154 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review Wisdom Learning Knowledge Theory Experience Information Contextual Contextual Filter Data Filter Fig. 1. Knowledge: A Derivative of Theory, Information, and Experience. Most discussions and deﬁnitions of knowledge distinguish between two types of knowledge: tacit andexplicit. Explicit knowledge is knowledge that can be codiﬁed. It is more formal and systematic andis often found in books, enterprise repositories, databases, and computer programs. Tacit knowledge,which is highly personal, is difﬁcult to articulate and is rooted primarily in our contextual experiences.The deﬁnition of tacit knowledge originated with Polanyi’s  concept of tacit knowing. In Polanyi’sdiscussion of human knowledge, he states, “we know more than we can tell” and provides an example offace recognition to illustrate this. While the human can recognize a face, we can not articulate preciselyhow we do it. Nonaka  expands on the concept of tacit knowledge and describes tacit knowledgeas consisting partly of technical skills and also as having a cognitive dimension that consists of mentalmodels, beliefs, and ingrained perspectives. Enterprise or Organizational Knowledge is also a very important concept. Many discussions ofenterprise knowledge are contained in the works of Polanyi ; Nonaka and Takeuchi ; Cook andBrown ; Miller and Morris ; Leonard ; Leonard and Strauss ; Davenport and Prusak .Enterprise knowledge is generally said to be a dynamic mix of individual, group, organizational andinter-organizational experiences, values, information, and expert insights. It originates in the mindsof the individual knowledge worker and emerges as individual knowledge workers interact with otherknowledge workers and the environment. Most discussions of knowledge distinguish between data, information, and knowledge. Miller andMorris , for example, deﬁne knowledge as the intersection of information, experience, and theory.This can be extended to include wisdom, which might be deﬁned as successfully applied knowledge andwhich will often be tacit in nature. Their concept of knowledge is shown in Fig. 1. Cook and Brown  distinguish organizational knowledge from organizational knowing. They referto the concept that knowledge is something that is processed by the individual as the “epistemologyof possession.” Critical to their theory is that the tacit/explicit dimension and the individual/groupdimension yields four types of knowledge that are each distinct and that, none is subordinate to or madeup of any of the others. Additionally, they contend that there is an element of knowledge not capturedby these types of knowledge. An individual can have knowledge of why or what it means to ride abike, but not necessarily be able to personally ride a bike, which requires knowledge that is rooted inpractice. Knowing, as action, calls for an “epistemology of practice.” Figure 2 depicts these four typesof knowledge that interact with knowing and provides an example of each. It is through this interaction,which Cook and Brown describe as a generative dance, that new knowledge is created.
C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 155 Individual Group Knowledge Knowledge Concepts Stories Explicit Knowledge Knowing (As Action) Tacit Knowledge Skills Genres Fig. 2. Interaction of Knowing and Types of Knowledge. Fig. 3. Nonanaka and Takeuchi Based Four Stages of Knowledge Creation. Tacit and explicit knowledge are each critical to the Nonaka and Takeuchi  theory of organizationalknowledge creation. As shown in Fig. 3, the interaction of tacit knowledge and explicit knowledge formsthe four stages of knowledge conversion (socialization, externalization, combination, and internalization)identiﬁed by these authors and which results in different knowledge content. Individual and groupknowledge are not distinct here, but are captured in the theory as the ontological dimension that relatesto the knowledge creation entities. Enterprise Knowledge Management is also a very important concept, as we have noted. Most discourseregarding the management of knowledge comes from two primary schools of thought: one that focuseson existing, explicit knowledge and a second that focuses on the building or creation of knowledge.Some KM studies focus almost entirely upon information technology tools, whereas others focus on KMas a transdisciplinary subject with major behavioral as well as technology concerns. Deﬁnitions andstudies found in the computer science and artiﬁcial intelligence literature generally focus on tools andtechnology. For example, O’Leary  deﬁnes enterprise KM as the formal management of knowledgeresources to facilitate access and reuse of knowledge that is generally enabled by advanced informationtechnology. Knowledge resources vary from enterprise to enterprise, but usually include manuals,letters, customer information, and knowledge derived from work processes. To this end, Alavi andLeidner ) deﬁne knowledge management as the “systemic and organizationally speciﬁed process for
156 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A reviewacquiring, organizing, and communicating both tacit and explicit knowledge . . .”. Other works of interestthat discuss primarily the information systems technologies efforts in knowledge management includeMalhotra [45,46], Maier , Tiwana [73,74], and Srikantaiah and Koenig . The works of Nonaka and Takeuchi  and Leonard  are well-known works concerning themanagement of knowledge which focus on generation and creation of knowledge. There is a majorenvironmental context associated with this “knowledge” and an appropriate deﬁnition of knowledgeis that it is information imbedded in environmental context such that the information can be usedsuccessfully for decision related purposes. A not inappropriate deﬁnition of knowledge managementis that it is the management of the context and environment for knowledge acquisition, representation,transformation, sharing, and use. Many contemporary organizations, with the objective of enhanced organizational performance, haveinitiated knowledge management programs and related activities  to enable the sharing (exchange)and integration of knowledge. Knowledge which is created in the mind of the individuals is generallyof little value to an enterprise unless it is shared. Organizations are rapidly learning that, just becauseappropriate knowledge technology exists, knowledge will not necessarily ﬂow freely throughout anorganization. Cultural issues are regularly cited as one of the concerns of those implementing KMinitiatives. The cultural issues that concern managers as reported by Alavi and Leider  were theimplications of change management, and the ability to convince organizational entities (individuals andbusiness units) to share their knowledge. In many organizations, a major cultural shift would be requiredto change the employee’s attitude toward knowledge sharing. Holtshouse  identiﬁed two knowledgeresearch issues that are related to knowledge sharing: 1) the exchange of tacit knowledge, and 2) the ﬂowof knowledge. While not using the term knowledge sharing explicitly, knowledge sharing is very implicitin each of these activities. There seems generally uniform agreement among these authors and manyothers, such as the work of Thomas et al. , that the beneﬁt of knowledge management initiatives willnot be realized unless the cultural, management, human, social, and organizational elements or factors arealigned appropriately. Of course, appropriate attention needs to be paid to the many technology facets that enable successful knowledge management as well. There have been many recent efforts to provideintegration and synthesis of knowledge management efforts. In a recent bibliometric analysis , noless that 1407 knowledge management publications were noted. Another recent work by Nonaka andPeltokorpi  attempts present a review and categorization of what the authors describe as the “twentymost inﬂuential knowledge management publications in management journals”.2. Existing KM models and frameworks A model is a representation of reality. Casti  deﬁnes a taxonomy of models that include exper-imental, logical, mathematical/computational, and theoretical. Most KM models are theoretical in thesense that they are an imagined mechanism, or process that has been developed to account for observedphenomena. Theoretical models are based on hypothesized relationships among factors. Within thistaxonomy, models are further categorized by their purpose: Predictive – enables us to predict what a system’s behavior will be. Explanatory/descriptive – provides a framework in which past observations can be understood as partof an overall process. These models are also called descriptive because they are explicit descriptions thatcapture and organize information. Prescriptive – provides a picture of the real world as it will be if certain postulates (prescriptions) orformal axiomatic rules of behavior are applied.
C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 157 A survey of the literature ﬁnds many descriptive KM models and frameworks. Many of the frameworkshave been developed by large consulting ﬁrms and have been used both for internal and external KMimprovement. Apostolou and Mentzas  distinguish four groups of KM frameworks: those that focus on knowledgegeneration, those that focus on knowledge processes, those that focus on technology, and those that areholistic. They identify and provide an overview of models in each group. The model developed byNonaka and Takeuchi  and the framework proposed by Leonard  are included in the knowledgegeneration group. The knowledge processes group frameworks include those of the APQC [2–4] andRomhardt and Probst . Within IBM’s Knowledge Management Framework , the primary businessgoals that can be improved through knowledge management are highlighted: innovation, responsiveness,productivity, and competency. Holistic frameworks emphasize the interdisciplinary nature of KM andexplicitly include technology, processes, organizational structures, and cultural issues. The holisticframeworks include those of Coopers and Lybrand  and the Intellectual Capital Framework (ICM)of IBM . Based on an analysis and adaptation of these frameworks, Apostolou and Mentzas adopted a KM framework that included six elements: context, goals, strategy, culture, KM processes,and technological and organizational infrastructure. The framework was used to perform a comparativeanalysis of KM efforts. Holsapple and Joshi  provide a description and comparative analysis of ten descriptive KMframeworks and models. Each of these frameworks or models attempts to explain one or more aspectsof the KM phenomena. They analyze the frameworks in ﬁve areas: 1) the focus, 2) roots/origin, 3)knowledge resources, 4) knowledge manipulation activities, and 5) inﬂuences on the conduct of KM.The ﬁrst two areas include the context which describes the objective and development process of theKM framework. The other areas address the conduct of KM within an organization. Findings andobservations of the analysis include the following: KM frameworks are being approached from a varietyof perspective and methodologies,minimum attention has been given to knowledge resources, no commonway of characterizing knowledge manipulation activities or inﬂuences on the conduct of KM is beingused, and no individual KM framework subsumes the others. They conclude that a more comprehensiveKM framework is needed in order to more fully describe knowledge manipulation activities and theirrelationships. Arthur Andersen and APQC  developed a KM Assessment Tool TM (KMAT) to promote discussionabout organizational KM and to facilitate benchmarking. This tool is built around an organizationalKM model that consists of the KM processes (Apply, Share, Create, Identify, Collect, Adapt, andOrganize) and its enablers (Leadership, Measurement, Technology, and Culture). This tool is used tocharacterize the current state of the processes and to assess how well the enablers within an organizationare supporting the KM processes. Liebowitz  also discusses a variety of issues and some tools forknowledge management. Bukowitz and Williams  present a KM Process Framework that includes both the tactical andstrategic processes of managing knowledge assets. They espouse managing both the tactical and strategicelements together to ensure that the right mix of knowledge assets and the capability to access themare available. In this model, the tactical components of KM consist of the knowledge managementprocesses that knowledge workers exercise as they carry out day-to-day work activities: get, use, learn,and contribute. According to Bukowitz and Williams , the get and use elements of the KM frameworkare process elements that organizations have generally been performing for a long time. The learn andcontribute process elements, described as being relatively new to organizations, are indicated to be themost challenging. A brief description of these process elements, which are tactical in nature, follows.
158 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review Get – This is the process step a knowledge worker uses to ﬁnd information to solve a problem.Knowledge workers are familiar with seeking information. The challenge for the organization is how todo it efﬁciently with the glut of information available today. Use – This process step is often associated with innovation. Organizations are interested in knowledgeworkers using and combining information in ways not previously thought of to create more innovativesolutions. Learn – This process step is generally new from the perspective that organizations are now formallyexamining learning as a way of generating and keeping competitive advantage. Contribute – This is the process step of getting knowledge workers to contribute to organizationalknowledge bases. It will also be new for most organizations. Technology often exists to support someof this, such as to help organize and post information. The challenge is getting employees to believethat there is some beneﬁt in contributing knowledge for them. This element is one facet of enterpriseknowledge sharing; however, knowledge sharing is a broader concept and encompasses sharing of bothtacit and explicit knowledge at the individual, group, and enterprise level. There is also a strategic component of the framework, and the goal of the strategic component ofthe KM Process Framework is to ensure that knowledge strategy is aligned with business strategy. Thestrategic process steps, which include assess, build/sustain, and divest, are performed by KM leadershipand groups and are deﬁned as follows: Assess – This process step assesses how well the current knowledge assets fulﬁlls current knowledgeneeds. It includes developing metrics that link the investments in knowledge bases to the companybeneﬁts. Build and Sustain – This process step entails the design and maintenance of knowledge bases with thegoal of ensuring that the organization remains viable. Divest – This process step examines the organizational knowledge bases in terms of opportunity costsand alternative sources of value. Knowledge bases are assessed to determine whether they are enough tojustify continued maintenance. The KM Process Framework is used as the foundation of the diagnostics and improvements guidanceprovided in the Knowledge Management Fieldbook of Bukowitz and Williams . As noted, Nonaka and Takeuchi  present a theory of knowledge creation that consists of fourknowledge conversion phases: socialization, externalization, combination, and internalization. Theconversion phase takes place in ﬁve steps: sharing of tacit knowledge, creating concepts, justifyingconcepts, building an archetype, and cross-leveling knowledge. Critical to this theory is the conceptof levels of knowledge: individual, group, organizational, and inter-organization. Knowledge sharingprimarily occurs during the socialization, externalization, and combination phases. It does not generallyoccur during internalization. The importance of sharing in the creation of knowledge is captured in theconcept of ‘redundancy.’ Those concepts created by an individual or group will often need to be sharedby other individuals who may not need the concept initially or immediately. During the socializationstage, sharing occurs primarily at the individual and group levels. In the externalization stage, knowledgeis codiﬁed and shared at the group and organizational levels. In the cross-leveling knowledge phase, anenterprise shares knowledge both intra- and inter-organizationally. The relationship of knowledge sharingto the enterprise knowledge-creation process, as adapted from Nonaka and Takeuchi’s organizationalknowledge-creating process, is depicted in Fig. 4. Knowledge creation is a natural phenomenon; however, within the context of an enterprise, there areoften practices that are embedded in organizational culture, processes, and strategies that inhibit thisprocess. In addition, there may be insufﬁcient technological support to enable knowledge sharing, evenwhen other organizational support is present, although this would represent an uncommon occurrence.
C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 159 Fig. 4. Knowledge Sharing and Enterprise Knowledge-Creation Model of Nonaka and Takeuchi. KM Ontology Knowledge Resources Knowledge Manipulation KM Influences Activities Managerial Resource Schema Content Internalizing Acquiring Culture Artifacts Selecting Using Environmental Strategy Participants Knowledge Purpose Infrastructure Human Computer-based Computer- Fig. 5. Knowledge Management Ontology. Holsapple and Joshi  developed a knowledge management (KM) ontology using a collaborativemethodology based on a study of international practitioners and researchers. The design of the KMontology is based on Knowledge Management Episodes (KMEs) which consist of activities that occurfrom the time a knowledge need is recognized until the time the knowledge need is satisﬁed. During aKME, knowledge resources are manipulated in KM activities by knowledge participants, which are gov-erned by KM inﬂuences. Examples of KMEs include making a decision, solving a problem, developinga prototype, or servicing a customer. The major components of the KM ontology are basic knowledgemanipulation activities that occur with KMEs, major inﬂuences on KM episodes, and knowledge re-sources. A brief description of the KM ontology components and subcomponents, depicted in Fig. 5, isof interest. The knowledge manipulation component of the ontology consists of four major activities: acquiring,selecting, internalizing, and using knowledge. Each of these activities is further decomposed intosub-activities as follows:
160 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review Acquiring knowledge – Refers to the activity of identifying knowledge in the environment and transforming it to knowledge that can be used. Sub-activities include: identifying, capturing, organizing, and transferring knowledge. Selecting Knowledge – Refers to the activity of identifying knowledge needs within an organization. Sub-activities include: identifying, capturing, organizing, and transferring knowledge. Internalizing Knowledge – Includes the activities that make knowledge part of an organization. Sub-activities include: assessing, targeting, structuring, and delivering. Delivering involves storing, updating, disseminating, and sharing knowledge. Using Knowledge – Incorporates the activities that apply existing knowledge to generate new knowl- edge or externalization (make available outside the organization). Sub-activities include: generating and externalizing knowledge. The inﬂuence component of the KM ontology includes factors that inﬂuence the success of KMinitiatives in an organization. The inﬂuence factors are categorized into three major types of inﬂuences:managerial, resource, and environmental. The sub-factors of these three inﬂuences are: Managerial – includes leadership, coordination, control, and measurement. Resource – includes ﬁnancial, knowledge manipulation skills, material, human, and knowledge resource. Environmental (external to organization) – includes competition, fashion, markets, and technology. The KM ontology resource component includes the major knowledge resources that should be availableto an organization during a KME. The taxonomy of ingredients in this component includes contentknowledge resources and schema knowledge resources. Content knowledge resources are tangible(useable) representations of knowledge and can be either of two types: participant knowledge andartifacts. Participant knowledge resources, which can be either human or material resources, haveknowledge processing capabilities, whereas artifacts do not. Examples of material knowledge resourcesare decision support systems, expert systems and performance support systems. Schematic knowledge, as deﬁned by Holsapple and Joshi , is knowledge that is embedded in theworkings of an organization. While this type of knowledge resource can be captured in artifacts, it existsindependently. The schematic knowledge resources identiﬁed in the KM ontology are as follows: Culture – an organization’s values, norms, and unwritten rules. Infrastructure – the knowledge that structures the participants in the organization based on role, relationships, and policies that govern the relationships. Purpose – deﬁnes the reason an organization exists. It can include mission, vision, purpose, and objectives. Strategy – deﬁnes how an organization plans to achieve its purpose. All of these are needed ontological components in a knowledge management process. There are a relatively large variety of related efforts. Of special note are the works of Pfeffer andSutton , Stewart , Morey et al. , Davis et al. , Dalkir , Garavelli et al. , Mertinset al. , and Von Krogh [76,77]. Wong and Aspinwall  also review knowledge management frameworks and place particular em-phasis upon identifying suitable KM implementation frameworks. Based on their studies they suggestﬁve guidelines for developing an implementation framework: 1. Incorporate a clear structure within the framework to enable construction and organization of the to-be-identiﬁed KM tasks.
C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 161 2. Address the different knowledge resources or types of knowledge to be managed. 3. Include the KM processes that will be needed to manipulate the knowledge. 4. Identify and include signiﬁcant inﬂuences that will affect performance of KM efforts. 5. Provide balanced and integrated technological and cultural, social and behavioral perspectives. These authors suggest that their studies indicate that none of the currently available frameworks are incomplete accord with these guidelines. We believe that this is potentially capable of realization throughdevelopment of a knowledge management process architectural framework (KMPAF) and a knowledgemanagement process architecture development process (KMPADP) that can be used to instantiate theKMPAF such as to result in an appropriate knowledge management process architecture that ultimatelyleads to an enterprise or organizational knowledge management process. Back et al.  provide a framework and methodology for managing knowledge in networks. Theyespouse managing knowledge in networks needs to integrate various disciplines such as human resource,organization development, change management, strategy, information technology, sociology, and net-work theory. The network-based approach differs from other KM frameworks in that it attempts tointegrate these diverse disciplines into a holistic framework. It also addresses both explicit and im-plicit knowledge and where and how knowledge is being created and transferred. Knowledge workprocesses, knowledge network architecture, and facilitating conditions are important building blocks forthe methodology. There are also several works by Rouse and Sage [59–61,65,66] that focus strongly on the role ofinformation systems frontiers and contemporary information technology in supporting systems engi-neering and systems management, including a very recent one  that is much concerned with effectiveenterprise management.3. Enterprise knowledge sharing Enterprise knowledge sharing can occur in many forms. While a survey of the literature yieldsnumerous KM articles, frameworks and models, and assessment tools, few are targeted speciﬁcally atknowledge sharing. Enterprise knowledge sharing is often described in the literature as being critical tothe performance of knowledge creation and in the leveraging of knowledge . Ives et al.  describe knowledge sharing as a human behavior that must be examined in the context ofhuman performance. Human performance is described as a complex activity that is inﬂuenced by manyfactors. They describe a human performance model that includes the business context and organizationaland individual factors. Organizational performance factors include: structure and roles, processes,culture, and physical environment. Individual performance factors include: direction, measurement,means, ability, and motivation. These inter-related factors each contribute to successful knowledgesharing and can not be effective alone. A description of these factors and how they contribute toknowledge sharing is of interest. 1. Business Context – Employees are more likely to share knowledge if the behavior is linked to business goals. These authors emphasize the need for the business strategy to be communicated to employees. 2. Organizational Structure and Roles – Supporting knowledge sharing is encouraged by means of a two-part organizational structure: 1) a dedicated KM staff who owns the knowledge processes, templates, and technologies; and 2) knowledge sponsors and integrators from the business units who “own” the knowledge content.
162 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 3. Organizational Processes – Knowledge processes need to be built into the daily work process, and well-deﬁned knowledge capture processes should exist. Knowledge processes should depend on the type and level of knowledge. 4. Organizational Culture – In addition to stressing the importance of organizational culture to Knowledge-sharing (KnS) behavior, the authors also emphasize the importance of understanding the cultural differences between individual knowledge workers. Steps to achieving a KnS culture include setting KnS priorities, strong KnS leadership, KnS investment support, and modeling by senior leadership (i.e., visible advocacy of KnS behavior). 5. Physical Environment – Many organizations are beginning to recognize the need to create envi- ronments (e.g., quiet space, informal environments, relaxed physical environments enhanced by technology) that are appropriate for knowledge sharing. 6. Direction – Knowledge sharing is a new behavior to many organizations, so guidance is needed to achieve enhanced value. Guidance for knowledge sharing is therefore needed in terms of the contextual awareness abstractions of what to share, when to share, and how to share, as well as why share, what to share and who to share with. Guidance of this sort that is given in the context of the daily work processes is especially useful to knowledge workers. 7. Measurement – Human performance measurement is becoming increasingly more important as knowledge-based organizations begin to recognize that the organization’s greatest resource is comprised of its people. How a KnS proﬁciency has been established and measured at the authors company is described. KnS expectations are communicated and translated into actions that can be documented in a performance review. Individual and team KnS metrics provide deﬁnition to KnS behavior and communicate that the organization places a value on it. Documenting the mission impact (outcome metrics) of KnS behavior is important to obtaining and keeping senior leadership support. 8. Means – Effective enterprise knowledge sharing can not be done without information technology (IT). The existing knowledge management infrastructure (i.e., e-mail, internet, intranet, group- ware, and web technologies) can be extended to support KnS processes. Videoconferencing, application sharing, and electronic meeting support are KnS enablers. Many organizations focus on the IT component of knowledge sharing because it is the most tangible; however, it is im- portant to provide the means to accomplish this within the context of the various organizational performance attributes. 9. Ability – KnS behavior within a corporate environment needs ongoing support and training. It is important to coordinate or integrate KnS training with the entire array of training initiatives. Knowledge workers need training prior to job performance, knowledge support during job exe- cution, and time to reﬂect on lessons learned to improve individual learning and to contribute to organizational learning. 10. Motivation – There are individual and cultural differences that drive KnS behavior. Knowledge sharing is best supported by intrinsic rewards (e.g., saving work time, participating in useful and interesting dialog, or professional pride in being recognized as an expert). External rewards must be selected carefully because what motivates in one organization may be a barrier in another. The importance of employee care and trust is also emphasized. KnS motivation factors cited include: being a normal part of the job, being related to career growth, receiving thanks and recognition, knowing how others used their contributions, and knowing it is expected behavior. For many companies, getting employees to share knowledge and to contribute knowledge to organi-zational repositories is the focus of their knowledge management programs. Liebowitz and Chen 
C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 163espouse the view that establishing a KnS proﬁciency can help to jump start and build a KnS culture. Theydeﬁne a KnS proﬁciency as “an attribute that allows the creation of knowledge to take place through anexchange of ideas, expressed either verbally or in some codiﬁed way.” Their investigation of existingKnS assessment instruments found several assessment instruments that broadly cover the area of KM, butfew if any that explicitly addressed knowledge sharing. Recognizing this void, Liebowitz and Chen developed a KnS effectiveness inventory that consists of 25 questions covering four areas: 1. Communications ﬂows – assesses how knowledge and communication exchanges are captured and disseminated throughout the organization. 2. KM environment – examines internal cultural factors. 3. Organizational facilitation – assesses the sophistication of the KM infrastructure and KnS capa- bility. 4. Measurement – assesses the likelihood of knowledge sharing and KM being successful within the organization. The effectiveness inventory was designed to assess how well an organization is performing KnSactivities. The results of the inventory instrument allow an organization to pinpoint potential areas ofimprovement. APQC conducted a benchmarking study to determine what best practice ﬁrms do to develop a KnSculture. This study  examined culture on three levels: 1. Company’s espoused philosophy, values, structures, and systems 2. Behavior of people’s peers and managers 3. Deeper core company values. This study found that several factors inﬂuenced and/or enabled a KnS culture to varying degrees.The factors included: link between knowledge sharing and business strategy; ﬁt with overall cultureof the organization; ﬁt with daily work; role of leaders and managers; role of human networks; andinstitutionalization of learning disciplines. This study provided no insight into the extent of the inﬂuenceof each factor. It did conclude, however, “. . . what draws people to share is different in variousorganizations and matches the company’s core values as well as the look and feel of other organizationalprocesses.” Managing knowledge sharing efforts is very important. Huysman and de Wit  identify the set ofKnS practices that facilitate and structure knowledge sharing for knowledge workers. They conductedresearch on KnS practices with ten large (more than 1000 employees each) companies. They identifythree primary reasons for sharing knowledge: knowledge acquisition, knowledge reuse, and knowledgecreation. They identify the following traps: the information and communication technology (ICT) trap,the management trap, and the local learning trap. The authors assert that the second wave of KM will putknowledge connections center stage and will link the idea of social capital (social networks that createopportunities) with KM. Social capital has three dimensions: structural (network ties), cognitive (sharedcodes and languages); and relational (mutual trust and norms). While the authors describe the ﬁrst waverather negatively, they conclude by recognizing the signiﬁcance and importance of managing knowledgesharing in the second wave. Liao et al.  assert that knowledge sharing in business is strongly related to behavioral factors. Theyconducted a case study of a Taiwanese ﬁnance and securities ﬁrm in order to investigate employee attitudesand intentions regarding knowledge sharing in the context of employee relationships. The premise ofthis research is that by managing employee relations an organization could have a positive impact onknowledge sharing. The variables examined in the study include the working environment, conditions of
164 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review Systems Planning Research, System Acquisition, and Development, Test and Production, or Marketing Evaluation Procurement Conversion Knowledge Perspectives Conversion Sharing Knowledge Principles Sharing Knowledge Practices Learning over Time Fig. 6. Knowledge Sharing and Conversion across SE Processes.respect, conditions of support, justice perception, relationships with superiors, self-satisfaction, and self-learning. The study found that conditions of respect, justice perception, and relationships with superiorscould affect attitudes toward knowledge sharing in a major way. The study found that employees withgood relationships with their ﬁrm would generally share knowledge voluntarily and unconditionally,while employees with not so good relationships with their ﬁrm were reluctant to share knowledge andexperiences with colleagues. The authors conclude that organizations should devote much attention tomanaging employee relationships because of the impact they can have on the resulting KnS behavior. In another notable work, Styrhre and Kailing  describe different knowledge sharing programs attwo large international corporations in the paper and pharmaceuticals industry. The ability to acquire, create, and make actionable the knowledge needed to achieve business goalsis critical to enterprises that engage professionally in systems engineering. Both strategic and tacticalknowledge are needed to remain competitive. Systems engineering consists of three primary lifecy-cles : Systems Planning and Marketing; Research, Development, Test and Evaluation; and SystemAcquisition, Production and Procurement. As illustrated in Fig. 6, knowledge is created in each of thesephases and is shared and used by other phases. This results in proactive and interactive learning. In this work, knowledge perspectives represent the strategic knowledge about future directions. Thisknowledge is used primarily by the Systems Planning and Marketing lifecycle. Knowledge principles areformal problem solving methods and are used primarily during the Research, Development, and Test andEvaluation lifecycle. Knowledge practices enable systems acquisition based upon generally proven andlow risk approaches. In order for knowledge to ﬂow properly from one life cycle to the other, knowledgeconversion and knowledge sharing are each needed. In order to improve enterprise knowledge sharing, effective ways of measuring KnS behavior areneeded. As previously discussed, there are two types of knowledge: tacit and explicit. Lee investigates KnS measurement from the perspective of the four stages of knowledge conversion asdescribed by Nonaka and Takeuchi : tacit to tacit, tacit to explicit, explicit to explicit, and explicitto tacit. He contends that most KnS metrics focus on the tacit to explicit or explicit to tacit knowledgeconversion. Examples of metrics for the tacit to explicit knowledge conversion process include: – Number of shared documents published.
C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 165 – Number of suggestions for improvement. – Corporate directory coverage. – Number of patents issued. – Number of presentations made. Examples of explicit to tacit knowledge conversion process metrics include: – Number of hits on document repository. – Subscriptions to journals. – Attendance at group presentations. – Size of discussion data bases. Lee contends that tacit to tacit knowledge sharing contributes to 90% of total knowledge sharing.Emphasizing the importance of tacit knowledge sharing, Lee  proposes KnS “in process” metricsfor the tacit to tacit knowledge conversion process. “In process” metrics measure the processes that canlead to the outcome metrics found on the Balanced Scorecard . Given the nature of tacit knowledge,the author suggests that measuring social interactions can provide a workable proxy for measuring thedegree of tacit to tacit knowledge sharing. Adapting the Social Network Analysis techniques, Leedeveloped KnS metrics for tacit to tacit knowledge transfer based on the number and perceived quality ofrelationships. Lee  indicated that the Global Maintenance Network (GMN) was established by BHP,a global resource company headquartered in Australia, to enable sharing of best maintenance practicesworldwide. A case study using the adapted Social Network Analysis technique was conducted at BHP.The tacit to tacit KnS metrics included the following: – Number of links per respondent. – Frequency of advice seeking. – Individual with highest number of nominations for being an expert in a given area. – Ratio of internal to external links. – Proportion of total contacts that are inward. – Proportion of total contacts that are outward. These metrics are intended to complement the traditional Balanced Scorecard metrics captured by theorganization. The MITRE Corporation  developed a KM Measurement framework that includes two goals relatedto knowledge sharing: enable and motivate knowledge sharing and actually share knowledge. Using theBalanced Scorecard . methodology, indicators for the achievement of the KnS goals were identiﬁed.Indicators for explicit and tacit knowledge sharing included: demographics of work product capture,demographics of knowledge exchanges, strength of communities of practice, and breadth of knowledgecapture. Indicators for enabling and motivating knowledge sharing include: reward and recognition;alignment with business strategy; alignment with culture; effective KnS tools; and support structure forcommunities of practice (CoPs). Measures were identiﬁed and captured for each of the indicators. Inanother recent and useful work, Brauner and Becker  discuss issues associated with the managementof knowledge sharing systems. These authors suggest that it is explicit and unshared expertise, ratherthan implicit and shared knowledge, which is truly the most valuable for organizations. They proposeknowledge management as an instrument of organizational learning since a major objective is managingthe organizational accessibility of this knowledge. In this sense, knowledge management as a socialprocess is stressed, and not just knowledge management and sharing as a technical process.
166 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review4. KM assessment and improvement models The KM literature identiﬁes several KM maturity models [34,37,49] that are used to assess or improvethe maturity of the KM process. These models generally leverage the work of the several Carnegie MellonUniversity (CMU) Software Engineering Institute (SEI) Capability Maturity Models (CMMs) . Kochikar  leveraged the work of the SEI CMM in the development of the knowledge managementmaturity (KMM) model. The KMM model has ﬁve levels: 1- Default, 2- Reactive, 3- Aware, 4-Convinced, and 5- Sharing. The knowledge lifecycle has three stages: acquisition, sharing/dissemination,and reuse. The state of the three key result areas (process, people, and technology) is used to assessthe KMM level. The Systems Engineering Capability Maturity Model (SE-CMM) identiﬁes seventeenprocess areas that are critical to systems engineering. Each of these process areas consists of multiplebase practices. While the SE-CMM does not explicitly discuss knowledge management, the captureactivity is explicit in many of the base practices.5. Summary – the missing pieces Many descriptive KM representations exist. They differ in their focus and purpose. These repre-sentations serve to provide a foundation for understanding KM and potential initiatives that can resultin an enhanced state of KM within an organization, but they generally provide minimum support forprescriptive and predictive study and assessment. Additionally, most KM representations lack automatedsimulation-based support that allows empirical experimentation. Enterprise knowledge sharing is a critical aspect of the leveraging and transmission of knowledge, andof the enterprise knowledge creation process. Enterprises are as diverse as the knowledge workers thatcomprise them. KM leadership and practitioners need enhanced tools to help them better understandwhat inﬂuences knowledge workers to share. Knowledge sharing is a human behavior that is inﬂuencedby both the KnS environment and other knowledge workers in the environment. Knowledge workers arediverse and heterogeneous. The KM models and tools that exist today do not address the heterogeneousattributes of the knowledge workers and pay minimal attention to the interaction between knowledgeworkers. To improve the KnS process, the interaction of the knowledge worker within the environment aswell as the interactions among knowledge workers must be addressed. A complex adaptive system basedenterprise KnS model may well speak effectively to these concerns and are addressed in a companionpaper. In this survey paper we have attempted to present an overview of contemporary knowledge managementissues. While we have discussed a number of relevant works, there are a number of value [8,25–27,30,50,53,69,70,77–79] that we have not speciﬁcally discussed here.References  M. Alavi and D. Leider, Knowledge Management Systems: Emerging Views and Practices from the Field, (Vol. Track 7), Proceedings for the 32nd Hawaii International Conference on on Systems Sciences, 5–8 Jan. 1999, 8.  American Productivity & Quality Center (APQC). 1997. Using Information Technology to Support Knowledge Manage- ment. Consortium Benchmarking Study – Best-Practice Report.  American Productivity & Quality Center (APQC). 1999. Creating a Knowledge-Sharing Culture. Consortium Bench- marking Study – Best-Practice Report.  American Productivity & Quality Center (APQC). 2000. Successfully Implementing Knowledge Management. Consor- tium Benchmarking Study – Final Report, 2000.
C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 167  D. Apostolou and G. Mentzas, Towards a Holistic Knowledge Leveraging Infrastructure: The KNOWNET Approach, Proceeding of the 2nd International Conference on Practical Aspects of Knowledge Management (PAKM98) Basel, Switzerland, 29–30 October 1998, 3–1 through 3–8.  D. Apostolou and G. Mentzas, Managing Corporate Knowledge: A Comparative Analysis of Experiences in Consulting Firms, Part 1. Knowledge and Process Management 6(3) (1999), 129–138.  Arthur Anderson and APQC. 1996. The Knowledge Management Assessment ToolTM : External Benchmarking Version.  E.M. Awad and H.M. Ghazin, Knowledge Management, Prentice Hall, Englewood Cliffs NJ, 2003.  A. Back, G. von Krogh, A. Seufert and E. Enkel, Putting Knowledge Networks into Action: Methodology, Development, Maintenance, Springer-Verlag, Heidelberg, 2005. E. Brauner and A. Becker, Beyond Knowledge sharing: The Management of Transactive Knowledge Systems, Knowledge and Process Management 13(1) (2006), 62–71. J.S. Brown and P. Duguid, The Social Life of Information, Harvard Business School Press, Boston, MA, 2000. W.R. Bukowitz and R.L. Williams, The Knowledge Management Fieldbook, Financial Times Prentice Hall, London, 1999. Carnegie Mellon University/Software Engineering Institute (SEI). 1995. The Capability Maturity Model: Guidelines for Improving the Software Process. Addison-Wesley Publishing Company, Inc. J.L. Casti, Would-Be Worlds: How Simulation Is Changing the Frontiers of Science, John Wiley & Sons, Inc., New York, NY., 1997. C.W. Choo, The Knowing Organization: How Organizations Use Information to Construct Meaning, Create Knowledge and Make Decisions, International Journal of Information Management 16(5) (1996), 329–340. R.E. Cole, ed., Special Issue on Knowledge and the Firm, California Management Review 40(5) (1998). S.D. Cook and J.S. Brown, Bridging Epistemologies: The Generative Dance Between Organizational Knowledge and Organizational Knowing, Organization Science 10(4) (July-August 1999), 381–400. K. Dalkir, Knowledge Management in Theory and Practice, Butterworth-Heinemann, 2005. T.H. Davenport and L. Prusak, Working Knowledge: How Organizations Manage What They Know, Harvard Business School Press, Boston MA, 1998. J. Davis, E. Subrahmanian and A. Westerberg, eds, Knowledge Management: Organizational and technological Dimen- sions, Verlag, Heidelberg, 2005. N.M. Dixon, Common Knowledge: How Companies Thrive by Sharing What They Know, Harvard Business School Press, Boston MA, 2000. D.P. Ford, Trust and Knowledge Management: The Seeds of Success, in: Handbook on Knowledge Management 1: Knowledge Matters, C.W. Holsapple, ed., Springer-Verlag, 2003. C. Garavelli, M. Gorgoglione and B. Scozzi, Knowledge Management Strategy and Organization: A Perspective of Analysis, Knowledge and Process Management 11(4) (2004), 273–282. Y. Gu, Global Knowledge Management Research: a Bibliometric Analysis, Scientometrics 61(2) (2004), 171–190. Harvard Business Review on Knowledge Management, Harvard Business School Press, 1998. C.W. Holsapple, ed., Handbook on Knowledge Management 1: Knowledge Matters, Springer-Verlag, 2003. C.W. Holsapple, ed., Handbook on Knowledge Management 2: Knowledge Directions, Springer-Verlag, 2003. C.W. Holsapple and K.D. Joshi, Description and Analysis of Existing Knowledge Management Frameworks, (Vol. Track1), Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences, 5–8 Jan. 1999, 15. C.W. Holsapple and K.D. Joshi, A Knowledge Management Ontology, in: Handbook on Knowledge Management 1: Knowledge Matters, C.W. Holsapple, ed., Springer-Verlag, 2003, 89–124. D. Holtshouse, “Knowledge Research Issues” in Special Issue on Knowledge and the Firm, California Management Review 40(3) (Spring 1998), 277–280. K.T. Huang, Capitalizing Collective Knowledge for Winning, Execution and Teamwork, Journal of Knowledge Manage- ment 1(2) (February 1997), 149–156. M. Huysman and D. de Wit, Practices of Managing Knowledge Sharing: Towards a Second Wave of Knowledge Management, Knowledge and Process Management 11(1) (2004), 81–92. W. Ives, B. Torrey and C. Gordon, Knowledge Sharing is Human Behavior, in: Knowledge Management: Classic and Contemporary Works, D. Morey, M. Maybury and B. Thuraisingham, eds, MIT Press, Cambridge MA, 2003. M. Kaner and R. Karni, A Capability Maturity Model for Knowledge-Based Decisionmaking, Information, Knowledge, and Systems Management 4(4) (2004), 225–252. R.S. Kaplan and D.P. Norton, The Balanced Scorecard, Harvard Business School Press, Boston, Massachusetts, 1996. M.E. Knapp, Knowledge Management: The Key to Success in the 21st Century, Proceedings European Business Information Conference, Lisbon, 18 March, 1998. V. Kochikar, The Knowledge Management Maturity Model – A Staged Framework for Leveraging Knowledge, Proceedings of the KM World 2000 Conference, September 2000.
168 C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review L.L. Lee, Knowledge Sharing Metrics for Large Organizations, in: Knowledge Management: Classic and Contemporary Works, W. Ives, B. Torrey and C. Gordon, eds, The MIT Press, Cambridge, MA, 2003. D. Leonard, Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation, Harvard Business School Press, Boston, MA, 1995. D. Leonard and S. Straus, “Putting Your Company’s Whole Brain to Work, Harvard Business Review on Knowledge Management. Boston: Harvard Business School Press, Boston, MA, 1998. S.H. Liao, J.C. Chang, S.C. Cheng and C.M. Kuo, Employee Relationship and Knowledge Sharing: A Case Study of a Taiwanese Finance and Securities Firm, Knowledge Management Research & Practice 2(1) (2004), 24–34. J. Liebowitz, ed., Knowledge Management Handbook, CRC Press, Boca Raton FL, 1999. J. Liebowitz and Y. Chen, Knowledge Sharing Proﬁciencies: The Key to Knowledge Management, in: Handbook on Knowledge Management 1: Knowledge Matters, C.W. Holsapple, ed., 2003, pp. 409–424. R. Maier, Knowledge Management Systems: Information and Communication Technologies for Knowledge Management, Springer, Berlin, 2001. Y. Malhorta, ed., Knowledge Management and Virtual Organizations, Idea Group Publishing, Hershey PA, 2000. Y. Malhorta, ed., Knowledge Management and Business Model Innovation, Idea Group Publishing, Hershey PA, 2001. K. Mertins, P. Heisig and J. Vorbek, eds, Knowledge Management: Best Practices in Europe, Springer, Heidelberg, 2001. W.L. Miller and L. Morris, Fourth Generation R&D: Managing Knowledge, Technology, and Innovation, Wiley, Hoboken NJ, 1999. M. Maybury, Knowledge Management at MITRE, in: Case Studies in Knowledge Management Practices. Leading With Knowledge: KM Practices in Global InfoTech Companies, M. Rao, ed., New Delhi: Tata McGraw-Hill, 2003. D. Morey, M. Maybury and B. Thuraisingham, eds, Knowledge Management: Classic and Contemporary Works, MIT Press, Cambridge, 2002. I. Nonaka, 1991, The Knowledge-Creating Company, Harvard Business Review on Knowledge Management, Harvard Business School Press, 1998. I. Nonaka and H. Takeuchi, The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, New York, 1995. I. Nonaka and T. Nishiguchi, eds, Knowledge Emergence: Social, Technical, and Evolutionary Dimensions of Knowledge Creation, Oxford University Press, 2001. I. Nonaka and V. Peltokorpi, Objectivity and Subjectivity in Knowledge Management: A Review of 20 Top Articles, Knowledge and Process Management 13(2) (2006), 73–82. D.E. O’Leary, Enterprise Knowledge Management, IEEE Computer 31(3) (March 1998), 54–61. J. Pfeffer and R.I. Sutton, The Knowing Doing Gap: How Smart Companies Turn Knowledge into Action, Harvard Business School Press, Cambridge MA, 2000. M. Polanyi, 1966, The Tacit Dimension, Doubleday & Company Inc, Reprinted 1983. K. Romhardt and G. Probst, Building Blocks of Knowledge Management – A Practical Approach, Input-Paper for the seminar: Knowledge Management and the European Union-Towards a European Knowledge Union, 12–14 May 1997. W.B. Rouse, Need to Know – Information, Knowledge, and Decision Making, IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews 32(4) (2002), 282–292. W.B. Rouse, A Theory of Enterprise Transformation, Systems Engineering 8(4) (2005), 279–295. W.B. Rouse and A.P. Sage, Information Technology and Knowledge Management, in: Chapter 30 in Handbook of Systems Engineering and Management, A.P. Sage and W.B. Rouse, eds, John Wiley and Sons, New York, 1999, pp. 1175–1209. R.L. Ruggles, ed., Knowledge Management Tools, Butterworth-Heinemann, Boston, 1997. R.L. Ruggles, “The State of the Notion: Knowledge Management in Practice” Special Issue on Knowledge and the Firm, California Management Review 40(3) (Spring 1998), 80–89. A.P. Sage, Systems Management for Information Technology and Software Engineering, John Wiley & Sons, Inc, New York, 1995. A.P. Sage and W.B. Rouse, Information Systems Frontiers in Knowledge Management, Information Systems Frontiers 1(3) (1999), 205–219. A.P. Sage, “Knowledge Management,” McGraw Hill Yearbook of Science and Technology, 2003, 210–212. C. Small and J. Tatalias, Knowledge Management Model Guides KM Process, The EDGE, The MITRE Corporation, Bedford, April 2000. T.K. Srikantaiah and M.E.D. Koenig, eds, Knowledge Management for the Information Professional, American Society for Information Science, Medford NJ, 2000. A.S. Thomas, The Wealth of Knowledge: Intellectual Capital and the Twenty-ﬁrst Century Organization, Doubleday, New York, 2000. M. Stankosky, ed., Creating the Discipline of Knowledge Management: The Latest in University Research, Butterworth- Heinemann, 2005. A. Styhre and T. Kailing, Knowledge Sharing in Organizations, Copenhagen Business School Press. Copenhagen, 2003.
C.T. Small and A.P. Sage / Knowledge management and knowledge sharing: A review 169 K.E. Sveiby, The New Organizational Wealth: Managing and Measuring Knowledge-Based Assets, Berret-Koehler Publications, San Francisco, 1997. A. Tiwana, The Essential Guide to Knowledge Management, Prentice Hall, Englewood Cliffs NJ, 2001. A. Tiwana, The Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Platforms, Prentice Hall, Englewood Cliffs NJ, 2002. G. Von Krogh, K. Ichijo and I. Nonaka, Enabling Knowledge Creation, Oxford University Press, Oxford, 2000. G. Von Krogh, J. Roos and D. Kleine, eds, Knowing in Firms: Understanding, Managing, and Measuring Knowledge, Sage Publications, London, 1998. K. Wiig, Knowledge Management Foundations, Schema Press, Arlington VA, 1993. K. Wiig, Knowledge Management, Schema Press, Arlington VA, 1994. K. Wiig, Knowledge Management Methods, Schema Press, Arlington VA, 1995. K.Y. Wong and E. Aspinwall, Knowledge Management Implementation Frameworks: A Review, Knowledge and Process Management 11(2) (2004), 93–104. Cynthia Taylor Small is the Associate Department Head of the Information Management Department at The MITRE Corporation. She received a BA from The College of William and Mary, a MS in Technology Management from American University, and a PhD in Information Technology from George Mason University. She has held numerous positions, providing system engineering and IT support, and knowledge management (KM) for a host of government agencies. She participates in a variety of academic, industry, and government forums, authoring articles and presentations in the area of knowledge management. Her research interests include knowledge engineering, knowledge sharing, knowledge governance, KM measurement, and complex adaptive systems. E-mail: email@example.com. Andrew P. Sage received the BSEE degree from the Citadel, the SMEE degree from MIT and the Ph.D. from Purdue, the latter in 1960. He received honorary Doctor of Engineering degrees from the University of Waterloo in 1987 and from Dalhousie University in 1997. He has been a faculty member at several universities including holding a named professorship and being the ﬁrst chair of the Systems Engineering Department at the University of Virginia. In 1984 he became First American Bank Professor of Information Technology and Engineering at George Mason University and the ﬁrst Dean of the School of Information Technology and Engineering. In May 1996, he was elected as Founding Dean Emeritus of the School and also was appointed a University Professor. He is an elected Fellow of the Institute of Electrical and Electronics Engineers, the American Association for the Advancement of Science, and the International Council on Systems Engineering. He is editor of the John Wiley textbookseries on Systems Engineering and Management, the INCOSE Wiley journal Systems Engineering and is coeditor of Information,Knowledge, and Systems Management. He edited the IEEE Transactions on Systems, Man, and Cybernetics from January 1972through December 1998, and also served a two year period as President of the IEEE SMC Society. In 1994 he received theDonald G. Fink Prize from the IEEE, and a Superior Public Service Award for his service on the CNA Corporation Boardof Trustees from the US Secretary of the Navy. In 2000, he received the Simon Ramo Medal from the IEEE in recognitionof his contributions to systems engineering and an IEEE Third Millennium Medal. In 2002, he received an Eta Kappa NuEminent Membership Award and the INCOSE Pioneer Award. He was elected to the National Academy of Engineering in2004 for contributions to the theory and practice of systems engineering and systems management. His interests includesystems engineering and management efforts in a variety of application areas including systems integration and architecting,reengineering, engineering economic systems, and sustainable development.