizational learning practices which make explicit key aspects of its knowledge base. . . .\nIt is about enhancing the use of organizational knowledge through sound practices of information management and organizational learning.” (Broadbent 1997, 8–9)\nThe process-technology perspective provides some sample definitions, as well:\nKnowledge management is the concept under which information is turned into actionable\nknowledge and made available effortlessly in a usable form to the people who can apply it. (Patel\nand Harty, 1998)\nLeveraging collective wisdom to increase responsiveness and innovation. (Carl Frappaolo, Delphi\nGroup, Boston, http://www.destinationkm.com/articles/default.asp?ArticleID=949)\nA systematic approach to manage the use of information in order to provide a continuous flow\nof knowledge to the right people at the right time enabling efficient and effective decision making\nin their everyday business. (Steve Ward, Northrop Grumman, http://www.destinationkm.com/\narticles/default.asp?ArticleID=949)\nA knowledge management system is a virtual repository for relevant information that is\ncritical to tasks performed daily by organizational knowledge workers. (What is KM? http://www\n.knowledgeshop.com)\nIntroduction to Knowledge Management\n7\nThe tools, techniques, and strategies to retain, analyze, organize, improve, and share business\nexpertise. (Groff and Jones 2003, 2)\nA capability to create, enhance, and share intellectual capital across the organization . . . a shorthand covering all the things that must be put into place, for example, processes, systems, culture,\nand roles to build and enhance this capability. (Lank 1997)\nThe creation and subsequent management of an environment that encourages knowledge to be\ncreated, shared, learnt [sic], enhanced, organized and utilized for the benefit of the organization\nand its customers. (Abell and Oxbrow 2001)\nWiig (1993, 2002) also emphasizes that, given the importance of knowledge in\nvirtually all areas of daily and commercial life, two knowledge-related aspects are vital\nfor viability and success at any level. These are knowledge assets that must be applied,\nnurtured, preserved, and used to the largest extent possible by both individuals and\norganizations; and knowledge-related processes to create, build, compile, organize,\ntransform, transfer, pool, apply, and safeguard knowledge. These knowledge-related\naspects must be carefully and explicitly managed in all affected areas.\nHistorically, knowledge has always been managed, at least implicitly. However, effective and\nactive knowledge management requires new perspectives and techniques and touches on almost\nall facets of an organization. We need to develop a new discipline and prepare a cadre of knowledge professionals with a blend of expertise that we have not previously seen. This is our challenge! (Wiig, in Grey 1996)\nKnowledge management is a surprising mix of strategies, tools, and techniques—\nsome of which are nothing new under the sun: storytelling, peer-to-peer mentoring,\nand learning from mistakes, for example, all have precedents in education, training,\nand artificial intelligence practices. Knowledge management makes use of a mixture\nof techniques from knowledge-based system design, such as structured knowledge\nacquisition strategies from subject matter experts (McGraw and Harrison-Briggs 1989)\nand educational technology (e.g., task and job analysis to design and develop task\nsupport systems; Gery 1991).\nThis makes it both easy and difficult to define what KM is. At one extreme, KM\nencompasses everything to do with knowledge. At the other extreme, KM is narrowly\ndefined as an information technology system that dispenses organizational knowhow. KM is in fact both of these and much more. One of the few areas of consensus\nin the field is that KM is a highly multidisciplinary field.\n8\nChapter 1\nMultidisciplinary Nature of KM\nKnowledge management draws upon a vast number of diverse fields such as:\n•\nOrganizational science\n•\nCognitive science\n•\nLinguistics and computational linguistics\n•\nInformation technologies such as knowledge-based systems, document and informa-\ntion management, electronic performance support systems, and database technologies\n•\nInformation and library science\n•\nTechnical writing and journalism\n•\nAnthropology and sociology\n•\nEducation and training\n•\nStorytelling and communication studies\n•\nCollaborative technologies such as Computer-Supported Collaborative Work (CSCW)\nand groupware as well as intranets, extranets, portals, and other web technologies\nThe above is by no means an exhaustive list but serves to show the extremely varied\nroots that KM grew out of and continues to be based upon today. Figure 1.1 illustrates\nsome of the diverse disciplines that have contributed to KM.\nThe multidisciplinary nature of KM represents a double-edged sword: on the one\nhand, it is an advantage as almost anyone can find a familiar foundation upon which\nto base an understanding and even practice of KM. Someone with a background in\nDatabase Technologies\nCollaborative Technologies\nHelp Desk Systems\nOrganizational Science\nCognitive Science\nKM Disciplines\nTechnical Writing\nArtificial Intelligence\nElectronic Performance\nSupport Systems\nDocument and\nInformation Management\nWeb Technologies\nDecision Support Systems\nLibrary and Information Sciences\nFigure 1.1\nInterdisciplinary nature of knowledge management\nIntroduction to Knowledge Management\n9\njournalism, for example, can quickly adapt this skill set to capture knowledge from\nexperts and reformulate this knowledge as organizational stories to be stored in corporate memory. Someone coming from a more technical database background can\neasily extrapolate his or her skill set to design and implement knowledge repositories\nthat will serve as the corporate memory for that organization. However, the diversity\nof KM also results in some challenges with respect to boundaries. Skeptics argue that\nKM is not and cannot be said to be a separate discipline with a unique body of knowledge to draw upon. This attitude is typically represented by statements such as “KM\nis just IM” or “KM is nonsensical—it is just good business practices.” It becomes very\nimportant to be able to list and describe what attributes are necessary and in themselves sufficient to constitute knowledge management both as a discipline and as a\nfield of practice that can be distinguished from others.\nOne of the major attributes lies in the fact that KM deals with knowledge as well\nas information. Knowledge is a more subjective way of knowing, typically based on\nexperiential or individual values, perceptions, and experience. Consider the example\nof planning for an evening movie to distinguish between data, information, and\nknowledge.\nData\nContent that is directly observable or verifiable: a fact; for example, movie list-\nings giving the times and locations of all movies being shown today—I download the\nlistings.\nInformation Content that represents analyzed data; for example, I can’t leave before\n5, so I will go to the 7 pm show at the cinema near my office.\nKnowledge At that time of day, it will be impossible to find parking. I remember the\nlast time I took the car, I was so frustrated and stressed because I thought I would miss\nthe opening credits. I’ll therefore take the commuter train. But first, I’ll check with\nAl. I usually love all the movies he hates, so I want to make sure it’s worth seeing!\nAnother distinguishing characteristic of KM, as opposed to other information\nmanagement fields, is the fact that knowledge in all of its forms is addressed: tacit\nknowledge and explicit knowledge.\nThe Two Major Types of Knowledge: Tacit and Explicit\nWe know more than we can tell.\n—Polanyi 1966\nTacit knowledge is difficult to articulate and difficult to put into words, text, or\ndrawings. Explicit knowledge represents content that has been captured in some\n10\nChapter 1\nTable 1.1\nComparison of properties of tacit versus explicit knowledge\nProperties of tacit knowledge\nProperties of explicit knowledge\nAbility to adapt, to deal with new and\nexceptional situations\nAbility to disseminate, to reproduce, to access\nand re-apply throughout the organization\nExpertise, know-how, know-why, and\ncare-why\nAbility to teach, to train\nAbility to collaborate, to share a vision, to\ntransmit a culture\nAbility to organize, to systematize, to\ntranslate a vision into a mission statement,\ninto operational guidelines\nCoaching and mentoring to transfer\nexperiential knowledge on a one-to-one,\nface-to-face basis\nTransfer knowledge via products, services,\nand documented processes\ntangible form such as words, audio recordings, or images. Tacit knowledge tends to\nreside within the heads of knowers, whereas explicit knowledge is usually contained\nwithin tangible or concrete media. However, it should be noted that this is a rather\nsimplistic dichotomy. In fact, the property of tacitness is a property of the knower:\nthat which is easily articulated by one person may be very difficult to externalize by\nanother. The same content may be explicit for one person and tacit for another.\nThere is also somewhat of a paradox at play here: highly skilled, experienced, and\nexpert individuals may find it harder to articulate their know-how. Novices, on the\nother hand, are more apt to easily verbalize what they are attempting to do because\nthey are typically following a manual or how-to process. Table 1.1 summarizes some\nof the major properties of tacit and explicit knowledge.\nTypically, the more tacit knowledge is, the more valuable it tends to be. The\nparadox lies in the fact that the more difficult it is to articulate a concept such as story,\nthe more valuable that knowledge may be. This is often witnessed when people make\nreference to knowledge versus know-how, or knowing something versus knowing how\nto do something. Valuable tacit knowledge often results in some observable action\nwhen individuals understand and subsequently make use of knowledge. Another\nperspective is that explicit knowledge tends to represent the final end product whereas\ntacit knowledge is the know-how or all of the processes that were required in order\nto produce that final product.\nWe have a habit of writing articles published in scientific journals to make the work as finished\nas possible, to cover up all the tracks, to not worry about the blind alleys or how you had the\nwrong idea at first, and so on. So there isn’t any place to publish, in a dignified manner, what\nyou actually did in order to do the work. (Feynman 1966).\nIntroduction to Knowledge Management\n11\nA popular misconception is that KM focuses on rendering that which is tacit into\nmore explicit or tangible forms, then storing or archiving these forms somewhere,\nusually some form of intranet or knowledge portal. The “build it and they will come”\nexpectation typifies this approach: Organizations take an exhaustive inventory of\ntangible knowledge (i.e., documents, digital records) and make them accessible to all\nemployees. Senior management is then mystified as to why employees are not using\nthis wonderful new resource. In fact, knowledge management is broader and includes\nleveraging the value of the organizational knowledge and know-how that accumulates\nover time. This approach is a much more holistic and user-centered approach that\nbegins not with an audit of existing documents but with a needs analysis to better\nunderstand how improved knowledge sharing may benefit specific individuals, groups,\nand the organization as a whole. Successful knowledge-sharing examples are gathered\nand documented in the form of lessons learned and best practices and these then form\nthe kernel of organizational stories.\nThere are a number of other attributes that together make up a set of what KM\nshould be all about. One good technique for identifying these attributes is the concept\nanalysis technique.\nThe Concept Analysis Technique\nConcept analysis is an established technique used in the social sciences (i.e., philosophy and education) in order to derive a formula that in turn can be used to generate\ndefinitions and descriptive phrases for highly complex terms. We still lack a consensus\non knowledge management–related terms, and these concepts do appear to be complex\nenough to merit the concept analysis approach. A great deal of conceptual complexity\nderives from the fact that a word such as knowledge is necessarily subjective in nature,\nnot to mention value laden in interpretation.\nThe concept analysis approach rests on the obtaining consensus around three major\ndimensions of a given concept (shown in figure 1.2).\n1. A list of key attributes that must be present in the definition, vision, or mission\nstatement\n2. A list of illustrative examples\n3. A list of illustrative nonexamples\nThis approach is particularly useful in tackling multidisciplinary domains such\nas intellectual capital, because clear criteria can be developed to enable sorting\ninto categories such as knowledge versus information, document management versus\nknowledge management, and tangible versus intangible assets. In addition, valuable\n12\nChapter 1\nConcept Name\nKey Attributes\nExamples\nNonexamples\n1.\n1.\n1.\n2.\n2.\n2.\n3.\n3.\n3.\n4.\n4.\n4.\n5.\n5.\n5.\n6.\n6.\n6.\n7.\n7.\n7.\nFigure 1.2\nIllustration of the Concept Analysis Technique\ncontributions to the organization’s intellectual capital are derived through the production of ontologies (semantic maps of key concepts), identification of core competencies, and identification of knowledge, know-how, and know-why at risk of being lost\nthrough human capital attrition.\nConcept analysis is a technique used to visually map out conceptual information\nin the process of defining a word (Novak 1990, 1991). This is a technique derived from\nthe fields of philosophy and science education (Bareholz and Tamir 1992; Lawson\n1994) and is typically used in clearly defining complex, value-laden terms such as\ndemocracy or religion. It is a graphical approach to help develop a rich, in-depth understanding of a concept. Figure 1.2 outlines the major components of this approach.\nDavenport and Prusak (1998) decry the ability to provide a definitive account of\nknowledge management since “epistemologists have spent their lives trying to understand what it means to know something.” In his 2008 keynote address, Michael\nStankosky reiterated this disappointment that we still “don’t know what to call it!” If\nIntroduction to Knowledge Management\n13\nyou can’t manage what you cannot measure, then you can’t measure what you cannot\nname. Knowledge management, due to this still ongoing lack of clarity and lack of\nconsensus on a definition, presents itself as a good candidate for this approach. In\nvisioning workshops, this is the first activity that participants are asked to undertake.\nThe objective is to agree upon a list of key attributes that are both necessary and sufficient in order for a definition of knowledge management to be acceptable. This is\ncompleted by a list of examples and nonexamples, with justifications as to why a\nparticular item was included on the example or nonexample list. Semantic mapping\n(Jonassen, Beissner, and Yacci 1993; Fisher 1990) is the visual technique used to extend\nthe definition by displaying words related to it. Popular terms to distinguish clearly\nfrom knowledge management include document management, content management,\nportal, knowledge repository, and others. Together, the concept and semantic maps\nvisually depict a model-based definition of knowledge management and its closely\nrelated terms.\nIn some cases, participants are provided with lists of definitions of knowledge\nmanagement from a variety of sources can so they can try out their concept map of\nknowledge management by analyzing these existing definitions. Definitions are typically drawn from the knowledge management literature as well as internally, from\ntheir own organization. The use of concept definition through concept and semantic\nmapping techniques can help participants rapidly reach a consensus on a formulaic\ndefinition of knowledge management, that is, one that focuses less on the actual text\nor words used but more on which key concepts need to be present, what comprises\na necessary and sufficient (complete) set of concepts, and rules of thumb to use in\ndiscerning what is and what is not an illustrative example of knowledge\nmanagement.\nRuggles and Holtshouse (1999) identified the following key attributes of knowledge\nmanagement:\n•\nGenerating new knowledge\n•\nAccessing valuable knowledge from outside sources\n•\nUsing accessible knowledge in decision making\n•\nEmbedding knowledge in processes, products and/or services\n•\nRepresenting knowledge in documents, databases, and software\n•\nFacilitating knowledge growth through culture and incentives\n•\nTransferring existing knowledge into other parts of the organization\n•\nMeasuring the value of knowledge assets and/or impact of knowledge management\n14\nChapter 1\nSome key knowledge management attributes that continue to recur include:\n•\nBoth tacit and explicit knowledge forms are addressed; tacit knowledge (Polanyi\n1966) is knowledge that often resides only within individuals, knowledge that is difficult to articulate such as expertise, know-how, tricks of the trade, and so on.\n•\nThere is a notion of added-value (the so what? of KM).\n•\nThe notion of application or use of the knowledge captured, codified, and dissemi-\nnated (the impact of KM).\nIt should be noted that a good enough or sufficient definition of knowledge has been\nshown to be effective (i.e., settling for good enough as opposed to optimizing; when 80\npercent is done because the incremental cost of completing the remaining 20 percent\nis disproportionately expensive and/or time-consuming in relation to the expected\nadditional benefits). Norman (1988, 50–74) noted that knowledge might reside in two\nplaces—in the minds of people and/or in the world. It is easy to show the faulty nature\nof human knowledge and memory. For example, when typists were given caps for\ntypewriter keys, they could not arrange them in the proper configuration—yet all\nthose typists could type rapidly and accurately. Why the apparent discrepancy between\nthe precision of behavior and the imprecision of knowledge? Because not all of the\nknowledge required for precise behavior has to be in the mind. It can be distributed—\npartly in the mind, partly in the world, and partly in the constraints of the world.\nPrecise behavior can thus emerge from imprecise knowledge (Ambur 1996). It is for\nthis reason that once a satisfactory working or operational definition of knowledge\nmanagement has been arrived at, then a knowledge management strategy can be\nconfidently tackled.\nIt is highly recommended that each organization undertake a concept analysis\nexercise to clarify their understanding of what KM means in their own context. The\nbest way to do this would be to work as a group in order to achieve a shared understanding at the same time that a clearer conceptualization of the KM concept is\ndeveloped. Each participant can take a turn to contribute one good example of what\nKM is and another example of what KM is not. The entire group can then discuss this\nexample/nonexample pair in order to identify one (or several) key KM attributes.\nMiller’s (1956) magic number can be used to define the optimal number of attributes\na given concept should have—namely, seven plus or minus two attributes. Once the\ngroup feels they have covered as much ground as they are likely to, the key attributes\ncan be summarized in the form of a KM concept formula such as:\nIn our organization, knowledge management must include the following: both tacit\nand explicit knowledge; a framework to measure the value of knowledge assets; a\nprocess for managing knowledge assets . . .\nIntroduction to Knowledge Management\n15\nThe lack of agreement on one universal formulation of a definition for knowledge\nmanagement makes it essential to develop one for each organization (at a very\nminimum). This working or operational definition, derived through the concept analysis\ntechnique, will render explicit the various perceptions people in that company may\nhave of KM and bring them together into a coherent framework. It may seem strange\nthat KM is almost always defined at the beginning of any talk or presentation on the\ntopic (imagine if other professionals such as doctors, lawyers, or engineers began every\ntalk with “here is a definition of what I do and why”) but this is the reality we must\ndeal with. Whether the lack of a definition is due to the interdisciplinary nature of\nthe field and/or because it is still an emerging discipline, it certainly appears to be\nhighly contextual. The concept analysis technique allows us to continue in both\nresearch and practice while armed with a common, validated, and clear description\nof KM that is useful and adapted to a particular organizational context.\nHistory of Knowledge Management\nAlthough the term knowledge management formally entered popular usage in the late\n1980s (e.g., conferences in KM began appearing, books on KM were published, and\nthe term began to be seen in business journals), philosophers, teachers, and writers\nhave been making use of many of the same techniques for decades. Denning (2002)\nrelated how from “time immemorial, the elder, the traditional healer, and the midwife\nin the village have been the living repositories of distilled experience in the life of the\ncommunity”(http://www.stevedenning.com/ knowledge_management.html).\nSome form of narrative repository has been around for a long time, and people\nhave found a variety of ways to share knowledge in order to build on earlier experience, eliminate costly redundancies, and avoid making at least the same mistakes\nagain. For example, knowledge sharing often took the form of town meetings, workshops, seminars, and mentoring sessions. The primary vehicle for knowledge transfer\nwas people themselves—in fact, much of our cultural legacy stems from the migration\nof different peoples across continents.\nWells (1938), while never using the actual term knowledge management, described\nhis vision of the World Brain that would allow the intellectual organization of the sum\ntotal of our collective knowledge. The World Brain would represent “a universal organization and clarification of knowledge and ideas” (Wells 1938, xvi). Wells in fact\nanticipated the World Wide Web, albeit in an idealized manner, when he spoke of\n“this wide gap between . . . at present unassembled and unexploited best thought and\nknowledge in the world . . . we live in a world of unused and misapplied knowledge\nand skill” (p. 10). The World Brain encapsulates many of the desirable features of the\n16\nChapter 1\nintellectual capital approach to KM: selected, well-organized, and widely vetted\ncontent that is maintained, kept up to date, and, above all, put to use to generate\nvalue to users, the users’ community, and their organization.\nWhat Wells envisioned for the entire world can easily be applied within an organization in the form of an intranet. What is new and termed knowledge management\nis that we are now able to simulate rich, interactive, face-to-face knowledge encounters virtually through the use of new communication technologies. Information technologies such as an intranet and the Internet enable us to knit together the intellectual\nassets of an organization and organize and manage this content through the lenses\nof common interest, common language, and conscious cooperation. We are able to\nextend the depth and breadth or reach of knowledge capture, sharing and dissemination activities, as we had not been able to do before and find ourselves one step\ncloser to Wells’ (1938) “perpetual digest . . . and a system of publication and distribution” (pp. 70–71) “to an intellectual unification . . . of human memory” (pp.\n86–87).\nDrucker was the first to coin the term knowledge worker in the early 1960s (Drucker\n1964). Senge (1990) focused on the learning organization as one that can learn from\npast experiences stored in corporate memory systems. Dorothy Barton-Leonard (1995)\ndocumented the case of Chapparal Steel as a knowledge management success story.\nNonaka and Takeuchi (1995) studied how knowledge is produced, used, and diffused\nwithin organizations and how this contributes to the diffusion of innovation.\nThe growing importance of organizational knowledge as a competitive asset was\nrecognized by a number of people who saw the value in being able to measure intellectual assets (see Kaplan and Norton; APQC 1996; Edvinsson and Malone 1997,\namong others). A cross-industry benchmarking study was led by APQC’s president\nCarla O’Dell and completed in 1996. It focused on the following KM needs:\n• Knowledge management as a business strategy\n• Transfer of knowledge and best practices\n• Customer-focused knowledge\n• Personal responsibility for knowledge\n• Intellectual asset management\n• Innovation and knowledge creation (APQC 1996)\nThe Entovation timeline (available at http://www.entovation.com/timeline/\ntimeline.htm) identifies a variety of disciplines and domains that have blended\ntogether to emerge as knowledge management. A number of management theorists\nhave contributed significantly to the evolution of KM such as Peter Drucker, Peter\nIntroduction to Knowledge Management\nKnowledge\nCreating\nCompany\nHBR Nonaka\nARPANET\n1969\n17\nEmergence\nof virtual\norganizations\nOrganizational\nLearning\nSloan Mgmt.\nMeasurement\nof intellectual\nassets\nCommunity\nof Practice\nBrown\n1988\n1991\n1994\n1985\nProliferation\nof information\ntechnology\nFifth\nDiscipline\nSenge\nKnowledge\nManagement\nFoundations\nWiig\nYour Company’s\nMost Valuable\nAsset:\nIntellectual\nCapital\nCertification\nStewart\nof knowledge\ninnovation\nstandards\n1997\nThe Balanced\nScorecard\nKaplan and Norton\nFirst CKO\nEdvinsson\nCorporation\n2000 +\nFirst KM\nprograms in\nuniversities\nAPQC\nbenchmarking\nFigure 1.3\nA summary timeline of knowledge management\nSenge, Ikujiro Nonaka, Hirotaka Takeuchi, and Thomas Stewart. An extract of this\ntimeline is shown in figure 1.3.\nThe various eras we have lived through offer another perspective on the history of\nKM. Starting with the industrial era in the 1800s, we focused on transportation technologies in 1850, communications in 1900, computerization beginning in the 1950s,\nand virtualization in the early 1980s, and early efforts at personalization and profiling\ntechnologies beginning in the year 2000 (Deloitte, Touche, Tohmatsu 1999). Figure\n1.4 summarizes these developmental phases.\nWith the advent of the information or computer age, KM has come to mean the\nsystematic, deliberate leveraging of knowledge assets. Technologies enable valuable\nknowledge to be remembered, via organizational learning and corporate memory; as\nwell as enabling valuable knowledge to be published, that is, widely disseminated to\nall stakeholders. The evolution of knowledge management has occurred in parallel\nwith a shift from a retail model based on a catalog (e.g., Ford’s famous quote that you\ncan have a car in any color you like—as long as it is black) to an auction model (as\nexemplified by eBay) to a personalization model where real-time matching of user\nneeds and services occur in a win-win exchange model.\nIn 1969, the launch of the ARPANET allowed scientists and researchers to communicate more easily with one another in addition to being able to exchange large\ndata sets they were working on. They came up with a network protocol or language\nthat would allow disparate computers and operating systems to network together\n18\nChapter 1\nPersonalization\n2000 ++\nVirtualization\n1980\nComputerization\nCommunications\nTransportation\nIndustrialization\n1950*\n1900\n1850\n1800\n* Birth of the Internet, 1969\nFigure 1.4\nDevelopmental phases in KM history\nacross communication lines. Next, a messaging system was added to this data file\ntransfer network. In 1991, the nodes were transferred to the Internet and World Wide\nWeb. At the end of 1969, only four computers and about a dozen workers were\nconnected.\nIn parallel, there were many key developments in information technologies devoted\nto knowledge-based systems: expert systems that aimed at capturing experts on a diskette, intelligent tutoring systems aimed at capturing teachers on a diskette and artificial\nintelligence approaches that gave rise to knowledge engineering, someone tasked with\nacquiring knowledge from subject matter experts, conceptually modeling this content,\nand then translating it into machine-executable code (McGraw and Harrison-Briggs\n1989). They describe knowledge engineering as “involving information gathering,\ndomain familiarization, analysisand design efforts. In addition, accumulated knowledge must be translated into code, tested and refined” (McGraw and Harrison Briggs,\n5). A knowledge engineer is “the individual responsible for structuring and/or constructing an expert system” (5). The design and development of such knowledge-based\nsystems have much to offer knowledge management that also aims at the capture,\nvalidation, and subsequent technology-mediated dissemination of valuable knowledge from experts.\nIntroduction to Knowledge Management\n19\nTable 1.2\nKnowledge management milestones\nYear\nEntity\nEvent\n1980\nDEC, CMU\nXCON Expert System\n1986\nDr. K. Wiig\nCoined KM concept at UN\n1989\nConsulting Firms\nStart internal KM projects\n1991\nHBR article\nNonaka and Takeuchi\n1993\nDr. K. Wiig\nFirst KM book published\n1994\nKM Network\nFirst KM conference\nMid 1990s\nConsulting Firms\nStart offering KM services\nLate 1990s\nKey vertical industries\nImplement KM and start seeing benefits\n2000–2003\nAcademia\nKM courses/programs in universities with\nKM texts\n2003 to present\nProfessional and Academic\nCertification\nKM degrees offered by universities, by\nprofessional institutions such as KMCI\n(Knowledge Management Consortium\nInternational; information available at:\nhttp://www.kmci.org/) and PhD students\ncompleting KM dissertations\nBy the early 1990s, books on knowledge management began to appear and the field\npicked up momentum in the mid 1990s with a number of large international KM\nconferences and consortia being developed. In 1999, Boisot summarized some of these\nmilestones. Table 1.2 shows an updated summary.\nAt the 24th World Congress on Intellectual Capital Management in January 2003,\na number of KM gurus united in sending out a request to academia to pick up the KM\ntorch. Among those attending the conference were Karl Sveiby, Leif Edvinsson, Debra\nAmidon, Hubert Saint-Onge, and Verna Allee. They made a strong case that KM had\nup until now been led by practitioners who were problem-solving by the seat of their\npants and that it was now time to focus on transforming KM into an academic discipline, promoting doctoral research in the discipline, and providing a more formalized\ntraining for future practitioners. Today, over a hundred universities around the world\noffer courses in KM, and quite a few business and library schools offer degree programs\nin KM (Petrides and Nodine 2003).\nFrom Physical Assets to Knowledge Assets\nKnowledge has increasingly become more valuable than the more traditional physical\nor tangible assets. For example, traditionally, an airline organization’s assets included\nthe physical inventory of airplanes. Today, however, the greatest asset possessed by\n20\nChapter 1\nan airline is the SABRE reservation system, software that enables the airline to not\nonly manage the logistics of its passenger reservations but also to implement a seatyield management system. The latter refers to an optimization program that is used\nto ensure maximum revenue is generated from each seat sold—even if each and every\nseat carried a distinct price. Similarly, in the manufacturing sector, the value of nonphysical assets such as just-in-time (JIT) inventory systems is rapidly proving to\nprovide more value. These are examples of intellectual assets, which generally refer to\nan organization’s recorded information, and human talent where such information is\ntypically either inefficiently warehoused or simply lost, especially in large, physically\ndispersed organizations (Stewart 1991).\nThis has led to a change in focus to the useful lifespan of a valuable piece of\nknowledge—when is some knowledge of no use? What about knowledge that never\nloses its value? The notion of knowledge obsolescence and archiving needs to be\napproached with a fresh lens. It is no longer advisable to simply discard items that\nare past their due date. Instead, content analysis and a cost-benefit analysis are needed\nin order to manage each piece of valuable knowledge in the best possible way.\nIntellectual capital is often made visible by the difference between the book value\nand the market value of an organization (often referred to as goodwill). Intellectual\nassets are represented by the sum total of what employees of the organization know\nand know how to do. The value of these knowledge assets is at least equal to the cost\nof recreating this knowledge. The accounting profession still has considerable difficulty in accommodating these new forms of assets. Some progress has been made (e.g.,\nSkandia was the first organization to report intellectual capital as part of its yearly\nfinancial report), but there is much more work to be done in this area. As shown in\nfigure 1.5, intellectual assets may be found at the strategic, tactical, and operational\nlevels of an organization.\nSome examples of intellectual capital include:\nCompetence The skills necessary to achieve a certain (high) level of performance\nCapability\nStrategic skills necessary to integrate and apply competencies\nTechnologies Tools and methods required to produce certain physical results\nCore competencies are the things that an organization knows how to do well, that\nprovide a competitive advantage. These are situated at a tactical level. Some examples\nwould be a process, a specialized type of knowledge, or a particular kind of expertise\nthat is rare or unique to the organization. Capabilities are found at a more strategic\nlevel. Capabilities are those things that an individual knows how to do well, which,\nunder appropriate conditions, may be aggregated to organizational competencies.\nIntroduction to Knowledge Management\n21\nIntellectual capital\nIncreasing complexity\nPolitical negotiation\nMainly subjective\nStrategic\nTactical\nTechnical integration\nMainly objective\nOperational\nFigure 1.5\nThree levels of intellectual capital\nCapabilities are potential core competencies and sound KM practices are required\nin order for that potential to be realized. A number of business management texts\ndiscuss these concepts in greater detail (e.g., Hamel and Prahalad 1990). It should be\nnoted that the more valuable a capability is, and the less it is shared among many\nemployees, then the more vulnerable the organization becomes should that employee\nleave.\nOrganizational Perspectives on Knowledge Management\nWiig (1993) considers knowledge management in organizations from three perspectives, each with different horizons and purposes:\nBusiness perspective Focusing on why, where, and to what extent the organization\nmust invest in or exploit knowledge. Strategies, products and services, alliances, acquisitions, or divestments should be considered from knowledge-related points of view.\nManagement perspective\nFocusing on determining, organizing, directing, facilitating,\nand monitoring knowledge-related practices and activities required to achieve the\ndesired business strategies and objectives\nHands-on perspective Focusing on applying the expertise to conduct explicit knowledge-related work and tasks\n22\nChapter 1\nThe business perspective easily maps onto the strategic nature of knowledge management, the management perspective to the tactical layer, and the hands-on perspective may be equated with the operational level.\nLibrary and Information Science (LIS) Perspectives on KM\nAlthough not everyone in the LIS community is positively inclined toward KM\n(tending to fall back on arguments that IM is enough and that KM is encroaching\nupon this territory, as shown in some of the earlier definitions), others see KM as a\nmeans of enlarging the scope of activities that information professionals can participate in. Gandhi (2004) notes that knowledge organization has always been part of the\ncore curriculum and the professional toolkit of LIS; and Martin et al. (2006, 15) point\nout that LIS professionals are also expert in content management. The authors go on\nto state that\nLibraries and information centers will continue to perform access and intermediary roles which\nembrace not just information but also knowledge management (Henczel 2004). The difference\ntoday is that these traditional roles could be expanded if not transformed . . . through activities\naimed at helping to capture tacit knowledge and by turning personal knowledge into corporate\nknowledge that can be widely shared through the library and applied appropriately.\nBlair (2002) notes that the primary differences between traditional information\nmanagement practiced by LIS professional and knowledge management consist of\ncollaborative learning, the transformation of tacit knowledge into explicit forms, and\nthe documentation of best practices (and presumably their counterpart, lessons\nlearned). The author often uses the phrase “connecting people to content and connecting people to people” to highlight the addition of non-document-based resources\nthat play a critical role in KM.\nAs with KM itself, there is no best or better perspective; instead, the potential added\nvalue is to combine the two perspectives in order to get the most out of KM. One of\nthe easiest ways of doing so would be to ensure that both perspectives—and both\ntypes of skill sets—are represented on your KM team.\nWhy Is KM Important Today?\nThe major business drivers behind today’s increased interest and application of KM\nlie in four key areas:\n1. Globalization of business Organizations today are more global—multisite, multilingual, and multicultural in nature.\nIntroduction to Knowledge Management\n2. Leaner organizations\n23\nWe are doing more and we are doing it faster, but we also\nneed to work smarter as knowledge workers—increased pace and workload.\n3. Corporate amnesia\nWe are more mobile as a workforce, which creates problems of\nknowledge continuity for the organization, and places continuous learning demands\non the knowledge worker—we no longer expect to work for the same organization for\nour entire career.\n4. Technological advances\nWe are more connected—information technology advances\nhave made connectivity not only ubiquitous but has radically changed expectations:\nwe are expected to be on at all times and the turnaround time in responding is now\nmeasured in minutes, not weeks.\nToday’s work environment is more complex due to the increase in the number of\nsubjective knowledge items we need to attend to every day. Filtering over two hundred\ne-mails, faxes, and voice mail messages on a daily basis should be done according to\ngood time management practices and filtering rules, but more often than not, workers\ntend to exhibit a Pavlovian reflex to beeps announcing the arrival of new mail or the\nringing of the phone that demands immediate attention. Knowledge workers are\nincreasingly being asked to think on their feet with little time to digest and analyze\nincoming data and information, let alone time to retrieve, access, and apply relevant\nexperiential knowledge. This is due both to the sheer volume of tasks to attend to, as\nwell as the greatly diminished turnaround time. Today’s expectation is that everyone\nis on all the time—as evidenced by the various messages embodying annoyance at not\nhaving connected, such as voice mails asking why you have not responded to an\ne-mail, and e-mails asking why you have not returned a call!\nKnowledge management represents one response to the challenge of trying to\nmanage this complex, information overloaded work environment. As such, KM is\nperhaps best categorized as a science of complexity. One of the largest contributors to\nthe complexity is that information overload represents only the tip of the iceberg—\nonly that information that has been rendered explicit. KM must also deal with the\nyet to be articulated or tacit knowledge. To further complicate matters, we may not\neven be aware of all the tacit knowledge that exists—we may not know that we don’t\nknow. Maynard Keynes (in Wells 1938, 6) hit upon a truism when he stated “these\n. . . directive people who are in authority over us, know scarcely anything about the\nbusiness they have in hand. Nobody knows very much, but the important thing to\nrealize is that they do not even know what is to be known.” Though he was addressing politics and the economic consequences of peace, today’s organizational leaders\nhave echoed his words countless times.\n24\nChapter 1\nIn fact, we are now entering the third generation of knowledge management, one\ndevoted to content management. In the first generation, the emphasis was placed on\ncontainers of knowledge or information technologies in order to help us with the\ndilemma exemplified by the much quoted phrase “if only we knew what we know”\n(O’Dell and Grayson 1998). The early adopters of KM, large consulting companies that\nrealized that their primary product was knowledge and that they needed to inventory\ntheir knowledge stock more effectively, exemplified this phase. A great many intranets\nand internal knowledge management systems were implemented during the first KM\ngeneration. This was the generation devoted to finding all the information that had\nup until then been buried in the organization with commonly produced by-products\nencapsulated as reusable best practices and lessons learned.\nReeling from information overload, the second generation swung to the opposite\nend of the spectrum, to focus on people; this could be phrased as “if only we knew\nwho knows about.” There was growing awareness of the importance of human and\ncultural dimensions of knowledge management as organizations pondered why the\nnew digital libraries were entirely devoid of content (i.e., information junkyards) and\nwhy the usage rate was so low. In fact, the information technology approach of the\nfirst KM generation leaned heavily toward a top-down, organization-wide monolithic\nKM system. In the second generation, it became quite apparent that a bottom-up or\ngrassroots adoption of KM led to much greater success and that there were many\ngrassroots movements—which were later dubbed communities of practice. Communities\nof practice are good vehicles to study knowledge sharing or the movement of knowledge throughout the organization to spark not only reuse for greater efficiency but\nknowledge creation for greater innovation.\nThe third stage of KM brought about an awareness of the importance of content—\nhow to describe and organize content so that intended end users are aware it exists,\nand can easily access and apply this content. This phase is characterized by the advent\nof metadata to describe the content in addition to the format of content, content\nmanagement, and knowledge taxonomies. After all, if knowledge is not put to use to\nbenefit the individual, the community of practice, and/or the organization, then\nknowledge management has failed. Bright ideas in the form of light bulbs in the pocket\nare not enough—they must be plugged in and this can only be possible if people know\nwhat there is to be known, can find it when they need, can understand it, and, perhaps\nmost important, are convinced that this knowledge should be put to work. A\nslogan for this phase might be something like: “taxonomy before technology” (Koenig\n2002, 3).\nIntroduction to Knowledge Management\n25\nKM for Individuals, Communities, and Organizations\nKnowledge management provides benefits to individual employees, to communities\nof practice, and to the organization itself. This three-tiered view of KM helps emphasize why KM is important today (see figure 1.6).\nFor the individual, KM:\n•\nHelps people do their jobs and save time through better decision making and\nproblem solving\n•\nBuilds a sense of community bonds within the organization\n•\nHelps people to keep up to date\n•\nProvides challenges and opportunities to contribute\nFor the community of practice, KM:\n•\nDevelops professional skills\n•\nPromotes peer-to-peer mentoring\n•\nFacilitates more effective networking and collaboration\n•\nDevelops a professional code of ethics that members can adhere to\n•\nDevelops a common language\nFor the organization, KM:\n•\nHelps drive strategy\n•\nSolves problems quickly\n•\nDiffuses best practices\n•\nImproves knowledge embedded in products and services\n•\nCross-fertilizes ideas and increases opportunities for innovation\n•\nEnables organizations to better stay ahead of the competition\n•\nBuilds organizational memory\nCommunities\nContainers\nContent\nFigure 1.6\nSummary of the three major components of KM\n26\nChapter 1\nSome critical KM challenges are to manage content effectively, facilitate collaboration, help knowledge workers connect, find experts, and help the organization to learn\nto make decisions based on complete, valid, and well-interpreted data, information,\nand knowledge.\nIn order for knowledge management to succeed, it has to tap into what is important\nto knowledge workers, what is of value to them and to their professional practice as\nwell as what the organization stands to gain. It is important to get the balance right.\nIf the KM initiative is too big, it risks being too general, too abstract, too top-down,\nand far too remote to catalyze the requisite level of buy-in from individuals. If the KM\ninitiative is too small, however, then it may not be enough to provide sufficient interaction between knowledge workers to generate synergy. The KM technology must be\nsupportive and management must commit itself to putting into place the appropriate\nrewards and incentives for knowledge management activities. Last but not least, participants need to develop KM skills in order to participate effectively. These KM skills\nand competencies are quite diverse and varied, given the multidisciplinary nature of\nthe field, but one particular link is often neglected, and that is the link between KM\nskills and information professionals’ skills. KM has resulted in the emergence of new\nroles and responsibilities. Many of these new roles can benefit from a healthy foundation from not only information technology (IT) but also information science. In fact,\nKM professionals have a crucial role to play in all processes of the KM cycle, which is\ndescribed in more detail in chapter 2.\nKey Points\n•\nKM is not necessarily something completely new but has been practiced in a wide\nvariety of settings for some time now, albeit under different monikers.\n•\nKnowledge is more complex than data or information; it is subjective, often based\non experience, and highly contextual.\n•\nThere is no generally accepted definition of KM, but most practitioners and profes-\nsionals concur that KM treats both tacit and explicit knowledge with the objective of\nadding value to the organization.\n•\nEach organization should define KM in terms of the business objective; concept\nanalysis is one way of accomplishing this.\n•\nKM is all about applying knowledge in new, previously unencumbered or novel\nsituations.\n•\nKM has its roots in a variety of different disciplines.\nIntroduction to Knowledge Management\n•\n27\nThe KM generations to date have focused first on containers, next on communities,\nand finally on the content itself.\nDiscussion Points\n1. Use concept analysis to clarify the following terms:\na. Intellectual capital versus physical assets\nb. Tacit knowledge versus explicit knowledge\nc. Community of practice versus community of interest\n2. “Knowledge management is not anything new.” Would you argue that this\nstatement is largely true? Why or why not? Use historical antecedents to justify your\narguments.\n3. What are the three generations of knowledge management to date? What was the\nprimary focus of each?\n4. What are the different types of roles required for each of the above three\ngenerations?\nReferences\nAbell, A., and N. Oxbrow. 2001. Competing with knowledge: The information professional in the\nknowledge management age. London: Library Association Publishing.\nAmbur, O. 1996. Sixth generation knowledge management: Realizing the vision in working\nknowledge, http://ambur.net/ (accessed October 20, 2008).\nAPQC. 1996. The American Productivity and Quality Centre, http://www.apqc.org.\nBareholz, H., and P. Tamir. 1992. A comprehensive use of concept mapping in design instruction\nand assessment. Research in Science & Technological Education 10 (1):37–52.\nBarth, S. 2000. Heeding the sage of the knowledge age. CRM Magazine. May, http://www\n.destinationcrm.com/articles/default.asp?ArticleID=832. (accessed October 18, 2008).\nBarton-Leonard, D. 1995. Wellsprings of knowledge—Building and sustaining sources of innovation.\nBoston, MA: Harvard Business School Press.\nBlair, D. 2002. Knowledge management: Hype, hope or help? Journal of the American Society for\nInformation Science and Technology 53 (12):1019–1028.\nBoisot, M. 1999. Knowledge assets. New York: Oxford University Press.\nBroadbent, M. 1997. The emerging phenomenon of knowledge management. Australian Library\nJournal 46 (1):6–24.\n28\nChapter 1\nBrooking, A. 1999. Corporate memory: Strategies for knowledge management. London: International\nThomson Business Press.\nDavenport, E., and B. Cronin. 2000. Knowledge management: Semantic drift or conceptual shift?\nJournal of Education for Library and Information Science 41 (4):294–306.\nDavenport, T., and L. Prusak. 1998. Working knowledge. Boston, MA: Harvard Business School\nPress.\nDavenport, T. 2005. Thinking for a living, how to get better performance and results from knowledge\nworkers. Boston, MA: Harvard Business School Press.\nDeloitte, Touche, Tohmatsu. 1999. Riding the e-business tidal wave, http://www.istart.co.nz/\nindex/HM20/PC0/PVC197/EX245/DOCC65/F11843 (accessed November 4, 1999).\nDenning, S. 2002. History of knowledge management, http://www.stevedenning.com/knowledge_management.html (accessed May 17, 2004).\nDrucker, P. 1994. The social age of transformation. Atlantic Monthly. November, http://www\n.theatlantic.com/politics/ecbig/soctrans.htm (accessed October 18, 2008).\nDrucker, P. 1964. Managing for results. Oxford, UK: Butterworth-Heineman.\nEdvinsson, L., and M. Malone. 1997. Intellectual capital: Realizing your company’s true value by\nfinding its hidden brain power. New York: Harper Collins.\nFeynman, R. 1966. The Development of the Space-Time View of Quantum Electrodynamics.\nScience 153 (3737):699–708.\nFisher, K. M. 1990. Semantic networking: The new kid on the block. Journal of Research in Science\nTeaching 27 (10):1001–1018.\nGandhi, S. 2004. Knowledge management and reference services. Journal of Academic Librarianship\n30 (5):368–381.\nGery, G. 1991. Electronic performance support systems. Cambridge, MA: Ziff Institute.\nGrey, D. 1996. What is knowledge management? The Knowledge Management Forum. March\n1996, http://www.km-forum.org/t000008.htm.\nGroff, T., and T. Jones. 2003. Introduction to knowledge management: KM in business. Burlington,\nMA: Butterworth-Heineman.\nHamel, G., and C. Prahalad. 1990. The core competence of the corporation. Harvard Business\nReview (May–June):79–91.\nHenczel, S. 2004. Supporting the KM environment: The roles, responsibilities, and rights of\ninformation professionals. Information Outlook 8 (1):14–19.\nHobohm, H.-C., ed. 2004. Knowledge management. Libraries and librarians taking up the\nchallenge. IFLA Publications Series 108. Berlin: Walter de Gruyter GmbH & Co. KG.\nIntroduction to Knowledge Management\n29\nJonassen, D. H., K. Beissner, and M. A. Yacci. 1993. Structural knowledge: Techniques for conveying,\nassessing and acquiring structural knowledge. Hillsdale, NJ: Lawrence Erlbaum Associates.\nKaplan, R., and D. Norton.1996. The Balanced Scorecard: Translating Strategy into Action. Boston:\nHarvard Business School Press.\nKlein, D. 1998. The strategic management of intellectual capital. Oxford, UK: ButterworthHeineman, Oxford.\nKoenig, M. 2002. The third stage of KM emerges. KM World, 11 (3), http://www.kmworld.com/\nArticles/Editorial/Feature/The-third-stage-of-KM-emerges-9327.aspx (accessed October 19, 2008).\nLank, E. 1997. Leveraging invisible assets: The human factor. Long Range Planning 30\n(3):406–412.\nLawson, M. J. 1994. Concept mapping. In Vol. 2 of The international encyclopedia of education.,\n2nd ed., edited by T. Husen and T. N. Postlewaite. Oxford: Elsevier Science, 1026–1031.\nLesser, E., and L. Prusak. 2001. Preserving knowledge in an uncertain world. MIT Sloan Management Review 43 (1):101–102.\nMartin, B., A. Hazen, and M. Sarrafzadeh. 2006. Knowledge management and the LIS professions:\nInvestigating the implications for practice and for educational provision. Australian Library Journal\n27 (8):12–29.\nMcGraw, K., and K. Harrison-Briggs. 1989. Knowledge acquisition: Principles and guidelines. Englewood Cliffs, NJ: Prentice Hall.\nMiller, G. 1956. The magical number seven, plus or minus two. Psychological Review 63:81–97.\nNickols, F. 2000. KM overview, http://home.att.net/~discon/KM/KM_Overview_Context.htm\n(accessed October 18, 2008).\nNonaka, I., and H. Takeuchi. 1995. The knowledge-creating company: How Japanese companies create\nthe dynamics of innovation. New York: Oxford University Press.\nNorman, D. A. 1988. The design of everyday things. New York: Doubleday.\nNorton, N., and D. Kaplan. 1996. The balanced scorecard: Translating strategy into action. Boston,\nMA: Harvard Business School Press.\nNovak, J. 1990. Concept mapping: A useful tool for science education. Journal of Research in Science\nTeaching 60 (3):937–940.\nNovak, J. 1991. Clarify with concept maps: A tool for students and teachers alike. Science Teacher\n(Normal, Ill.) 58 (7):45–49.\nO’Dell, C., and C. Grayson. 1998. If only we knew what we know: The transfer of internal knowledge\nand best practice. New York: Simon & Schuster. The Free Press.\nPasternack, B., and A. Viscio. 1998. The centerless corporation. New York: Simon and Schuster.\n30\nChapter 1\nPatel, J., and J. Harty. 1998. Knowledge management: Great concept but what is it? Information\nWeek, March 16, 1998.\nPetrides, L., & Nodine, T. 2003. Knowledge management in education: Defining the landscape.\nThe Institute for the Study of Knowledge Management in Education, http://www.iskme.org/\nwhat-we-do/publications/km-in-education.\nPfeffer, J., and R. Sutton. 1999. The knowing-doing gap: How smart companies turn knowledge into\naction. Boston, MA: Harvard Business School Press.\nPolanyi, M. 1966. The tacit dimension. Gloucester, MA: Peter Smith.\nRigby, D. 2009. Management Tools 2009: An Executive’s Guide, http://www.bain.com/\nmanagement_tools/home.asp.\nRuggles, R., and D. Holtshouse. 1999. The knowledge advantage. Dover, New Hampshire: Capstone\nPublishers.\nSenge, P. 1990. The fifth discipline: The art and practice of the learning organization. New York:\nDoubleday.\nStankosky, M. 2008. Keynote address to ICICKM (International Conference on Intellectual\nCapital, Knowledge Management and Organisational Learning), 9–10.\nStewart, T. 2000. Software preserves knowledge, people pass it on. Fortune 142 (5):4.\nStewart, T. 1997. Intellectual capital. New York: Doubleday.\nStewart, T. 1991. Intellectual capital: Your company’s most valuable asset. Fortune Magazine\nJune:44–60.\nSveiby, K. 1997. The intangible assets monitor. Journal of Human Resource Costing & Accounting\n12 (1):73–97.\nWells, H. G. 1938. World brain. Garden City, NY: Doubleday, Doran & Co.\nWiig, K. 1993. Knowledge management foundations. Arlington, TX: Schema Press.\nWiig, K. M. 2000. Knowledge management: An emerging discipline rooted in a long history. In\nKnowledge management, ed. D. Chauvel and C. Despres. Paris: Theseus.\n2 The Knowledge Management Cycle\nA little knowledge that acts is worth infinitely more than much knowledge that is idle.\n—Kahlil Gibran (1883–1931)\nThis chapter provides a description of the major phases involved in the knowledge\nmanagement cycle, encompassing the capture, creation, codification, sharing, accessing, applying, and reuse of knowledge within and between organizations. Four\nmajor approaches to KM cycles are presented from Meyer and Zack (1996), Bukowitz\nand Williams (2000), McElroy (1993, 2003), and Wiig (1993). A synthesis of these\napproaches is then developed as a framework for following the path that information\ntakes to become a valuable knowledge asset for a given organization. This chapter\nconcludes with a discussion of the strategic and practical implications of managing\nknowledge throughout the KM cycle.\nLearning Objectives\n1. Describe how valuable individual, group, and organizational knowledge is captured,\ncreated, codified, shared, accessed, applied, and reused throughout the knowledge\nmanagement cycle.\n2. Compare and contrast major KM life cycle models including the Meyer and Zack,\nBukowitz and Williams, McElroy, and Wiig life cycle models.\n3. Define the key steps in each process of the KM cycle and provide concrete examples\nof each.\n4. Identify the major challenges and benefits of each phase of the KM cycle.\n5. Describe how the integrated KM cycle combines the advantages of other KM life\ncycle models.\n32\nChapter 2\nIntroduction\nEffective knowledge management requires an organization to identify, generate,\nacquire, diffuse, and capture the benefits of knowledge that provide a strategic advantage to that organization. A clear distinction must be made between information—\nwhich can be digitized—and true knowledge assets—which can only exist within the\ncontext of an intelligent system. As we are still far from the creation of artificial intelligence systems, this means that knowledge assets reside within a human knower—not\nthe organization per se. A knowledge information cycle can be envisioned as the route\nthat information follows in order to become transformed into a valuable strategic asset\nfor the organization via a knowledge management cycle.\nOne of the major KM processes identifies and locates knowledge and knowledge\nsources within the organization. Valuable knowledge is then translated into explicit\nform, often referred to as codification of knowledge, in order to facilitate more widespread dissemination. Networks, practices, and incentives are instituted to facilitate\nperson-to-person knowledge transfer as well as person–knowledge content connections in order to solve problems, make decisions, or otherwise act based on the best\npossible knowledge base. Once this valuable, field-tested knowledge and know-how is\ntransferred to an organizational knowledge repository, it is said to become part of\ncorporate memory. This is sometimes also referred to as ground truth.\nAs was the case with a generally accepted definition of KM, a similar lack of consensus exists with respect to the terms used to describe the major steps in the KM\ncycle. Table 2.1 summarizes the major terms found in the KM literature.\nHowever, upon closer inspection, the differences in term definitions are not really\nthat great. The terms used differ, but there does appear to be some overlap with regard\nto the different types of steps involved in a KM cycle. To this end, four models were\nselected as they met the following criteria:\n•\nImplemented and validated in real-world settings\n•\nComprehensive with respect to the different types of steps found in the KM\nliterature\n•\nIncluded detailed descriptions of the KM processes involved in each of the steps\nThese four KM cycle approaches are from Meyer and Zack (1996), Bukowitz and\nWilliams (2000), McElroy (1999, 2003), and Wiig (1993).\nThe Knowledge Management Cycle\n33\nTable 2.1\nA comparison of key KM cycle processes\nWiig (1993)\nMcElroy (1999)\nRollet (2003)\nBukowitz and\nWilliams (2000)\nMeyer and\nZack (1996)\nCreation\nIndividual and group\nlearning\nPlanning\nGet\nAcquisition\nSourcing\nKnowledge claim\nvalidation\nCreating\nUse\nRefinement\nCompilation\nInformation acquisition\nIntegrating\nLearn\nStore/retrieve\nTransformation\nKnowledge validation\nOrganizing\nContribute\nDistribution\nDissemination\nKnowledge integration\nTransferring\nAssess\nPresentation\nApplication\nMaintaining\nBuild/sustain\nValue realization\nAssessing\nDivest\nMajor Approaches to the KM Cycle\nThe Meyer and Zack KM Cycle\nThe Meyer and Zack KM cycle is derived from work on the design and development\nof information products (Meyer and Zack 1996). Lessons learned from the physical\nproducts cycle can be applied to the management of knowledge assets. Information\nproducts are broadly defined as any information sold to internal or external customers such as databases, news synopses, customer profiles, and so forth. Meyer and\nZack (1996) propose that research and knowledge about the design of physical\nproducts can be extended into the intellectual realm to serve as the basis for a KM\ncycle.\nThis approach provides a number of useful analogies such as the notion of a product\nplatform (the knowledge repository) and the information process platform (the knowledge refinery) to emphasize the notion of value-added processes required in order to\nleverage the knowledge of an organization. The KM cycle consists primarily of creating\na higher value-added knowledge product at each stage of knowledge processing. For\nexample, a basic database may represent an example of knowledge that has been\ncreated. Value can then be added by extracting trends from these data. The original\ninformation has been repackaged to now provides trend analyses that can serve as the\nbasis for decision making within the organization. Similarly, competitive intelligence\ncan be gathered and synthesized in order to repackage raw data into meaningful,\ninterpreted, and validated knowledge that is of immediate value to users, that is, it\ncan be put into action directly. Yet another example is a news gathering service that\n34\nChapter 2\nsummarizes or repackages information to meet the needs of distinct individuals\nthrough profiling and personalization value-added activities.\nMeyer and Zack echoed other authors in stressing “the importance of managing\nthe evolution and renewal of product architecture for sustained competitive success\n. . . different architectures result in different product functionality, cost, quality and\nperformance. Architectures are . . . a basis for product innovation” (Meyer and Zack\n1996, 44). Research and knowledge about the design of physical information products\ncan inform the design of a KM cycle. In Meyer and Zack’s approach, the interfaces\nbetween each of the stages are designed to be seamless and standardized. Experience\nsuggests the critical importance of specifying internal and external user interfaces in\norder to do so.\nThe Meyer and Zack KM cycle processes are composed of the technologies, facilities,\nand processes for manufacturing products and services. He suggests that information\nproducts are best viewed as a repository comprising information content and structure.\nInformation content is the data held in the repository that provides the building\nblocks for the resulting information products. The content is unique for each type of\nbusiness or organization. For example, banks have content relating to personal and\ncommercial accounts, insurance companies hold information on policies and claims,\nand pharmaceutical companies have a large body of scientific and marketing knowledge around each product under design or currently sold.\nIn addition to the actual content, the other important elements to consider are the\noverall structure and approach as to how the content is stored, manipulated, and\nretrieved. The information unit is singled out as the formally defined atom of information to be stored, retrieved, and manipulated. This notion of a unit of information is\na critical concept that should be applied to knowledge items as well. A focus at the\nlevel of a knowledge object distinguishes KM from document management. While a\ndocument management system (DMS) stores, manipulates, and retrieves documents\nas integral wholes, KM can easily identify, extract, and manage a number of different\nknowledge items (sometimes referred to as “knowledge objects”) within the same\ndocument. The unit under study is thus quite different—both in nature and scale. This\nagain links us back to the notion that KM is not about the exhaustive collection of\nvoluminous content but rather more selective sifting and modification of existing\ncaptured content. The term often used today is “content management systems.”\nDifferent businesses once again make use of unique meaningful information units.\nFor example, a repository of financial statements is held in Mead’s Data System Lexis/\nNexis and the footnotes can be defined as information units. A user is able to select\na particular financial statement for analysis based on key attributes of the footnotes.\nThe Knowledge Management Cycle\n35\nAn expertise location system may have, as knowledge objects, the different categories\nof expertise that exist within that organization (e.g., financial analysis) and these\nattributes are used to search for, select, and retrieve specific knowledgeable individuals\nwithin the company.\nA well-designed repository will include schemes for labeling, indexing, linking, and\ncross-referencing the information units that together comprise its content. Although\nMeyer and Zack (1996) specifically address information products, their work is more\nbroadly applicable to knowledge products as well . Whereas knowledge does indeed\npossess unique attributes not found in information (as discussed in chapter 1), this\ndoes not necessitate adopting a tabula rasa approach and reinventing decades of tried,\ntested, and true methods. This is especially true when managing explicit knowledge\n(formal, codified), which has the greatest similarity to information management. In\nthe case of tacit knowledge, new management approaches need to be used, but these\nshould, once. again, build on solid content management processes.\nThe repository becomes the foundation upon which a firm creates its family of\ninformation and knowledge products. This means that the greater the scope, depth,\nand complexity, the greater the flexibility for deriving products and thus the greater\nthe potential variety within the product family. Such repositories often form the first\nkernel of an organizational memory or corporate memory for the company. A sample\nrepository for a railway administration organization is shown in figure 2.1.\nMeyer and Zack analyzed the major developmental stages of a knowledge repository\nand these stages were mapped on to a KM cycle consisting of acquisition, refinement,\nstorage/retrieval, distribution, and presentation/use. Meyer and Zack refer to this as\nthe “refinery.” Figures 2.2 and 2.3 summarize the major stages in the Meyer and Zack\ncycle.\nAcquisition of data or information addresses the issues regarding sources of raw\nmaterials such as scope, breadth, depth, credibility, accuracy, timeliness, relevance,\ncost, control, exclusivity, and so on. The guiding principle is the well-known adage\nof “garbage in garbage out,” that is, source data must be of the highest quality, otherwise the intellectual products produced downstream will be inferior.\nRefinement is the primary source of added value. This refinement may be physical\n(e.g., migrating form one medium to another) or logical (restructuring, relabeling,\nindexing, and integrating). Refining also refers to cleaning up (e.g., sanitizing content\nso as to ensure complete anonymity of sources and key players involved) or standardizing (e.g., conforming to templates of best practice or lessons learned as used within\nthat particular organization). Statistical analyses can be performed on content at this\nstage to conduct a meta-analysis (e.g., a high-level summary of key themes, or patterns\n36\nChapter 2\nWhat’s new\nActions\nRepository\nadministration\nHead office\nRegions\nLinks\nReports\nUpcoming events\nSafety related news\nSimple search\nOne critical, 96 hurt as Amtrak train derails in…\nAdvanced search\nLatest accident reports\nNew publications\nHelp\nNew members\nGlossary\nFigure 2.1\nExample screen for a repository\nProduct family\nContent\nPackaging format\nAccess distribution\nInteractivity\nRepository\nContent\nStructure\nAcquisition\nRefinement\nStorage\nretrieval\nFigure 2.2\nHigh-level view of the Zack Information Cycle\nDistribution\nUsers\nSources\nProduct platform\nPresentation\n37\nRepository\nof research\nresults\nAcquire\nRefine\nCalls and\nsurveys\nAnalyze,\ninterpret, report\nReports\nnewsletters\nbulletins\nUsers\nSources\nThe Knowledge Management Cycle\nStore\nDistribute\nPresent\nIndexed and\nlinked\nknowledge units\nOnline via Web\nand groupware\nInteractive\nselection of\nknowledge units\nEdit and format\nDecompose into\nk units, index,\nand link\nFigure 2.3\nDetailed view of the Zack Information Cycle\nfound in a collection of knowledge objects). This stage of the Meyer and Zack cycle\nadds value by creating more readily usable knowledge objects and by storing the\ncontent more flexibly for future use.\nStorage/retrieval forms a bridge between the upstream acquisition and refinement\nstages that feed the repository and downstream stages of product generation. Storage\nmay be physical (file folders, prin… \nPurchase answer to see full\nattachment
Popular Research Topics in Healthcare
Healthcare is a vast and constantly evolving field, with a wide range of research topics. Some of the most popular research topics in healthcare include:
Chronic diseases: such as diabetes, heart disease, and cancer. Researchers are looking for ways to prevent, diagnose, and treat these conditions.
Mental health: including depression, anxiety, and substance abuse. Researchers are looking for ways to improve treatment and prevent these conditions.
Aging and geriatrics: as the population continues to age, researchers are studying ways to improve the health and well-being of older adults.
Telemedicine: the use of technology, such as teleconferencing, to provide healthcare services remotely.
Personalized medicine: using genetic and other health data to tailor treatments to individual patients.
Infectious diseases: including the study of new and emerging infections, as well as finding ways to prevent and treat existing infections.
Health equity: examining the ways in which social and economic factors contribute to health disparities, and finding ways to improve access to care for all populations.
These are just a few of the many popular research topics in healthcare. As healthcare continues to evolve and change, new research topics will emerge and current topics will become more complex and multifaceted.
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