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Knowledge Management Strategies: A Handbook of Applied Technologies: Back To Publication Details...
Preface The Knowledge Management Strategies: A Handbook of applied technologies is the fifth book in the Knowledge and Learning Society Book Series. Three titles are already available in the bookstores:
This book is complementary and is published together with the 5th book of the series entitled:
For mid 2008, are also planned two more edited volumes which contribute further to our vision for the knowledge society.
Introduction Knowledge management is a buzz world of late 90’s. In an era of business transition, the effective management of knowledge is proposed as a strategy that exploits the organizational intangible assets. This fact has intrinsic market attractiveness and a great interest for practical guidelines for the implementation of knowledge management strategies. However, the term of knowledge management has been used to describe many different applications. In some cases the tag of ‘knowledge management product’ is attached to several software programs purely for marketing reasons. The motivation for this book was based on the fact that literature on knowledge management rarely concentrates on the practical aspect of KM. More over in the situations where a book discusses KM technologies this is based on a taxonomy which is difficult to align with real world situations. This book recognizes knowledge management as a complex socio-technical phenomenon where the basic social constructs such as person, team and organization require support from ICT applications. This is not only due to the complexity of the phenomenon but also due to the contextual nature of knowledge. The inevitable relation of knowledge and strategy formation seems to be taken for granted in most of approaches. From this perspective Knowledge Management is contextual phenomenon and its performance has to be secured through enormous effort of codifying strategies that deploy specific technologies. Figure one, provides an initial stage for analysis: Knowledge Management infrastructure within business organizations facilitates project teams that work towards the achievement of deliverables n given deadlines. Of course teams are not the only level of analysis. KM is recognized as a critical enabler of qualitative achievements in the organizational and inter-organizational level as well. The book intents to give answers to problems that business organizations face where they try to implement knowledge management. Mainly two critical issues are addressed:
The ultimate objective of the book is to provide practical guidelines for applied knowledge management through the discussion of specific technologies. Or, in another words, which components provide the basic KM infrastructure and how the selection of several technologies can be justified through specific Knowledge Management Strategies. The whole book is organized around the following pillars of the Knowledge Management research Agenda: ARTIFACT LEVEL • Managing Documents • Managing Metadata and Semantics • Managing Taxonomies INDIVIDUAL LEVEL • Constructing Yellow pages of experts • Managing individual profiles • Managing Tacit Knowledge TEAM LEVEL • Managing Workflows • Managing Discussion Forums • Exploiting Collaborative Work Systems • Managing Team Dynamics ORGANIZATIONAL LEVEL • Building Best Practices • Developing Knowledge Maps / Ontologies • Managing Competencies • Managing Organizational Memory INTER - ORGANIZATIONAL LEVEL • Managing inter-organizational network • Managing Projects • Future Technologies We are very happy since during the preparation of this edited book we launched also the International Journal of Technology Enhanced Learning, http://www.inderscience.com/ijtel. IJTEL fosters multidisciplinary discussion and research on technology enhanced learning (TEL) approaches at the individual, organisational, national and global levels. Its key objective is to be the leading scholarly scientific journal for all those interested in, researching and contributing to the technology enhanced learning episteme. For this reason, IJTEL delivers research articles, position papers, surveys and case studies aiming: • To provide a holistic and multidisciplinary discussion on technology enhanced learning research issues • To promote the international collaboration and exchange of ideas and know how on technology enhanced learning • To investigate strategies on how technology enhanced learning can promote sustainable development Our wonderful journey in the research and vision for the Knowledge Society has one more stop. In September 2008 [and in each forthcoming September], we organize the Athens World Summit on the Knowledge Society [for more info drop a mail to Lytras@ceid.upatras.gr]. The Athens World Summit on the Knowledge Society aims at becoming the leading forum for the dissemination of latest research on the intersection of Information and Communications technology (ICT) and any area of human activity including production, economy, interaction and culture. Athens World Summit on the Knowledge Society brings together:
The Underlying idea is to define, discuss and contribute to the overall agenda on how emerging technologies reshape the basic pillars of our societies towards a better world for all. This is why five general pillars provide the constitutional elements of the Summit:
The Athens World Summit on Knowledge Society event series provide a distinct, unique forum for cross-disciplinary fertilization of research, favouring the dissemination of research that is relevant to international research agendas as the EU FP7. Last but not least we invite you to read the just published special issue on Semantic based Knowledge Management special issue we developed for the IEEE Internet Computing Magazine Issue: Sept/Oct 2007, Guest Editors: John Davies, Miltiadis Lytras and Amit Sheth. We do believe that this edition contributes to the literature. We invite you to be part of the exciting Knowledge Management Research Community and we are really looking forward for your comments, ideas and suggestions for next editions. Structure/Edititng strategy/Synopsis of the bookWhen dealing with Knowledge Management it is really of no sense to trying be exhaustive. Not only because of the fast pace in technologies that support KM strategies but mostly due to the many different aspects of the domains. More over when you are trying to investigate the new insights of KM, like social networks, semantic web, then the mission becomes even more complex. This is why from the beginning we knew that our book should be selective and focused. In simple words we decided to develop a book with characteristics that would help reader to follow several different journeys through the contents. We also decided to open the book to big audiences. While we could pursue through our excellent contacts and great network of collaborators a publication aiming to promote the discipline, we decided that it would be most significant (from a value adding perspective) to develop a reference book. And this is what we made with the support of great contributors: A reference book for KM strategies providing an excellent overview of the emerging research agenda and the state of the art. Having already the experience of the edition of four edited books and getting feedback from 100s of researchers from all over the world, we decided to keep the same presentation strategy. We tried and we think that we really made it to develop a book that has three characteristics:
The last characteristic is a novelty of our book. Several times editions seem like a compilation of chapters but without an orientation to the reader. This is why every edited chapter is accompanied by a number of additional resources that increase the impact for the reader. In each chapter we follow a common didactic-learning approach:
At the end of each chapter there are some very interesting sections, where reader can spend many creative hours. More specifically the relevant sections are entitled:
Knowledge Management Strategies Underpinnings: Dynamic flows in business organizations In figure two, we depict two entities that are the main actors in projects within knowledge intensive organizations. The person who carries experiences, skills, knowledge, cognition and a learning capacity, which are realized in behavior and attitudes. The project team, which utilizes the team synergy in order to achieve the desired objectives, is a qualitative whole in a knowledge intensive organization. The concept of culture is also important here, since the concept of team is not a solid whole with distinct borders, but rather a dynamic formation. Shared meaning emerges through any action that is undertaken while working in a project. The simple interaction presented in figure 2 is not representative of practice. In knowledge intensive organizations, several individuals and a number of project teams interact, forming a spaghetti-like group of relationships. A kind of network is realized with the various nodes playing an important role that merits research investigation. The dynamic flows between these two entities are rarely explicit in nature. The dynamics of individual and team working together on a project formulate a contextual environment where information technology is used to facilitate the value exchanges. Four kinds of dynamic flows are depicted: Team Formation, Knowledge Flow, Behavioral Change and Learning. These “flows” are knowledge transformation mechanisms. The knowledge capacity of each person is in a continuing exchange with the environment of the individual, which can be the team or the organization. The Knowledge Flow relates to the characteristic of humans to constitute teams that share a common objective and thus facilitate the exchange of knowledge. In this context the critical question is the nature of knowledge. To this end, a number of knowledge category models (McAdam and McCreedy 1999) have been proposed. A number of characteristics of knowledge have been distinguished providing the dimensions for categorization. The traditional approach seems to be the selection of two characteristics and the justification of a two-dimensional matrix where the specified kinds of knowledge are presented. Such abstraction is easily understandable but perhaps is simplistic. In the literature a number of knowledge categories models can be identified. The model of Boisot (1987) recognizes two critical characteristics of knowledge: diffusion and codification. Proprietary, Personal, Public Knowledge as well as common sense are the four suggested types of knowledge. A criticism of this model is that the distinction of personal knowledge according whether it is un-codified and undiffused does not assume that this knowledge is not exploited. The person in its daily practice refers to this knowledge and acts according to specific context. Hahn and Subramani (2000) provide a very interesting approach that investigates a framework of Knowledge Management Systems using two basic dimensions: The locus of knowledge and the level of the a-priori structure. These two dimensions determine the boundaries for four quadrants, where several applications are positioned in order to support knowledge management. In each quadrant specific knowledge types are determined providing an overview of knowledge types that require specific support through ICTs. Nonaka and colleagues (Nonaka 1994), (Nonaka and Takeuchi 1995) promote the well-known distinction of tacit and explicit knowledge which seems to be a manifestation in knowledge management, since in its simplistic categorization describes the admission of hidden and revealed knowledge. The Learning Flow corresponds to the archetype of human behavior that through action and feedback promotes the understanding and adoption to the environment. The contextual character of learning is of critical importance. Individuals, teams and organizations have a learning capacity, which is not simply a cumulative result of individual contributions. A number of theories concerning learning have been identified for every context mentioned earlier. In an organizational context Argyris (Argyris 1976; Argyris and Schön 1978; Argyris 1991; Argyris 1993), proposes double loop learning theory, which pertains to learning to change underlying values and assumptions. Double loop theory is based upon a "theory of action" perspective outlined by Argyris & Schon (1974). This perspective examines reality from the point of view of human beings as actors. Changes in values, behavior, leadership, and helping others, are all part of, and informed by, the actors' theory of action. An important aspect of the theory is the distinction between an individual's espoused theory and their "theory-in-use" (what they actually do); bringing these two into congruence is a primary concern of double loop learning. Typically, interaction with others is necessary to identify the conflict. There are four basic steps in the action theory learning process: (1) discovery of espoused and theory-in-use, (2) invention of new meanings, (3) production of new actions, and (4) generalization of results. Double loop learning involves applying each of these steps to itself. In double loop learning, assumptions underlying current views are questioned and hypotheses about behavior tested publicly. The end result of double loop learning should be increased effectiveness in decision-making and better acceptance of failures and mistakes. At the individual level many learning theories investigate the phenomenon of learning. Two interesting approaches are provided by Bloom and Krathwohl (1984) and Shuell (1992). Bloom’s Taxonomy of Educational Goals and the concept of learning function describe the concept of educational objectives while Shuell promotes a value carrier. Lytras, Pouloudi & Poulymenakou ( 2002) through an integration of educational goals and learning functions propose 9 learning processes that potentially set the context of learning. At the team level a number of theories promote the role of group as a learning facilitator. Action learning (Watkins and Marsick 1993) (ARL-Inquiry 1996) can be defined as a process in which a group of people comes together more or less regularly to help each other to learn from their experience. Cooperative learning (Bossert 1988), (Kagan 1992) is a generic term for various small group interactive instructional procedures. Students work together on academic tasks in small groups to help themselves and their teammates learn together. In general, cooperative learning methods include: Three-step Interview, Roundtable, Focused Listing, Structured Problem-solving, Paired Annotations, Structured Learning Team Group Roles, Send-A-Problem, Value Line, Uncommon Commonalities, Team Expectations, Double Entry Journal and Guided Reciprocal Peer Questioning. The Team Formation is one more dynamic flow, which needs further investigation that goes beyond the scope of this paper. The coherence of the team requires flows that prove to the members the value of the integration. Bird (1989) and Hackman (1990) have identified five parameters that promote the effectiveness of a team. These are vision, values, processes, structure and perceived business performance. Finally Behavioral Change (Bandura 1977) enlightens the way in which individuals transform their behavior according to feedback they gain from participation in bigger social constructions. According to the behaviorists, learning can be defined as the relatively permanent change in behavior brought about as a result of experience or practice. In fact, the term "learning theory" is often associated with the behavioral view. The focus of the behavioral approach is on how the environment impacts overt behavior. The psychomotor domain is associated with overt behavior when writing instructional objectives. In the behavioral approach, we assume that the mind is a "black box" that we cannot see into. The only way we know what is going on in the mind, according to most behaviorists, is to look at overt behavior. The feedback loop that connects overt behavior to stimuli that activate the senses has to be studied extensively. The previous analysis sets a context through the admission that some patterns of relationships contextually describe knowledge transformations without taking into account the socio-technical nature of the phenomenon. In other words the relevance of KM applications to support these relationships is something that needs justification. If we expand the basic construct by adding the organizational level, then a richer picture of relationships is revealed. In figure 3, the person, the team and the organization define dynamic flows that are of critical importance in knowledge intensive organizations. The Learning and Knowledge flow link together person(s) and organization as well as team(s) and organization. Of course team-to-team linkages can be defined as well as person-to-person relationships (these are not depicted in figure 3 for simplicity). These patterns of relationships imply specific scenarios of knowledge exploitation. The next step in our research approach is focusing on the socio-technical dimension of the phenomenon of knowledge transformations and dynamic flows. Knowledge Management Support Frameworks The justification of an application as a Knowledge Management (KM) one has to be based on a context. In the KM literature several ways for categorizing KM applications can be found (Nissen et al. 2000), (Binney 2001), (Lee and Hong 2002). Lee & Hong (2002) link IT applications to a 4 stages Knowledge Life Cycle. Binney (2001) recognizes six elements on the KM spectrum (Transactional, Analytical, Asset Management, Process, Developmental, Innovation & Creation) and corresponds various Knowledge Management Applications and Enabling Technologies to each element. A common approach in Knowledge Management is the analysis of the phenomenon from two perspectives. The process-centered and the product-centered approach (Hansen et al. 1988), (Koehn and Abecker 1997). Woods (1998) promotes a categorization of applications that support these two aspects of Knowledge Management, using the two basic approaches of knowledge management and mapping several KM applications in a two dimensional map. Figure 4, provides an overview of the suggested positioning. Applications include File Management Systems, Shared files, full text retrieval, Push Technology, Real Time Messaging, E-mail, Semantic Analysis, Intranet, Knowledge maps, Structured Document Repositories, White-boarding, Automatic Profiling, Net Conferencing and Discussion Groups. The depicted allocation of applications seems to be very interesting since it gives an overview of technologies and two coordinates can be assigned to each position. A critical question concerning positioning is which is the scale in each dimension? What is the maximum considered abstraction of a knowledge product? Are there any ingredients that incrementally are realized through the employment of specific technological components? And in the knowledge as a process dimension, despite the simplification of emphasis on knowledge transfer, we have to answer the critical question concerning scaling. In this approach several other contributions provide insight. Especially in the case of knowledge as a process, the relation of applications to several knowledge processes is a common approach. Nissen, Kamel and Sengupta (2000), provide an interesting approach concerning this aspect. They distinguish three levels of Knowledge Management, namely organizational level KM, Group Level and Individual Level. In Figure 5 we present their classification, which pays special attention to the distinction of the three levels. Their presentation is based on an amalgamated KM model which is a result of the integration of four others models (Nissen, Despres & Chaveul, Gartner Group, Davenport & Prusak). This model recognizes six knowledge management processes: Create, Organize, Formalize, Distribute, Apply, and Evolve. At the organizational level, Nissen, Kamel & Sengupta provide a number of applications and practices that seem to support each specified KM process. At the stage of Knowledge Creation, they depict the importance of Business Intelligence, the R&D practices, the Benchmarking approach and Data Mining as well as Artificial Intelligence. In the subsequent phases they emphasize the importance of knowledge maps, semantics networks, data warehouses and reports. It is obvious that the distribution process, where a number of systems and practices are recognized, has a special role in the whole continuum. At the Group and the Individual level the depicted practices and systems present an accumulation in the Organize and Distribute phase. It seems that the key issue in KM support is the distribution of knowledge. But the critical question is how can the distribution of knowledge be secured if in a previous stage the extensive codification of knowledge is not promoted? Moreover this classification does not pay any attention to learning capacity. All these applications do not stand in any context (team, individual, organization) just for facilitating the daily workload. Knowledge management from this perspective is weak if we do not reveal its capability to support learning initiatives that increase the capacity for effective action. Moreover the learning dimension is underlying in any system since if their users will not be able to align their behavior and attitudes to the requirements of the systems then their usage would be limited. Unfortunately the intangible nature of knowledge makes the ROI analysis of knowledge management systems a difficult task. This process-oriented approach provides an insight to the phenomenon of knowledge management and in the environment of knowledge intensive organization can justify implementations. A similar approach is provided by Lee & Hong, ( 2002) who recognize a four-stage KM life cycle and they juxtapose specific IT applications to each stage. Figure 7 provides the overview of their proposition. In this approach also, the learning dimension of knowledge management is disregarded. This is really a very weak point in the models if we consider Knowledge Management as a sequential indication of stages. The knowledge infrastructure in an organization must not be considered using a librarian perspective of knowledge management. In this dimension the empowerment of learning capability in an organization is a continuing process where specific technologies must secure the human resources management. Drucker (1992) stated that: “it is safe to assume that anyone with any knowledge will have to acquire new knowledge every four or five years or become obsolete”. An interesting categorization of KM technologies is provided by Binney (2001). In this mapping in the Developmental stage of the spectrum, a number of Knowledge Management applications are recognized as of critical importance and some enabling technologies are depicted. In the next section we provide the basic notion for the categories of KM technologies that will be discussed in the relevant chapters of the proposed book. Knowledge exploitation as a dynamic flow requires the development of extensive practical capabilities in the direction of building competences. All the depicted dynamic flows in previous sections do not stand for just descriptive reasons. The revelation of the underlying logic forces the extensive analysis of infrastructures that support the realization of these flows. One of the most important obstacles in knowledge management is the persistence to descriptive models that unfortunately provide only formalization with limited practical implications. In this direction the proposed book expands further the ideas and the research presented in two published papers on the Journal of Knowledge Management. Knowledge Management Strategy and Technology Convergence • Students enrolling in KM courses • Special Interest Groups on KM: e.g. Associations, Public Bodies etc • Adult Trainers • Educational Policy Makers (with special interest in KM ) Respectively in the Business Market five more sub segments are distinguished: • Managers (interested in implementing KM) • KM Specialists • Knowledge Officers • Human Resources Management officers • Business Consultants • IT managers The specific added value we see in this book is by facilitating the creation of the ubiquitous business intelligence space. Knowledge Management, Learning Technologies and Semantic Web in the last five years have gained a significant interest in the Information Technology Research Community. The integration of these fields will be create a significant business interest for specific products and services, some of which are discussed in this book. The contribution of this book to the literature of IT is significant. Information Technologies are analyzed as Socio-Technical Systems. Business Intelligence based on advanced Knowledge Management Strategies that guide the deployment of technologies and infrastructures provides the context for the exploitation: Learning and Knowledge jointly formulate a challenging landscape for the deployment of information technology since their performance is directly related to behavioral-soft issues. |
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