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Doktorarbeit / Dissertation, 2004
154 Seiten, Note: A
This thesis examines the relationship of Knowledge Management (KM) and Information Technology (IT) using a holistic approach and view. Therefore, the first chapter presents definitions of knowledge and KM, discusses related fields to KM and knowledge types, argues what KM activities can be supported by IT, examines areas of IT related to KM, defines the focus of this thesis, and presents interesting artifacts. Generally speaking, KM is centered on becoming or staying competitive and IT is able to support such initiatives. As areas that require flexibility, creativity, and learning are especially in need of KM, this thesis concentrates on them.
Since there is hardly any complete description of the goals KM tries to achieve and the problems it addresses, the next chapter analyzes the goals of KM, problems to be addressed by KM-systems, goals and problems of a university, problems of (existing) KM-systems, and, finally, the requirements of a KM-system that supports a KM initiative that addresses the mentioned problems and does not suffer from the described problems. Furthermore, the identified requirements are supplemented by important non-functional requirements, as these are missing in the list of goals and problems.
Following the identification of requirements for KM-systems, chapter3 discusses important preconditions and foundations for KM in general and KM-systems in specific. As a “complete” KM-system can only be part of a “complete” and holistic KM initiative, this examination presents indispensable issues for such KM initiatives and discusses the importance and relevance of each topic.
The next chapter presents three existing solutions, namely CYMANTIX.NET, the Oracle solution, and the Lotus/IBM solution (the Lotus Discovery System plus additional software). While CYMANTIX.NET does not offer as many features and functions as the other examples, it provides interesting ones. On the other hand, the Oracle solution also lacks many features but provides a solid technological foundation to build a KM-system and the Lotus/IBM solution provides many relevant features and functions and, thus, is capable of supporting limited KM initiatives. Finally, the chapter examines to what extent the three solutions address the requirements identified and what is missing.
Next follows the main chapter of this thesis describing the proposed IT solution in the context of a holistic KM initiative. Therefore, it starts with a discussion of principles such as participatory design, etc. that have to be adhered to when designing, implementing, and introducing the proposed KM-system. Then, the proposed KM-system is presented consisting of three major building blocks, namely the central user interface, the virtual information pool, and automation as well as further aspects. Furthermore, the chapter examines the relationship of eLearning and KM with regard to the proposed KM-system, discusses implementation issues, and ends with an evaluation of the proposed solution.
The concluding chapter summarizes this thesis and stresses the holistic point of view that is combined with proposing a “complete” IT system supporting KM. Furthermore, it discusses the pros and cons of this approach and the results of the evaluation. Finally, it presents areas that need further research and what the future holds for KM.
Diese Dissertation untersucht die Zusammenhänge zwischen Wissensmanagement (WM) und Informationstechnologie (IT) unter Verwendung eines gesamtheitlichen Ansatzes und einer umfassenden Sichtweise. Daher werden im ersten Kapitel Definitionen von Wissen und Wissensmanagement vorgestellt. Weiters werden für WM relevante Forschungsfelder, Technologien, Klassifizierungen von Wissen, der Fokus dieser Arbeit, wichtige Artefakte sowie der Umfang, in dem IT WM prinzipiell unterstützen kann, diskutiert. Generell soll WM Firmen wettbewerbsfähiger machen und diese Arbeit untersucht, in welchen Bereichen und wie IT das unterstützen kann. Der Schwerpunkt liegt dabei auf Bereichen, die Flexibilität, Kreativität und Lernen voraussetzen, da WM hier den größten Erfolg verspricht.
Da es kaum eine komplette Beschreibung aller Ziele des WM und der Probleme, die es zu lösen versucht, gibt, enthält das nächste Kapitel eine Analyse der Ziele von WM, die Probleme die WM zu lösen versucht, die WM relevanten Ziele und Probleme einer Universität, bekannte Probleme von existierenden WM-Systemen und die Anforderungen, die sich aus dieser Analyse für ein WM-System ergeben. Zusätzlich werden auch so genannte nicht funktionale Anforderungen eines WM-Systems beschrieben.
Nach dieser Analyse werden im Kapitel3 Vorbedingungen und Grundlagen von WM im Allgemeinen und WM-Systemen im Speziellen untersucht. Da ein “komplettes” WM-System nur Teil einer “kompletten” und ganzheitlichen WM-Initiative sein kann, werden dabei nicht nur die für WM-Systeme wichtigen Punkte sondern auch die wichtigsten Punkte für WM selbst diskutiert.
Im folgenden Kapitel werden drei bestehende Lösungen präsentiert (CYMANTIX.NET, die Lösung von Oracle und die Lösung von Lotus/IBM – das Discovery System mit Erweiterungen). Obwohl CYMANTIX.NET relativ wenige Funktionalitäten anbietet, stellt es einen interessanten Ansatz dar. Die Lösung von Oracle, andererseits, bietet ebenfalls nur relativ wenige Funktionen direkt an, stellt aber eine gute technische Basis dar, um ein WM-System darauf aufzubauen. Die Lösung von Lotus/IBM schließlich offeriert eine Reihe von WM-relevanten Funktionalitäten und ist in der Lage, eingeschränkte WM-Initiativen zu unterstützen. Insgesamt untersucht das Kapitel das Ausmaßder Unterstützung von WM durch drei exemplarische Systeme und analysiert in welchen Bereichen es noch Unzulänglichkeiten gibt.
Danach kommt das Hauptkapitel dieser Dissertation, dass die vorgeschlagene IT Lösung für eine “komplette” und ganzheitliche WM-Initiative beschreibt. Zuerst wird diskutiert welche Prinzipien, wie etwa “Participatory Design”, während des Designs, der Implementierung und der Einführung eines solchen Systems berücksichtigt werden müssen. Danach werden die drei Grundpfeiler des WM-Systems vorgestellt, also der virtuelle Informationspool, das einheitliche Benutzerinterface und der Automationsaspekt, sowie die darüber hinausgehenden Teile der Lösung präsentiert. Im Weiteren untersucht das Kapitel den Zusammenhang zwischen eLearning und WM mit besonderer Berücksichtigung, was das für die vorgeschlagene Lösung bedeutet. Weiters werden noch Implementierungsaspekte diskutiert und im letzten Abschnitt wird die Lösung in Bezug auf die zu erreichenden Ziele und zu lösenden Probleme evaluiert.
Im abschließenden Kapitel wird diese Arbeit noch einmal kurz zusammengefasst und der Aspekt der gesamtheitlichen Betrachtung zusammen mit dem Vorschlag eines “kompletten” IT Systems zur Unterstützung von WM hervorgehoben. Weiters diskutiert es die Vor- und Nachteile der Lösung und die Ergebnisse der Evaluation. Abschließend wird noch kurz aufgezeigt, in welchen Bereichen weitere Forschungen sinnvoll und notwendig sind, und was die Zukunft des Wissensmanagements bringen könnte.
It is a pleasure for me to thank the many people who supported me in writing this thesis, either by sharing their knowledge or by reviewing drafts and providing useful feedback.
I am particularly grateful to my supervisors, Univ. Doz. Dr. GerhardBudin and o. Univ. Prof. Dr. AMinTjoa, for keeping me focused on the goal in hand and for their constant support and encouragement. Special thanks also go to ao. Univ. Prof. Dr. JürgenDorn for his support and help in early phases of my thesis.
Sincere thanks go to three friends of mine, namely Angela Dickinson, Dr. Johann Ortner, and Mark-René Uchida. Without their help, support, and encouragement I could never have finished this thesis.
I also owe thanks to Robert Baumgartner of Oracle Austria for providing insight into the Oracle Portal / KM solution and to Markus Wieland of RZB (one of Austria’s larger banks) for his help and support on the Lotus solution.
Special acknowledgements go to my former employer, CYMANTIX Software GmbH, and all the wonderful people who worked there. They helped me start this thesis and supported me while I was employed by the company.
Many others provided feedback and help during the development of this thesis, most notably the members of the group for IT and KM of the PWM (Plattform Wissensmanagement—www.pwm.at an Austrian [online] platform for KM) platform.
Of course, all brand and product names are trademarks or registered trademarks of their respective holders, even when I fail to acknowledge them in the text.
Finally, I would like to express my deepest thanks to my parents and family who supported and encouraged me throughout my thesis as they have been doing since I can remember.
Nowadays Knowledge Management (KM) is a widely discussed topic. It is safe to assume that most readers will have heard of it, while others surely have already gained some experience in the field. Nevertheless, since KM is not an exact science, it remains necessary to present the definitions and views this thesis is based on and therefore begins with discussing, why organizations should be interested in KM at all.
Generally speaking, KM enables organizations to utilize their inherent knowledge in the most effective way. As we are moving more and more towards an economy, where information and knowledge are more important than traditional assets such as land and capital the importance of dealing with this topic is easily recognized. This is further illustrated by quoting Prusak:
Those of us who are attempting to do research in the areas of sustainable competitive advantage have come to the conclusion that the only thing that gives an organization a competitive edge – the only thing that is sustainable – is what it knows, how it uses what it knows, and how fast it can know something new.
This sentence from Prusak stresses the importance of managing available knowledge, while at the same time emphasizing the importance of the generation of new knowledge (the goal of the learning organization). A short discussion of this important relationship is given in section3.5 and in section1.2 defining the term Knowledge Management.
As the importance of KM was quickly recognized when the topic first gained popularity in the mid-1990s, there are already numerous reports of successful and failed initiatives available that provide important insight into the issues involved. Many of the early – primarily IT-driven – initiatives centered on the introduction of IT-based systems (e.g. Intranets) and failed to live up to the (high) expectations. Despite these failures, a recent overview paper by Harris at the Gartner Group shows that the general idea of KM is still felt to be valid and of utmost importance. Most researchers and practitioners now agree that a holistic approach is required for successful KM.
Since this thesis is about KM & Information Technology (IT), two papers by Alavi and Leidner and provide an excellent overview of these topics. They summarize major relevant publications and discuss the relationships between knowledge, Knowledge Management, and Knowledge Management Systems. Equally important is the fact that these two authors have an economics background, the most important driving force behind KM (as a management discipline).
Despite the fact that scholars have devoted vast amount of time to the subject, there is still no generally accepted definition of knowledge. Therefore, I would like to quote Grey who presents a rather pragmatic definition:
Context-relevant, validated information clusters that emerge when people somehow deal/interact with information elements/people.
As we can see from this definition (and from many others in the field), knowledge is seen primarily as validated information. Most researchers, nevertheless, agree that knowledge is personalized (individual) information and, thus, cannot be stored electronically.
Of course, philosophers like Polanyi or Popper (to name two of the more recent scholars) already gave great consideration to knowledge. Although there are quite intriguing definitions to be found, no single, generally accepted definition exists.
Alavi and Leidner provide a good overview of the different proposed definitions for knowledge in the context of KM (see). They summarize attempts to differentiate data, information, and knowledge in a hierarchical form that come primarily from those with an IT background. In this view, data items are just raw numbers and facts, information is processed data and knowledge is authenticated information. While this remains a classical approach, more recently a researcher named Tuomi has even proposed an inverse hierarchy (with information being the result of knowledge). Although these definitions provide limited insight to understand the nature of knowledge, they are actually of minor importance for most KM initiatives, since they only deal with knowledge that can be made explicit. Different types of knowledge are discussed in section1.4.
Many researchers are not satisfied with the hierarchical definition of knowledge and have proposed a number of alternative definitions. Once more, Alavi and Leidner provide a good summary of the most important alternatives: “Knowledge may be viewed from several perspectives (1) a state of mind, (2) an object, (3) a process, (4) a condition of having access to information, or (5) a capability.” These different perspectives give rise to different views of KM and consequently to different strategies needing different systems to support KM.
What all these definitions result in (borrowing heavily from Alavi and Leidner): Knowledge is personalized and needs to be expressed in such a manner as to be interpretable by the receiver. Furthermore, hoards of information are of no value for themselves; they only become useful if processed (possibly after some transformations) in the mind of an individual.
As there is no clear definition of knowledge, the futility of providing an exact definition of KM is obvious. Therefore, I would, again, like to cite Grey, who offers a pragmatic view of Knowledge Management:
Broad-based discipline: mine (analyze) data; capture, create, store, catalog, validate, transform, and disseminate/share information; capture, emerge, store, catalog, and disseminate/share knowledge.
In other words, KM is about dealing with knowledge, possibly in the form of data or information. The consequence of such a general definition is, that it, thus, encompasses a wide range of issues of even whole research “fields” and, thus, one could say: KM is either everything or nothing at all. This means that KM needs to build upon different research areas and needs to incorporate the results of many fields ranging from IT/technology to psychology.
Although I am going to discuss related fields in section1.3 (including certain classes of IT systems), it is of utmost importance to realize that KM is not about IT tools. Indeed, some KM initiatives might not even need any IT support (or tools) at all.
On the other hand, most organizations have already incorporated IT systems into their normal work procedures. In practice, the introduction of KM will almost always necessitate a change in working methods, and any IT system will have to be adapted accordingly. At the same time, even organizations that are not using IT systems in their daily work might well be planning to do so in the near future.
Generally speaking, (nearly) every organization of moderate size and above uses or plans to introduce computers, at least in parts of the organization (e.g. office and management). Therefore, it can be assumed, that IT will have to be considered in the planning of a “complete” and holistic KM initiative in virtually any organization above a certain size (the actual number varying greatly due to cultural and organizational differences).
Thus, one can easily conclude, that although IT is not a necessity for KM, it needs to be considered in most realistic KM initiatives. The greater the extent of the KM initiative, the greater the need for a sophisticated KM-system supporting all relevant aspects seems to prove true.
While the definition given above by Grey is accepted at large, some researchers have a somewhat different view of KM. McElroy, for example, argues that crucial parts are missing in existing definitions of KM and calls the result “first-generation KM”. He criticizes them as being: “All about getting the right information to the right people at the right time” based on the assumption that the required knowledge already exists. His line of thought continues with the claim that “second-generation KM” has to include aspects of knowledge creation. Thus, he calls for the integration of organizational learning and the theory of complexity to produce a new KM built on a sounder foundation.
Although McElroy stresses the differences between his own and previous approaches, in fact, his view does not really differ to the one held by Grey. While there are some discrepancies (especially on what other fields have to be incorporated in KM), both acknowledge the importance of creating new knowledge in a holistic approach.
Consequently, as Grey’s definition is more in line with the KM mainstream, this thesis adheres primarily to this definition. However, it also includes and indeed looks at other potentially relevant issues, omitted by these definitions. Furthermore, it contains a discussion of the relations between KM and the learning organization in section3.5.
Of course, there are numerous other definitions of KM that stress certain views and are based on different assumptions. However, for the purposes of this thesis, only one further approach will be considered, namely Sveiby, one of the most influential pioneers in the field. He is advocating the importance of maximizing the ability of an organization’s members to create new knowledge. At the same time, he proposes to use the concept of intellectual capital to have some measurements of the current state and changes in intangible assets. Let me conclude this paragraph by quoting one of his “definitions” of KM: “To me Knowledge Management is: The Art of Creating Value from Intangible Assets”.
As we have already determined that KM is a broad-based discipline, it comes as no surprise that many related fields exist. In fact, there are so many that I deem it necessary to concentrate on discussing only the most important ones.
Certain classes of information management systems like “Management Information Systems” (MIS) or “Decision Support Systems” (DSS), to just name two of the most common ones, claim to be KM-systems. Then again, it is sometimes argued that KM is merely a part of organizational development, though this seems to be a rather limited view. Cognitive Research in turn emerges as a potentially even more interesting field that may provide the answer to the phenomenon of human mind and finally allow an exact definition of knowledge.
This illustrates the potential importance of relationships between KM and other fields mentioned, such as Cognitive Research. Therefore, these will now be discussed in brief.
There is a number of IT systems that deal with data and information like Database Management Systems (DBMS) or Information Management Systems (IMS). Indeed, IMS is often, although by no means exclusively, used as a general term for a class of similar systems like Management Information Systems (MIS), Decision Support Systems (DSS), or Expert Systems (ES).
While these systems are capable of providing effective, but limited support for KM by helping to manage information (or even work with a “knowledge base” in the case of ES), their focus is too narrow for them to be classified as KM-systems. These systems were not designed with KM in mind, but were instead developed to address more “conventional” problems and tasks, like providing current sales figures or expenditures, to present two examples illustrating the fundamental difference.
Their claims have even lead to misconceptions as to what KM really is and are partly responsible for the failure of early KM initiatives (for example described in McElroy). They represent a valuable class of technologies and ideas that need to be incorporated in a KM-system. Although these systems and technologies alone do not constitute a KM-system, they can and should form part of one.
Although I do not claim to be an expert in the field of organizational development (OD), it is obvious that both KM and the concept of the learning organization are often claimed to be merely a part of this field. While I cannot confirm these claims in the case of the learning organization (a concept most vividly promoted by Senge), in my opinion, they are definitely incorrect in the case of KM (particularly when consideration is given to the work being carried out in cognitive research). Of course, there will always be overlapping areas: Some approaches to KM will only involve the application of organizational development measures, while others will encompass a wider range of issues.
The learning organization is heavily influenced by system dynamics (see a webpage of the Forrester group at MIT for an introduction) and the work by Argyris on organizational learning. Its primary goal is to provide tools and methods to help organizations to become learning organizations that give their members the freedom and encouragement they need to learn. This ultimately encourages the creation of knowledge.
As McElroy maintains that creating knowledge is an integral part of his “second-generation KM”, he consequently argues for the inclusion of organizational learning in KM initiatives. While KM obviously needs to take this into account, a learning organization has a slightly different focus. While both want to foster knowledge creation, KM is more concerned with the capture and re-use of existing knowledge, and can thus at times inhibit the goals of the learning organization itself.
Summing up, this section demonstrates some of the differences and overlaps that exist between the fields. While some KM definitions definitely call for placing KM in the area of organizational development, there are aspects, which simply do not fit.
The fields of cognitive research and radical constructivism deal with the issues of the consciousness and the human mind. No valid and sound definition of knowledge will really be possible until a sound theory of knowledge has been developed in one of these two fields. To date, no such theory has established itself, although radical constructivism provides an interesting way of approaching the problem. It is based on the work of von Foerster, Maturana and Varela, Roth, von Glaserfeld, etc. and primarily proposes a self-referential nature of the human mind, thus raising doubts on the concept of objective knowledge, as the human senses do not reflect the true nature of the environment. Further information can be found, for example, in Maturana (describing his work on autopoiesis – which roughly means self-making) and Schmidt.
This approach seems similar to that of System Dynamics (although I would consider System Dynamics to be a more pragmatic approach and radical constructivism to be a well founded way of dealing with consciousness and knowledge). Since these are very challenging topics and I do not profess to be an expert in these fields, I do not want to extend this discussion any further. Instead, I would like to conclude by arguing that there is no (and probably never will be a) generally accepted concept for the phenomena of consciousness and knowledge in this field either.
The previous sections might make the reader wonder, whether KM really is something new or how it differs from more traditional approaches. At the same time, they raise the question of whether KM-systems are also something new or if they are just more elaborate (“complete”) MIS/DSS/ES systems.
In my opinion, KM is something new as it deals explicitly with knowledge (one of the major problems in this discipline). KM-systems as such are also something new as they offer more KM oriented functions than traditional systems.
A more rigorous discussion of this topic can be found in a paper by Spiegler, which discusses the differences between some of the fields mentioned above and KM (-systems). This paper explains why KM is a new idea and not simply a recycled concept. This subject is also handled in the research carried out by Essers and Schreinemakers in which they state, that simply establishing a strict referential distinction between knowledge and information will not automatically lead to a safe delineation between IM and KM. Instead, they argue that KM has different objectives and point out the dangers of a control-oriented approach, as this could turn into ’mind-control’.
After arguing the distinctions between KM and some close research fields, it is important to provide differentiations between KM and other management activities in general. A number of papers like that of Rollet or Romhardt (centered on organizational development and denying that other approaches classify as KM) deal with this important issue. Other papers, e.g. Anklam, show that people use the label KM for activities not really contributing to the field (the mentioned paper talks about technical communications and the changes since the beginning of the discipline).
Since no generally accepted definition of knowledge exists, the same holds true for KM. Nevertheless, many activities and systems claim to be (a) KM (-system) making it difficult to find really relevant and important contributions while (b) at the same time are responsible for “KM” failures.
Summing up, this section presents references to papers discussing the differences between KM and other fields in general as well as the boundaries of KM. It, furthermore, demonstrates the importance of well-founded boundaries to activities just claiming to be KM (a difficult task for a field lacking an exact definition).
While the differences between KM and certain IT systems (MIS/DSS/...) have already been presented, the general relationship between IT and KM still needs to be discussed in more detail. Section1.2.1 already established that KM is not about IT and, in principal, is possible without it. At the same time, this section argues that IT needs to be considered in the planning of most “complete” and holistic KM initiatives.
Consequently, IT supports and – to a certain degree – enables KM (a relationship similar to the support of other management activities by MIS/DSS/ESS...systems). Therefore, I am calling IT systems that support Knowledge Management, KM-systems (smaller systems might be called KM tools). A similar definition is provided by Alavi and Leidner in a paper (see) that also contains an overview of the possibilities of IT to support KM. As a result of the fact that IT is “just” supporting KM, computer science plays no role in the difficult problem of defining KM itself.
Note, however, that while the term KM-system is often used for IT systems as in this thesis, other authors refer to the result of a KM initiative, including, for example, organizational and IT changes, when using the term KM-system. This thesis, nevertheless, uses the narrow definition given in the last paragraph, and I hope that not too much confusion arises from this fact.
Wong and Aspinwall recently published a paper discussing the relationship of KM and IT and come to the conclusion that they are not totally equivalent as KM consists of technological, technical, and social issues. While this is true for many cases, especially those considered in this thesis, KM is possible without any help from IT, as it can consist solely of social and organizational changes.
As many proposed classification systems for knowledge exist, the most commonly used one is presented in this section (namely tacit/explicit sometimes extended to tacit/implicit/explicit knowledge) together with a brief coverage of one of the numerous alternatives (namely embedded versus embodied knowledge).
Since my thesis is centered on the relationship of KM and IT, the most important distinction is to be made between knowledge, that is already available as data and information, the additional amount of knowledge that can be made explicit, and other resources that may enable people to gain new knowledge and insights.
In the field of KM, the most commonly used classification of knowledge is that into tacit (to make matters worse, sometimes called implicit) and explicit knowledge. While explicit knowledge is a generally accepted term for phrased knowledge like a formula, tacit knowledge is not defined so clearly. Definitions can be found, for example, in Nonaka and Takeuchi or, presenting a more theoretical point of view, in Dienes and Perner (containing a definition of the differences between implicit and explicit knowledge).
The term tacit knowledge was first mentioned by Michael Polanyi who wrote in 1966 in his book The Tacit Dimension (p.4) “We can know more than we can tell.”. Summing up, Polanyi views tacit knowledge as a combination of bodily experience and practice.
Nonaka and Takeuchi make heavy use of the term tacit knowledge and are “responsible” for the common usage and “definition” in the field of KM. Although explicitly citing Polanyi they use the term with a wider meaning by including cultural aspects (like internalized judgments, norms, and ideals). While they do not rigorously define the term, one can find examples of what Nonaka views as tacit knowledge in his 1994 paper: A Dynamic Theory of Organizational Knowledge Creation.
The following paragraphs discuss the different meanings of the term tacit knowledge as used by Polanyi or Nonaka. The arguments are based on information obtained in discussions with my former colleague Dr. Johann Ortner.
In Nonaka’s research (for example, see Nonaka and Takeuchi) the most relevant arguments are presented with regard to the Kao Corporation. It results to three important principles/goals that are deeply rooted in Zen Buddhism (also presented as a major difference between Japanese and Western culture) and form the heart of the Kao Corporation:
- Serve (be of value to) the customer
- All people are equal
- The search for truth and wisdom
Nonaka says, These philosophical principles form the tacit knowledge base for Kao. This tacit knowledge base guides the behavior of Kaos employees and serves as the key driver for its unique corporate culture. Thus, Nonaka discusses internalized norms that can be made explicit and ideals when mentioning the term tacit knowledge.
Polanyi, on the other hand, is talking about the way higher life forms (cats, dogs, birds, etc. as well as human beings) experience the environment. More specifically, how entities (like an apple) are treated as a whole instead of just a number of frequencies of light registered in the eye. The background of his research is based on empirical as well as Gestalt psychology. In his opinion, the knowledge to experience such entities as a whole is bodily knowledge, inaccessible to the linguistic and rational thought. Instead, it is the reference base for the meaning of certain words. All these facts together are the background for his often-quoted “definition” of tacit knowledge (he did not really concentrate on this topic in his research).
Nonaka, on the other hand, views tacit knowledge as internalized necessities, challenges, and normative pressure that coerce people into being creative. Although often writing of bodily experience, all mentioned examples represent cultural knowledge and are not included by the original “definition” of Polanyi.
It is easy to see, consequently, that internalized knowledge about the environment that is shared with other higher life forms like cats and dogs is fundamentally different from internalized culture and, thus, there is a fundamental difference in the meaning of the term tacit knowledge as used by Nonaka compared to Polanyi.
Thus, I conclude that while Nonaka (and thus many KM researchers) claims to build upon the term of Polanyi he actually operates with a vague term “invented” by him. This is not necessarily a problem but has to be taken into account when reading about tacit knowledge in KM literature, especially when both Polanyi and Nonaka are being cited as references.
Considering the “definition” of tacit knowledge that is “vague” at best, it becomes obvious that the simple differentiation between tacit (or implicit) and explicit knowledge has serious shortcomings. From my point of view, the more recently proposed extension of the tacit/explicit model to additionally contain implicit knowledge (e.g., KMCI group or Nickols) seems more appropriate.
Let me present definitions of these three terms in the new model:
tacit This is knowledge we cannot tell. It cannot be expressed (therefore, sharing/transferring is very difficult).
implicit This is knowledge that we know and can tell if pressed to formulate it (but not available in linguistic terms in the mind).
explicit This is knowledge formulated in linguistic terms and available in the mind, thus being the only term with a widely accepted and sound definition.
Consequently, one has two options: Either to use the proposed tacit/implicit/explicit classification or to deal with tacit knowledge that is divided into expressible and inexpressible knowledge.
Please note, that the tacit/implicit/explicit model (as already argued in the section on the tacit/explicit model) is by no means sound from a philosophical or theoretical point of view. While this model is better suited for my thesis, the used concept of tacit knowledge remains different from the original definition given by Polanyi. In my opinion, this model seems to be more appropriate for KM than the more commonly used tacit/explicit differentiation. Thus, I conclude that it should be the focus of further research.
As the tacit/implicit/explicit classification of knowledge provides more clearly defined terms, I am going to use it in this thesis henceforth. Nevertheless, the popularity of the wider definition of tacit knowledge will make it necessary to sometimes use the terms implicit and tacit interchangeably. Consequently, there will be a remark or footnote in such a case.
Following the elaborate discussion of tacit, implicit, and explicit knowledge, I would like to present the embedded versus embodied differentiation in brief, to give one additional example of the numerous classifications of knowledge. Embedded knowledge is “within” an organization, while embodied knowledge is inside one member/person (an individual). It is easy to recognize that this model provides a different point of view and, thus, helps to gain a deeper understanding of the phenomenon knowledge.
Although this is very interesting for KM in general (like the many other proposed differentiations), its importance for (IT) KM-systems is rather low. Therefore, I am not going to discuss this (or any other) classification in more detail.
Up to now, this chapter focused on KM itself and on the possible role of IT in general. Therefore, a discussion what parts and processes of KM can be executed or supported by IT and in what areas this is not possible is still missing. While this issue is often mentioned in KM literature, in-depth discussions like that of Johannessen et al. are rare.
The two most important issues IT is incapable to manage are, on the one hand, cultural and organizational issues and, on the other hand, tacit knowledge. These topics are of utmost importance for any KM initiative and need to be addressed by non-IT means. Nevertheless, IT can provide some limited capabilities to support tacit knowledge transfer (by allowing people to communicate, for example, with video conference capabilities). Furthermore, existing IT systems will need to be adapted and newly introduced ones customized in accordance with necessary cultural and organizational changes. Consequently, it is possible to argue that IT is even supporting the cultural and organizational changes as well as tacit knowledge transfer. However, as IT systems are very limited with regard to these issues, they need to be addressed primarily outside the technological realm.
In the case of implicit knowledge, on the other hand, both IT and non-IT activities are necessary to ensure that all relevant pieces are made explicit and inserted into the KM-system. This means that the IT system provides good capturing and insertion capabilities, while non-IT aspects have to encourage people to “insert” their important implicit knowledge into the knowledge base.
Finally, explicit knowledge, codified as information, is the domain of IT (in the form of information management). Although the management of information is done by the IT system, non-IT activities need to ensure that the system and its capabilities are embedded efficiently into the daily work processes. As a result, the available information is managed and made accessible in as many forms and ways as sensible, while at the same time being used as the data source for more advanced features that aim at generating new relevant information. Such generated information is supposed to allow users to gain new insights, thus, ultimately generating new knowledge.
Summing up, these last paragraphs demonstrate that IT and non-IT activities have to play their respective role in all three of the considered knowledge types. In the case of IT the focus is on managing information and providing communication possibilities. On the other hand, non-IT activities have to take care of the cultural and organizational aspects and have to ensure that any IT support is aligned with the goals of the KM initiative and the organizational work procedures.
Many areas of IT have a high importance for KM and KM-systems. Indeed, some of the presented technologies are – either alone or in combination – capable of supporting limited or specialized KM initiatives. Nevertheless, a “complete” and holistic KM initiative needs a “real” KM-system that incorporates or integrates the mentioned technologies.
Business software is widely used to support organizations in areas like human resources, finance, and resource planning, etc. These systems represent a wide area of business related software products and are often called Enterprise Resource Planning (ERP) systems.
Not only do they contain valuable data about employees, customers, organizational hierarchies, projects, etc., but they also provide core IT functions for organizations. In most cases, they also “contain” a wealth of explicit knowledge (available in the form of data and information) together with contextual and historical information.
KM initiatives in organizations using business software (most of the bigger ones do) have two important tasks, namely to integrate the available information and, at least, the functions needed by the majority of the users (the goal is to integrate as much functions as possible).
Information Management Systems (IMS) primarily deal with structured information. As the relationship between IMS and KM-systems has already been discussed in section1.3.1, there is no need for more details.
These systems contain highly relevant information and provide important functions. Thus, such systems should be integrated to enrich the KM-system if they are already in place.
Groupware systems like Lotus Notes (Domino) or Microsoft Outlook (Exchange) are used for unstructured communication and collaboration. They provide functions for coordinating meetings, sharing documents, etc. Often there are add-on products enabling even more direct collaboration like multiple people working with one and the same document or extended communication facilities like chat or video conferencing.
While the unstructured nature of the information available in Groupware systems makes it difficult to integrate, the knowledge “contained” makes it more than worthwhile to do so. Furthermore, some of the functions provided by Groupware systems are of major importance for KM (-systems) and, thus, need to be integrated.
Customer Relationship Management (CRM) can be viewed as special business software. The primary objective of CRM is to provide customers with the best service possible by facilitating all available information about past contacts and purchases. At the same time, this information is used to find out what other products might be of interest to the individual customer ultimately generating increased sales.
Consequently, such systems can provide high quality information centered on customers, products, and feedback. Therefore, this information often will need to be incorporated in KM (-systems).
Artificial Intelligence (AI) is a technology that most of the time is not directly visible to the user of KM or related systems. Instead, it is used to provide “intelligent” functions in systems like CRM. This technology enables IT systems to appear smart by finding correlations, for example. Thus, it possibly is the single most important technology behind KM-systems, especially as it helps to differentiate KM-systems from IMS. Although AI is hidden most of the time from the user (as it is difficult to handle), expert users sometimes need functions based on this technology to directly exploit and explore the available data and information in every possible way.
Closely related areas to AI are Data Mining (DM), Machine Learning (ML), and Knowledge Discovery in Databases (KDD). Although the technology behind all these areas is more or less the same, they try to achieve different goals. These – often “fuzzy” – differences are irrelevant for my thesis and, therefore, not discussed in detail.
Detailed information on DM is, for example, available in papers by Petrak or by Witten and Frank. For an introduction to ML, see Mitchell.
While the mentioned areas represent the most important ones, there are many other relevant technologies. Some are basic technologies like databases or middleware systems, while others are examples of more advanced ones. Examples of the latter are geographical information systems or tools for devices with limited capabilities.
Of course, there are numerous other technologies with a certain importance for KM-systems. However, for introductory purposes this overview should suffice.
Since this introduction is slowly reaching its end, it is time to define the focus and boundaries of this thesis. It needs to be absolutely clear what is included and what is not.
Generally speaking, I am proposing a KM-system and, therefore concentrating on technological issues of KM. Nevertheless, it is of utmost importance to consider KM as a whole for a sound foundation of such a system. From an organizational point of view, my solution should fit for many situations, although this thesis is concentrating on areas where flexibility, creativity, and learning are of high relevance. Furthermore, mechanistic views of management are not considered, even though they often work reasonable well, as I am of the opinion, that the resulting behavior of management is not an appropriate foundation for my solution (without being able to argue this in a rigorous fashion).
The described system is intended for decision makers as well as individual staff (containing all the features both groups need). While it contains support for more conventional and standard requirements like document distribution, such requirements are more or less a side issue. The concept focuses on larger organizations that already have diverse systems in use and is more suited for non-routine tasks such as project management, software development, or more general product development. Although such a system would also address similar issues faced by smaller organizations, it is probably too complex and expensive for their needs.
Other researchers are trying to create tightly integrated IT systems that are based on conceptual knowledge. I think this approach is not feasible for the setting in question. While such a system should be superior from a technological point of view, a multitude of serious problems needs to be resolved first. One of the most important issues is the lack of clearly defined company wide (linguistic) terms in larger organizations (a problem sometimes also encountered in smaller ones). Consequently, I am not examining this topic in more detail.
A definition of the distinction between high and low tangibility processes can be found in Grey. As I am concentrating on the former one, this difference is of high importance for my thesis.
Typical examples of high tangibility processes are project management, software development, or more general product development. While low tangibility KM processes, of course, also benefit from using the proposed solution, the net result is expected to be significantly lower. Low tangibility KM processes need far less support for flexibility and creativity and, therefore, are more concerned with automation (workflows), processes...than with e.g. identifying experts through their project experience.
To conclude this introduction, I would like to discuss relevant artifacts for KM (-systems) and possible classification systems for them. Henceforth, the term artifact is used to have a more general word for what data/information plus additional context and information represents. Later chapters will use this definition only in special places, while in most cases just using the terms data and/or information.
Numerous interesting classification systems defining different kinds of data/information (the source of artifacts with relevance to KM-systems) exist. For example, one is the distinction between structured and unstructured data. Another one is information itself (e.g. the manual for a VCR, describing how to handle the device) as well as Meta information (e.g. who wrote the manual, or who translated it into another language). Furthermore, there is the distinction between declarative and procedural knowledge. Yet another distinction could be made of whether it is textual or image or data. All those distinctions serve specific purposes, while at the same time providing different views of reality.
With regard to KM-systems, there is no major need/application of these distinctions. The single most important distinction to me is what audience is addressed by a particular piece of data/information. The more general the audience, the better from a KM point of view, as less context and transformation needs to be provided, respectively done.
As the solution to be proposed is supposed to address all relevant artifacts, it is necessary to define what pieces of data or information are of importance. Therefore, I would like to present an (incomplete) list containing relevant examples: process descriptions, procedures, rules, best practices, technical descriptions, annotations/comments, ideas, observations, concepts, experience, norms/standards, projects and tasks, skills, knowledge maps, FAQs, Meta information, mental models, micro articles, domain knowledge, incoming messages (e-mails, letters, phone calls, etc.), outgoing messages, and so on.
Another important property of such artifacts is the domain addressed by them. They could, for example, be about customers, companies, ones markets, organization’s own products & services, competitors, employee skills, regulatory environment, methods & processes etc.
Goals, Problems, and Requirements of KM (-Systems)
As we are moving more and more towards a “knowledge society”, many organizations feel the need to enhance their processes around the phenomenon knowledge. Numerous researchers (most of them working in the field of economics) such as Nonaka and Takeuchi (for example in), Drucker, Prusak, Quinn, etc., argue, that knowledge and the related processes are key methodologies for organizations to stay ahead of competition. The most prominent (and broad) issues to be addressed are those of intangible assets (what an organization knows) and knowledge creation (knowing something new). Summing up, the ultimate goal of KM is that of a sustainable, competitive advantage.
While these broad issues help to illustrate what problems KM tries to solve, it is necessary to discuss the goals and problems of KM in more detail to be able to propose a “complete” KM-system. Furthermore, to define an IT system, it is necessary to have requirements and not just economic issues and ”guidelines”. These requirements, of course, need to be based on the identified goals and problems to be addressed by KM and are presented in the last section of this chapter.
The following list of goals is primarily based on two KPMG research reports conducted in early 1998 (see) and late 1999 (see). Additionally, some of the mentioned goals are taken from papers cited in section2.2 (with some goals being mentioned in more than one reference).
The elements of the list represent very diverse issues ranging from shareholder value to raising the potential for innovation. Please note that the following list is in no particular order:
- Improve decision-making
- Faster response times
- Support knowledge transfer
- Accelerate growth
- Discover new knowledge areas
- Strengthen core competencies/Defend market share
- Reduce costs/Increase profits
- Enhance product quality
- Enhance customer relationships/Improve customer handling
- Lower turn-around times (or product cycles)
- Reduce project time
- Raise potential for innovation/Create new business opportunities
- Support teamwork
- Enhance competitiveness/Increase market share
- Retain knowledge in company/Improve staff retention
- Enhance processes/New ways of working
- Increase productivity
- Re-use solutions/Share best practices
- Improve resource usage
- Support internal communication
- Make knowledge available
- Support creativity
- Increase share price
Of course, these goals are rather general and partly overlap. Nevertheless, they illustrate important reasons for introducing KM. The KPMG reports contain results of previous or ongoing KM initiatives and report that a surprisingly high number (over 90%) reached their goals.
The following list of goals provides a different point of view (inspired by a former colleague of mine, Dr. Johann Ortner) that tries to summarize and systematize the relevant issues in a slightly more formal way. Thus, while it presents an alternative way of formulating the goals of KM, this approach is intended to provide greater insight into the topic and does not claim to be superior in any way.
- Enhance transparency of communication flows, organizational structures, and all kinds of links
- Adaptability (to individuals and changing requirements): enhances the efficiency of work processes, prevents bottlenecks and idle phases
- Offering broad communication and transformation possibilities. As communication is the medium of information, it is necessary for knowledge transfer
- Support knowledge generation by offering context and visualization functions
- Support knowledge usage by providing transformations and diverse means of access
- Support knowledge exchange by offering diverse ways of communication and information management
Summing up, this section presents two lists of goals KM is supposed to address. The first one is more business oriented, while the second one provides an alternative point of view. Generally speaking, both have very much in common and supplement each other.
General problems that are addressed by KM (IT deals mainly—if not exclusively—with information or data; for simplicity I am not discussing each occurrence of the term knowledge but instead it is used with a rather general meaning in the following sections) are listed below. These points represent a combination of diverse sources. Some problems are listed explicitly in the references (e.g. in the KPMG reports), others can be extracted from the goals of KM-systems described in the literature, last but not least some come from my discussions/experiences with other people working in the field or considering a KM initiative/system.
- “We do not know what we know”/Internal experts cannot be found
- “Re-inventing the wheel/Making the same mistake twice”/Not invented here syndrome
- “Information overflow”/Knowing it is there but not finding it/Automatic production of irrelevant knowledge/Filtering information based on tasks and long-term interests
- “No/Inadequate automatic notification”
- “No/Inadequate distribution of new “knowledge’’’’
- “Missing/Inadequate capture of employee knowledge (including implicit knowledge; both for sharing and for retaining the knowledge of employees leaving or retiring)”
- Hiding information/Political use of information
- Barriers to information sharing/Delays in information sharing/Distortion of information
- “Missing “history”/Traceability”
- “No multiplication of the knowledge of experts” or experts overloaded with routine questions
- “Lack of knowledge provided for greater insight into a situation or to decide which actions should be taken”
- Missing context (already partially addressed by the point about history)/Allowing items to appear in multiple places/folders (=> one form of ambiguity)
- Using theoretical knowledge for practical problems
- No time (budget) to share knowledge
- Not using technology to share knowledge effectively
- Difficulties in capturing tacit knowledge
- Inaccurate/Out-of-date information
- Integrating new employees/acquisitions/sites
- Sharing/Co-operation with universities/suppliers/customers/...does not work
- Input for quality improvements is missing
- Missing information on competitors/products and new/innovative services
- Prevent alternative decision for the same topic/project
- Inefficient processes for knowledge creation
- No use/adaptation of external knowledge
- Costs for knowledge creation are too high
- Compatibility and externalization problems
- Important knowledge is forgotten / lost
- Missing capture of experiences gained in projects/Provide everything related to a project
- Adaptation of information to user preferences and device/access capabilities
- Damaged relationship to key clients/supplier when the account manager leaves
These issues demonstrate what can and does happen if a working KM-system / initiative is not in place. Ultimately, all these problems lead to the loss of significant income by losing knowledge, for example, on best practices in a specific area of operation. Therefore, this illustrates the importance of KM to all organizations (being small or big, low-tech or high-tech). Not surprisingly, organizations that have already been hit by one of the mentioned problems are more likely to have a KM-system than those not yet hit (–hard enough–).
While virtual teams and organizations are suffering from many of the mentioned problems, there are important differences that necessitate adapted or very different solutions. The single most important reason for this is that such teams have less common “context”, as they are from different organizations and/or cultures. Furthermore, they suffer from having hardly any personal meetings, whereas time differences pose an additional problem, as do expensive travels for any actual meeting.
More detailed information on virtual communities – an area closely related to virtual teams/organizations – can be found in Merali and Davis. The authors discuss the topic of knowledge capture and utilization in virtual communities, thus, offering greater insight into the problems virtual groups in general encounter.
While the previous sections presented general goals and problems to be addressed by KM (primarily from an industry point of view), this section is concentrating on relevant issues of universities. The background is provided by the Vienna University of Technology, although no full-blown KM initiative is planned at the moment. It comes as no surprise that university related issues are often identical or similar to generally identified goals and problems. Finally, in section5.4.3 there is a discussion how the proposed solution addresses the exemplified issues and what areas cannot be solved satisfactorily by an IT system.
The following table presents identified KM goals for a university and compares them to general identified goals (taken from section2.1). It is easy to recognize the numerous and striking similarities (with only one counterpart missing).
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This table demonstrates that only curriculum support is identified as a goal of KM in universities, which has no counterpart in the list of general goals of KM. While some of the mentioned issues are more “soft” (hard to address by IT), e.g. better qualitative output or becoming more attractive to students and researchers, others can be directly addressed by means of IT, e.g. more efficient ways of working or supporting/better communication/cooperative work. As this latter category consists of goals that provide qualitative and quantitative improvements, the (hopefully) positive results of a KM initiative (and a KM-system itself) can be measured and evaluated.
A third category of the presented goals is more “knowledge centered”, e.g. providing all available “knowledge” or capture new knowledge. Therefore, the effects a KM initiative has on these goals can only be measured in qualitative terms. Furthermore, it is of utmost importance to assess what effects the additional available information (achieved by integrating diverse sources or supplementary capturing of new items) has on the organization (essentially, whether the positive effects justify the costs).
After discussing the similarities and differences of goals of universities compared to general ones, the next step is to compare the problems encountered by universities relevant from a KM point of view with more general ones presented in section2.2. The following table provides a comparison that identifies similar problems as well as additional ones without a counterpart to be found in the list of the general ones.
Abbildung in dieser Leseprobe nicht enthalten
As this list is not backed by any literature (contrary to the one containing the general identified problems), it is necessary to discuss the items in more detail. Although some of the actual problems may seem simple, they need to be addressed by a “complete” and holistic KM initiative.
No automatic distribution of information At the moment, e-mail is the most sophisticated mean to “push” information in many universities. An integrated solution will make e-mail and similar functions more powerful and flexible as all kinds of group and other addressing mechanisms become available. While such advanced mechanisms partly address the problem at hand, they only provide very limited automation support. Therefore, any KM-system needs to provide functions to notify users of new information items that have a high relevance for their daily work and their interests. This can be accomplished by using interest profiles and considering what work (e.g. project) the user currently has to accomplish.
No management of interest profiles Already mentioned in the text on the previous issue, interest profiles are of high importance for a KM-system. Currently many universities use limited systems like mailing lists or newsgroups to address the eminent need. A KM-system needs to keep track of the interests of each user and allow the manual adaptation. Furthermore, it needs to propose the addition and removal of interests according to highly sophisticated assessments of the users’ real needs. Using interest profiles and similar relevant information a KM-system is capable of notifying users of new items that have a high probability of being of interest/relevant.
No support in managing curricula As already mentioned, this issue is the only one where I could not identify a counterpart in the list of the general problems to be addressed by KM. At the same time, there is only limited support to manage a curriculum by technological means. On the other hand, a number of potential relevant technologies like knowledge maps and visualization functions exist that promise to ease the problem. Knowledge maps and visualizing the curriculum or parts of it allows identifying areas where many similar courses are offered as well as what areas are lacking courses. A precondition for such functions is that only compatible and known terminologies are used. Of course, any resulting curriculum is important information in itself and helps students to get a better understanding of the university’s offer.
Inadequate capture of “knowledge” of employees/students/etc. Sophisticated support in this area is more or less non-existent in most universities. Of course, there are, for example, all kinds of libraries that offer searching capabilities and make all kinds of information available. Nevertheless, the offered functions are diverse and unsatisfying in one respect or another, while many potentially relevant information sources like newsgroups and e-mails are not included. It is, therefore, necessary to capture as much relevant information as possible, while, at the same time, offering the available information in a consistent and efficient way.
Inadequate support for information sharing At the moment, information sharing is achieved by mailing files, using a newsgroup, e-mail discussions, or personal meetings, etc. Generally speaking, these mechanisms are rather awkward and problematic from a KM point of view, as such unstructured information flows not only are difficult to integrate but also have proven to be ineffective and error-prone. Thus, there is the need for more consistent and structured means of information sharing to capture relevant knowledge and provide effective communication possibilities to partners and within an organization.
Inadequate/Missing context/history of individual pieces of information Every information item has a context and a history, which are of utmost importance to interpret items correctly and to understand what they contain. Although Document Management Systems (DMS) provide these capabilities, they are not in widespread use and do not incorporate all of the relevant pieces of information (for example, discussion forums are not provided or managed by most DMS). Consequently, a KM-system needs to capture context and history for all the available items, using all the available hints it has so that the system does not need to query the user for this information. Such context and history is, of course, very important for managing interest profiles or notifying users, to just name two examples.
Ambiguity not supported There is hardly any mechanism addressing this issue at the moment and definitely no satisfactory solution. Nevertheless, there is the need to provide pieces of information in multiple locations and forms. Although hyperlinks are used widely these days, they do not really solve the issue at hand, as they are just providing one-way and weak connections. Instead, there is need for a more consistent and powerful solution.
Inadequate support of co-operations with external partners As the issue of information sharing has already been discussed separately, the single most important problem remaining is that of cooperatively working on a desired result like a document. There are many ways of supporting this, for example, providing bi-directional access to the current version of the document. Furthermore, flexible and powerful means to communicate have to be provided and need to be accompanied by a powerful permission system to guarantee confidentiality.
No capture of experiences gained in projects While projects often use considerable time on general organizational and technological problems, such experiences are often not made available. Consequently, projects are “doomed” to spend time and energy on problems already solved by other members of the same organization. Although IT can provide functions to allow the easy insertion and retrieval of experiences gained in projects (for example, micro articles), the most important issue is to adapt the existing or introduce a new corporate culture to promote sharing experiences and to provide and search for help.
Inadequate systems for finding/accessing relevant information While there are many closely related issues, this particular one stresses the importance of providing efficient ways to all relevant information items a university has access to. While IT offers many technologies and functions to address this problem, integrated systems resulting from the usage of the relevant technologies are not yet widely used. Although building such a system is difficult, it is the most promising solution of this issue.
No system for identifying/finding experts At the moment, most universities lack systems that identify experts or people knowledgeable in a specific area. As such systems failed to meet their objectives in industry, it is of utmost importance to use a different approach. Therefore, such a system needs to maintain a list of fields and track affinities of people to those fields. Consequently, the resulting list of experts is always kept up-to-date without the need for manual input or changes.
Too much information available (i.e. the wrong information) Most of the issues described have a potential of adding to the information overload many people already experience. While KM initiatives try to provide all relevant information, KM-systems need to provide filters and ways of accessing that reduce the amount of information the user needs to look at to find the relevant items. The key to offer potentially relevant items is to consider information about the user’s situation like his interests or his hierarchical status. Such information can also be used to push items to users that have a very high probability of being of interest (and skipping the rest).
Summing up, the problems to be addressed by a university are often quite similar to those encountered in industry (considering, for example, research co-operations, this comes as no surprise). Furthermore, the discussion of the university problems in detail illustrates that many issues are of relevance to both types of organizations. The different number of mentioned problems finds its explanation in the fact that in section2.2 this thesis provides a summary of problems identified by many experts in many organizations with a diverse background.
The exemplified goals and problems of a university do not only illustrate practical, real world problems in their domain, but at the same time help to understand the more general goals and problems described in earlier sections of this chapter. Consequently, this section demonstrates that a “complete” and holistic solution is the best way to address all the diverse issues KM tries to resolve. While shrink-wrapped software cannot be the basis of a KM initiative, it is a reasonable approach to define a concept of one KM-system for different types of organizations.
As reports about successful and failed KM initiatives using various IT systems/tools exist, it is important to analyze the reports before defining the requirement of a KM-system. A newly proposed KM-system, of course, needs to address all problems that have been encountered in similar systems so far.
Many descriptions of such problems can be found in literature (e.g. Romhardt, Fagrell et al., Döring-Katerkamp and Trojan, or in a recent overview paper by Harris at the Gartner Group). The following list shows the problems either most commonly identified or being of highest importance:
- System not accepted/used / Lack of user uptake due to insufficient communication
- Information is not stored/classified/found properly in KM-system
- Using theoretical knowledge for practical problems
- KM not integrated into normal work procedures
- Users do not see the personal benefits
- The “knowledge” distribution system does not make sense
- Lack of time (a problem that should be solved by KM)
- Lack of training
- Difficulty/Failure to capture tacit knowledge
- Senior Management does not support the initiative
- Unsuccessful as a result of technological problems
As these problems either are organizational ones or related to the actual realization of the KM-system, building a “complete” and holistic KM-system is possible using currently available technology. Of course, all issues related to IT need to be considered and addressed by the proposed solution, while the KM initiative itself has to take care of the non-IT problems.
Further information can be found in a paper by Malhotra that contains a discussion of problems of KM-systems together with insights about the suitability of certain approaches. The author continues by describing what issues need to be taken into account in the case of a next generation KM-system. Summing up, the paper presents very interesting and important arguments, why certain KM-systems failed, that need to be considered when proposing such a system.
 Taken from: Prusak, L: “The knowledge advantage.” Strategy and leadership, March-April 1996, pp. 6–8.
 Denham Grey is working in the field of KM at least since 1994. He has been one of the most active participants in the KMCI group. The KM101 Summary and its views of knowledge and KM seem to be especially well suited as a foundation of my thesis.
 Information is often defined as “data endowed with relevance and purpose”. This particular definition is from Drucker, P.E. (1995): “The Post Capitalistic Executive.” in P.E. Drucker (ed.) Management in a Time of Great Change New York: Penguin.
 With Popper, for example, we deal mostly with World 2 or “subjective” knowledge that can be tacit/implicit/explicit or with World 3 or organizational knowledge (the codified problems, models, etc.) that forms the basis of World 2 knowledge.
 This approach is described in his paper: Data is more than Knowledge: Implications of the Reversed Hierarchy for Knowledge Management and Organizational Memory; In Proceedings of the Thirty-Second Hawaii International Conference on Systems Sciences, IEEE Computer Society Press, Los Alamitos, CA, 1999.
 For example, see Argyris, C. and Schön, D. A.: Organizational Learning: A Theory of Action Perspective. Addison-Wesley, Reading, MA, 1978.
 This topic is discussed starting with Digest Number 146 (May 4, 2001) by Joe Firestone.
 The more general concept would be to see tacit knowledge in different degrees of expressibility.
 Another issue where a widely accepted definition is missing.
 While the systems need to deal with the different forms adequately, the conceptual differences are just of minor importance from a pure technological point of view.
 As long as the “contained” knowledge is of similar importance and relevance.
 As many of the participants, claiming that their organizations reached their respective goals, were responsible for those KM initiatives, the high percentage should be viewed with certain skepticism.
 This problem is reported particularly often in literature but the question remains whether it really is of such high importance. When looking at a similar field – Software Development and Re-use (for example, see Basili et al. or) – there is conclusive research that “re-inventing the wheel” often is the most appropriate solution.
 The 1998 KPMG report, for example reports “too much knowledge” on page 17, which is a rather problematic description when thinking about the distinction between information and knowledge.
 The real problem may be lost knowledge of best practices in a specific area of operation. This problem is also discussed in a recent paper by Coakes et al. together with possible solutions like exit interviews, knowledge capturing, etc. Furthermore, a paper by Babilon explicitly discusses the use of best practices within NCR as part of their KM initiative.
 Although this problem is reported quite regularly, it is rated as being of low importance in the KPMG reports. Generally speaking, it would instead appear that people are often indifferent to KM-systems, which is in turn misinterpreted as information hiding.
 This problem demonstrates that even KM initiatives where people realize the importance of the topic might still fail due to simple but fundamental problems.
 Very simple examples are providing information about competitors and employees skills. Even such simple pieces of information often are not easily accessible.
 Many researchers argue that capturing tacit knowledge offers great potential for KM-systems. In light of the discussions in the first chapter, it is obvious that while implicit knowledge can be made explicit, this is not the case for tacit knowledge as defined by Polanyi.
 Additional problems arise if the units trying to share knowledge have different corporate or cultural backgrounds. While access to best practices is often proposed to address this issue, they are at best a limited solution when faced with different backgrounds.
 The importance of information about customers, products/services, competitors, etc. is also discussed in a paper by Chen et al..
 For example, NCR’s initiative as presented by Babilon describes the inclusion of best practices and lessons learned to their knowledge base.
 Although curriculum support is not listed in the previous sections, there are a number of non-university organizations that encounter similar problems as universities, especially companies offering courses.
 However, one must keep in mind that the provision of powerful push mechanisms needs to be accompanied by guidelines how to use them. Otherwise, there is the very real danger of misuse and, as a result, of flooding people with irrelevant information.
 For example, the Xlink and Xpointer standards by the W3C show that this problem is the focus of ongoing research.
 Although such a database addresses the need to offer multiple ways of finding and accessing expertise, one should not expect the whole organization to use this functionality, since many members will prefer other ways of acquiring the information they need.
 Please note that actual implementations of the proposed concept can vary to a high degree and that organizations may choose to implement only certain parts of it.
 Presenting a study with 347 participants from 12 countries on KM containing results of encountered problems.
 While incentive systems fail to provide long-term motivation they may help to start the process and give people an immediate reward (-> benefit). This issue is discussed in more detail in section3.2.1.
 Once again, one should not forget that the real problem would most probably lie in the capturing of implicit, informal, and contextual information.
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