Knowledge management process models for knowledge maps
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Knowledge management process models for knowledge maps



Knowledge management process models for knowledge maps

Knowledge management process models for knowledge maps



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    Knowledge management process models for knowledge maps Knowledge management process models for knowledge maps Document Transcript

    • Knowledgemanagementprocess models forknowledge maps
    • Colophon stDate : March 1 , 2004Version : 1.0Change :Project reference : Metis D4.3TI reference :URL :Access permissions : PublicStatus : FinalEditor :Company : University of AmsterdamAuthor(s) : Robert de HoogSynopsis:This report investigates how knowledgemanagement process models consisting of aset of tasks, can be linked to knowledge maps.Several models and properties of knowledgeare reviewed. The result is a combined modelof knowledge management tasks andproperties of knowledge that, whenincorporated in a knowledge map, can supporttask performance. Based on this model aprototype of a knowledge mapping tool can bedesigned and built.M E T I S D 4 . 3 III
    • Preface Most knowledge mapping efforts are directed toward mapping documents and other sources by using the domain content of these documents. While these maps can be useful for people engaged in the actual use of the knowledge, this does not necessarily hold for people concerned about managing the knowledge. A simple example from Basell can explain this. A person that has to repair or replace a component in the Extruder, needs the knowledge how to do this. This knowledge can be described in manuals and can also reside in people’s minds. A person that has to make sure that Basell possesses the knowledge to replace and repair a component, is more interested in how stable this knowledge is, whether the proficiency level of the knowledge is adequate, how accessible this knowledge is and other aspects. These properties of knowledge can be seen as a kind of meta-knowledge, knowledge about knowledge. Most of the time this meta-knowledge cannot be extracted from the domain content. As a consequence, knowledge mapping efforts relying on the content of documents will often fail to provide the information needed to deal with many knowledge management issues. More in general it can be said that different goals give rise to different knowledge maps and that goals can be derived from tasks people are performing. This implies that identification of tasks should most of the time precede knowledge mapping efforts. In this report several knowledge management process models consisting of tasks are reviewed and compared. Several criteria for constructing “good” knowledge management process models are identified. From another angle, properties of knowledge that have been proposed in the literature, independent of any knowledge management process model, are reviewed and combined. Synthesizing both in a knowledge management process–knowledge properties model, leads to a framework that can be used to design and build a knowledge mapping environment that can support knowledge management. IV
    • About Metis Knowledge management: smart employees and smart organisations Each company, even a company with knowledge workers, needs to invest to be able to continuously improve itself, that is, to become smart. The objective of knowledge management is to transform companies from organisations with just smart people to smart organisations. A smart organisation knows what knowledge it wants to have, has employees that have this knowledge, and uses technologies in a smart way to support the knowledge workers in the organisation. Of course, a smart organisation uses its network to acquire knowledge. Knowledge management is something you do, so why research? Managing knowledge, sharing knowledge, and making knowledge available, are things companies need to do themselves. In practice difficulties appears though. It has become clear, that companies that apply knowledge management, are left with a number of unanswered questions. Research to find solutions for those questions is therefore needed. Examples of those questions and solutions are: - How can I enhance the - Find combinations of technical support and innovative power of my organisational incentives that stimulate the exchange organisation? of knowledge, experience and vision, and the - How do I ensure that the application of new ideas knowledge and vision of my smart people becomes knowledge of my organisation? - Am I doing well with knowledge - Identify indicators of good knowledge management management? - What knowledge does my - Extract knowledge from communication and organisation have? documentation and provide relevant views on this - They say my organisation has knowledge for knowledge workers and knowledge smart people, but how do I managers know? - Does my organisation - Identify the opportunities that communication and sufficiently exploit the options to network infrastructures offer for the exchange of communicate with the outside information and knowledge across the borders of world? To learn from customers? organisations The current project explores these types of solutions. In this project we study how companies can apply knowledge management to enhance their business results. We support knowledge managers with insights and instruments. These could be innovative technologies, like the automatic generation of knowledge maps, but also guidelines, new business models or future scenarios. For this, we analyse existing knowledge management instruments, like communities and document management systems, elaborate on, and apply them in a specific company context of the project partners. In other words, we look ahead, and work together with companies to obtain tailored and long-lasting solutions. Thís is knowledge management between industry and the academic world in optima forma. For more information see Metis website. M E T I S D 4 . 3 V
    • Management summary This report investigates what aspects should be represented in knowledge maps that can support the performance of knowledge management tasks. In this context a clear distinction is made between using the knowledge and managing the knowledge. It is argued that this are different tasks that require mapping of different aspects of knowledge. Accepting that managing knowledge is a separate task, the next question to answer is how one can describe or model this task in more detail. This is needed because it is assumed that the information in knowledge maps are functional in the context of one or more tasks. This question is addressed by reviewing knowledge management process models described in previous Metis deliverables and the knowledge management literature. The Metis deliverables referring to knowledge management tasks most of the time do this in an incomplete or somewhat fuzzy way. The general knowledge management literature abounds with models, but there are at least four available that keep a more or less clear distinction between management and work. These models are reviewed and selected on the basis of a set of criteria consisting of cyclical nature, knowledge specificity, separating management from work, appropriate grain size, tool independency and time horizon. Not all of them perform well on all these criteria. As knowledge maps will basically display values of variables or properties, the question is which properties? Based on the knowledge management literature a set of agreed upon properties is constructed that could be used in knowledge management relevant knowledge maps. It is concluded that the value of the majority of these properties cannot or not easily derived by using techniques for knowledge mapping for work, for example based on the content of documents. In order to make things more specific for knowledge management two more aspects are analyzed and added: • Characteristics of knowledge that set knowledge apart from other organizational resources • General management goals that must be satisfied by (knowledge) management activities Based on knowledge management tasks, relevant properties, characteristics of knowledge and general management goals, three tables are constructed that together form a knowledge management – knowledge properties model for knowledge maps. Based on this model one can select either a knowledge management task, a knowledge characteristic or a management goal and find the knowledge properties that can be used to support the chosen perspective. These tables can be used to design meaningful knowledge maps for knowledge management as a management task, but also for designing web sites/portals that organize knowledge about knowledge management. VI
    • Table of Contents1 INTRODUCTION.......................................................................................................................12 KNOWLEDGE MANAGEMENT TASKS IN METIS..............................................................3 2.1 JUST-IN-TIME KNOWLEDGE MANAGEMENT ..............................................................................3 2.2 KEY PERFORMANCE INDICATORS FOR KNOWLEDGE MANAGEMENT IN A COMMUNITY OF PRACTICE ..........................................................................................................................................5 2.3 MANAGING KNOWLEDGE MANAGEMENT .................................................................................7 2.4 KNOWLEDGE MAPPING ...........................................................................................................8 2.5 FROM KNOWLEDGE MAP TO KNOWLEDGE WEB ......................................................................10 2.6 SUMMARY ...........................................................................................................................103 KNOWLEDGE MANAGEMENT TASKS IN THE LITERATURE......................................11 3.1 THE HOLSAPPLE-JOSHI MODEL .............................................................................................11 3.2 THE CIBIT MODEL...............................................................................................................17 3.3 THE WIIG ET AL. MODEL ......................................................................................................23 3.4 THE DUINEVELD ET AL. MODEL ............................................................................................28 3.5 SELECTION/CONSTRUCTION CRITERIA FOR A KNOWLEDGE MANAGEMENT MODEL AND KNOWLEDGE MANAGEMENT TASKS ..................................................................................................294 KNOWLEDGE PROPERTIES ................................................................................................325 A KNOWLEDGE MANAGEMENT – KNOWLEDGE PROPERTIES MODEL FORKNOWLEDGE MAPPING..............................................................................................................43 5.1 CHARACTERISTICS OF KNOWLEDGE ......................................................................................43 5.2 KNOWLEDGE MANAGEMENT GOALS ......................................................................................47 5.3 SUMMARY AND REFLECTION ................................................................................................516 REFERENCES..........................................................................................................................53M E T I S D 4 . 3 VII
    • 1 Introduction The knowledge management literature, which exists now for more than 10 years, is still in considerable terminological disarray. Though this is admissible for an emerging discipline, in the long run some kind of standardized set of terms and meanings should emerge. Unfortunately there are still not many visible signs of this desirable process. This holds also for the Metis project. A brief look at the 2002 deliverables shows a bewildering variety of words, concepts and terms. As a consequence it is very difficult to see the forest for the trees. Detecting similar developments and approaches is almost impossible, which in the end will be detrimental to the project’s objectives. The same observation can be found in the Metis Quick Guide 2002. In the context of the knowledge mapping task(s) the state of affairs is more or less the same. Knowledge mapping, for better or for worse, requires a goal and this goal determines what is mapped and how it should be mapped. The analogy with real maps is clear. The map needed for a walking tour in the Alps differs from the roadmap you need to traverse the same area. A map of the geology of a region differs from a map of the waterways. The first is needed to detect geological formations, the second is for navigating purposes. In all these examples the nature of the map is derived from the goal the user wants to pursue with the map. In this deliverable I will explore how the nature of knowledge maps is related to goals someone wants to achieve with these maps. Of course these goals are approached from a knowledge management perspective, which in turn requires some clear concepts about what knowledge management tasks are or could be. Most of the time our goals are bound to real world tasks we want to accomplish, see the verbs in the map examples above: walking, traversing, detecting and navigating. It is here that the terminological confusion hits hardest. No clear tasks, no clear goals, no clear knowledge mapping requirements. The same line of relating requirements to tasks, is also visible in requirements engineering in general (Lauesen, 2003). The identification of knowledge management tasks can proceed along two directions: • A descriptive approach: people performing knowledge management in real life are observed or interviewed, and from these data a descriptive model of knowledge management tasks is derived. • A prescriptive approach: from a theoretical/analytical viewpoint a normative model is built that prescribes what “good” knowledge management tasks are. Both approaches are present in the Metis project as well as the general literature on knowledge management. Sometimes both approaches meet. This happens when developers of normative models try to find out whether their theoretical work stands the test of practice. Descriptive models are sometimes transformed into normative ones by stating that reasonable people should follow “best practices”. There is no a priori preference for one approach. The M E T I S D 4 . 3 1
    • only requirement for authors could be to ask them to be clear about which approach, orcombination, they pursue. However, this requirement is only rarely met. The approach chosenhere is decidedly normative, but to a certain extent it is influenced by experiences from thepractical application of normative and descriptive models.In this report I will first briefly review several deliverables from the 2002 Metis batch to find outwhat they say about knowledge management tasks. Next I will review the existing literature ondefinitions of knowledge management tasks. This is followed by an inventory of domainindependent properties of knowledge (meta-knowledge or knowledge about knowledge) thatwere proposed in the literature. Finally I will develop a set of knowledge management tasksand combine these with properties of knowledge that should be represented/mapped in orderto be able to perform these tasks adequately.2
    • 2 Knowledge management tasks in Metis This chapter reviews several documents from the Metis project on the presence or absence of information about knowledge management tasks. All reports were scanned, but only the ones having (part of) the sought information are discussed in more detail. When reading the sections below the reader must be aware that these reports were read with a very specific objective in mind. So if the documents are criticized, they are criticized in the light of that objective. As most authors did not have this objective, one cannot blame them. In other words: if I find something missing in a document, this does not mean that it is a “bad” document. It can serve other objectives in an excellent way! 2.1 Just-in-time knowledge management The report by d’Huy et al. (2002) seems to focus on a subset of knowledge management where optimisation of the match between knowledge demand and knowledge supply in organisation is the central concern. As this research is, until now, mainly based on theoretical considerations and tries to define a model “for” JIT knowledge management, it can be classified as a prescriptive approach. Before the authors embark on their work, they first review several models from the literature. They adopt the knowledge value chain approach from Weggeman (p.16) as the definition of the knowledge management process (and by implication knowledge management tasks). However, later they introduce other models, for example the Baets model and the KnowMe models, which are far less clear in their identification of tasks. To make matters even more complex, they define on p.26 a set of questions, which in my view would constitute a fairly explicit brief for JIT knowledge management. If I rewrite these questions as tasks to find answers, and I combine them with the specific transformation considerations on p.33, the following set of knowledge management tasks would emerge: • Task 1 Determine knowledge demand o Task 1.1 Find out the actual demand for knowledge inside the organisation o Task 1.2 Find out the the knowledge demand expected in the future o Task 1.3 Determine which demand of knowledge comes from outside the organisation (market, business) o Task 1.4 Assess need to react to these demands • Task 2 Determine knowledge supply inside the organisation o Task 2.1 Find out which knowledge is available inside the organisation M E T I S D 4 . 3 3
    • o Task 2.2 Determine the location of the specific knowledge inside the organisation o Task 2.3 Find out which knowledge is not available inside the organisation and for which a certified demand has been determined in Task 1.4. • Task 3 Bring demand and supply together on a JIT basis o Task 3.1 Define fitting knowledge transformation instruments (e.g., from table 2, p.34) o Task 3.2 Determine costs associated with the transformation instruments o Task 3.3 Find out whether the time needed to effectuate the transformation fits the time available for meeting the demand o Task 3.4 Decide between outsourcing and do it ourselvesThough one could argue about the completeness of this model or the specificity forknowledge management (if “knowledge” was replaced by “iron”, would the model bedifferent?), there can be no doubt that it is a task model for JIT knowledge management. In 1addition the authors give on p.32 a brief list of characteristics for describing the demand : • time related: the knowledge is needed at a certain time • type: demand for a product/service, result, person.. • level: oriented on individual, group or organisation • quality • quantity • hardness (what possibilities there are to redefine the demand)Of course this list is only a sketch, but gives at least a clue about properties of knowledge thatshould be described to support the JIT knowledge management tasks. One should note thatthese properties are completely different from the properties that are usually mapped in(automated) knowledge mapping tools. These focus on the content, because they can bederived from the knowledge carriers (most of the time documents). Automated derivation ofproperties like the ones listed above from the carriers is almost impossible as the content willnot contain any clues about, for example, the “time related” attribute.1 Note that this is about properties of the “demand”, which can differ from the properties of the knowledge. One couldargue that “demand” is a property of knowledge which has also properties.4
    • Given what was said above, it seems a bit odd that in the final chapter the authors introducethe “first JIT knowledge management model” shown below in Figure 1. Figure 1: JIT knowledge management model by d’Huy et al. (2002)Compared with their earlier (reconstructed in the task model above) knowledge managementmodel, this model is less explicit in terms of tasks. Moreover the authors appear to beconfused about the model’s status. The text says it’s a model “for” JIT knowledgemanagement but the caption calls it a model “of” knowledge management. The former refersto a prescriptive view, the latter to a descriptive one.To summarize: in my opinion this report contains a skeletal task model for JIT knowledgemanagement. It also shows that mapping knowledge for JIT knowledge management has tofocus on properties that cannot, or not easily, automatically derived from the content ofdocuments. At the same time, this last aspect must be described in more detail to make aclear link between JIT knowledge management tasks and mapping the, for these tasks,relevant properties of knowledge.2.2 Key performance indicators for knowledge management in a community of practiceThe main question addressed by this document (de Moor & Smits, 2002) is: how to measureand use key performance indicators in knowledge management in organisational communitiesof practice. This is partly addressed by investigating the literature and partly by a case studyin a small knowledge intensive organisation. Thus it can be seen as a combination of aprescriptive and a descriptive approach. Nonetheless, an answer on the main question wouldbe more of a prescriptive than a descriptive nature. Key performance indicators can be seenas properties of knowledge and/or knowledge processes that one has to measure in thecontext of knowledge management tasks. Thus performance indicators presuppose tasks inwhose context their measurement is meaningful.M E T I S D 4 . 3 5
    • The case study in this report is inside a company for which innovation is the key to survival.This means that the process of knowledge creation is taken as the focus. As the authors sayon p.8: “Knowledge management concerns the support and stimulation of this cyclical processof knowledge creation”. The cyclical process they are referring to is the well-known Nonaka-Takeuchi model of knowledge creation. Next the question could arise how to organise this“support” and “stimulation”. Concerning this problem the authors propose a mixture ofenabling conditions, types of Ba, guidelines, strategies and learning disabilities (derived fromthe work of Senge). From this mixture it is very difficult to derive a set of knowledgemanagement tasks. If I apply the same method as in the previous section, rephrasing thecomponents of the mixture as tasks, a rather disorganized set remains with no discernible(sequential) structure. However, this seems not necessary. When they arrive at definingindicators they basically return to the four knowledge processes from Nonaka & Takeuchi,which are transformed into knowledge types in the following way: • Socialization -> Sympathized knowledge • Externalization -> Conceptual knowledge • Combination -> Systemic knowledge • Internalization -> Operational knowledgeFor each category they identify indicators (for example “direct communication links” forsympathized knowledge) which are used to give a “quick overview of the effectiveness of thevarious …. processes”. Re-engineering this to tasks, it amounts to saying that there are atleast four knowledge management tasks in whose context these measurements aremeaningful: • Promote and monitor socialization processes • Promote and monitor externalisation processes • Promote and monitor combination processes • Promote and monitor internalisation processesIt can easily be seen that this still are very general tasks without the details needed to carrythem out. On the other hand, it is also evident that the indicators used, the properties ofknowledge to be mapped to support these tasks, are hardly or not at all derivable in anautomatic way from documents containing knowledge content. In this respect the same holdsas in the previous section.Finally the authors introduce a model of levels of knowledge management for a community ofpractice (see Figure 2).6
    • Figure 2: Knowledge management model by de Moor & Smits (2002)When detailing these levels on p.27, several task related terms appear. For example:“Operational management receives (looks for) customer requests for a knowledge intensiveproduct or service. Operational KM then forms a project team consisting of some knowledgeworkers …… Operational KM needs an up-to date overview of free and available resources(represented in map1) to be able to effectively create the project team”. Comparablestatements are made for the other levels. It should be noted that the “maps” (or indicators)referred to are completely different from the set of indicators identified for the global tasksenumerated above. Thus from a knowledge task-knowledge mapping perspective this sectionof the report is the most interesting. A more detailed description of the tasks/indicators relatedto these levels would be welcome.Summarizing: part of the report does not contain clear indications of knowledge managementtasks. Only the last chapter, by defining different levels of knowledge management,approaches what could be labelled as an initial set of knowledge management tasks. At thesame time it has become clear that in both “parts” of the report knowledge mapping must befocused on properties of knowledge or knowledge processes that cannot be automaticallyderived from documents.2.3 Managing knowledge managementThis report by Efimova (2002) is definitely written from the descriptive angle. It gives theresults of an empirical investigation of the role of Chief Knowledge Officers (CKO’s). One ofthe questions this study focused on was: “What do CKO’s do? Activities and interventions”. Inaddition other topics are: what the capabilities and competencies of CKO’s are and theresources and support a CKO requires. The first two can contain clues about knowledgemanagement tasks, the third about properties of knowledge needed for carrying out theseM E T I S D 4 . 3 7
    • tasks. An interesting point in the first question is the distinction the author makes between“activities” and “interventions”, something that is missing in the documents analysed in theprevious sections. Though not clearly defined, I assume this to refer to tasks a CKO has toperform (“activities”) and interventions s/he carries out in the organisation.Unfortunately, this distinction is not so neatly kept in the reporting of the results (it shares thiswith the remarkable similar paper by McKeen & Staples, 2003). After some interpretation, Iassume that the “interventions” are described in the section on “KM techniques and tools”.The “activities” are probably located in the sections on “CKO goals and measurement” and“CKO responsibilities”. These goals and responsibilities can be rewritten as tasks, but just asin the previous section, they lack (sequential and nested) structure. Some are directlyformulated as tasks, e.g., “monitoring and evaluating” (see Figure 6 in the document) othersare not. An example of rewriting a goal as a task is: “Being an internal centre of excellence forknowledge sharing (goal) -> Developing myself into an internal centre….”. I could not find anylocation in the document where “resources and support” are described and, as aconsequence, it is not possible to derive any knowledge properties that could support theirimplicitly defined tasks or “activities”.To summarize: this report seems to fall short of its initial promise to identify CKO activities(i.e., knowledge management tasks). With some effort a list can be extracted, but this isunstructured and probably incomplete. In the same vein, no information is given aboutresources and support for these activities, which makes it impossible to derive properties ofknowledge that should be included in knowledge maps.2.4 Knowledge mappingThough this report by Huijsen et al. (2003) has mainly a technical flavour, it could containsome information about the relation between knowledge management tasks and knowledgemapping.The obvious place to look for this information is in the section devoted to Knowledge-Mappingstakeholders. This section describes 7 categories of stakeholders. However, their “stakes” aremainly described in terms of “typical questions” they might be interested in. For example formanagement: how many people have expert knowledge in subject area X. Or for knowledgemanagement: for which subjects that are many people interested in are there no communities(see p.9 and 10). Though again these questions can be rephrased as tasks, this will lead toanother incomplete and unstructured list. The Knowledge Cockpit demonstrator described inChapter 4 of the document seems mainly useful for knowledge workers who want to locateexpertise, which could also be one of the knowledge management concerns. The reportpresents a database schema shown below in Table 1.8
    • Item Attributes conference Title Dates course Title Dates document Title Content publication date e-mail date & time message Addressee keyword keyword string Language Definition approved/unapproved person Name Phone e-mail address Fax Location organisation (meant for external contacts that are included) Photograph position Description project Title Period webpage Title URL Table 1: Data base schema proposed by Huijsen et al. (2003)A comparison between what has been indicated in the previous sections about knowledgeproperties needed for mapping to support knowledge management tasks, shows that only avery limited subset of attributes from Table 1 can serve these purposes. The majority is tightlycoupled to a document as the main carrier of (static) knowledge.To summarize: this document contains almost no clues about knowledge management tasks.The database schema is mainly geared to support knowledge workers in their day-to-daywork, which is, of course, a quite valuable goal.M E T I S D 4 . 3 9
    • 2.5 From knowledge map to knowledge webThis case study by Brussee et al. (2003) delves deeply into the daily working of severalemployees in a company. The larger part of the document is irrelevant for the purpose of thisreport, but there is a small exception. This is due to the fact that one of the actors performs aHuman Resource Management task.The observations are analysed using the 5-T method and one of the T’s is about Tasks. In asection devoted to tasks, I found the following quote:” Yvonne de L. ‘s task is to find peoplesuitable for the Myth assignment. She divides this in subtasks: finding out what expertise isneeded for a particular task by looking into the expertise nodes of the knowledge maps andchecking with people if this is what is needed, and finding the people by checking theirexpertise and asking others what their impression is” (p.31). This quote amounts to a fairlyprecise description of a (sub)task of the HRM task(s) performed by Yvonne. It should benoted that she does not deal with the knowledge directly: She handles it as a resource to beused in other (operational) tasks. Another interesting point is that there is an immediate linkbetween a knowledge map (“nodes in the knowledge map”) and a task (“finding out what isneeded for a particular task”). Note also the implicit distinction between her HRM task(s) andthe tasks in the Myth project. In addition, what is described here is very similar to what wasdescribed in section 2.1 on JIT knowledge management.To summarize: though only a minor part of the report could be useful, at least one descriptioncan be seen as a good example of how (knowledge) management tasks and knowledgemaps, presenting relevant properties of knowledge, should be linked.2.6 SummaryThe review of several Metis reports has shown that only a few contain clues about knowledgemanagement tasks. The one that comes closest is the re-written set of questions from the JITdeliverable, but these are incomplete and lack detail. In the next chapter we will investigatewhether the literature on knowledge management has more to offer.10
    • 3 Knowledge management tasks in the literature In this chapter I will review several knowledge management models proposed in the knowledge management literature. As there is a bewildering array of models available it is not so easy to make a selection. The main focus in selecting is on identifying models that contain more or less explicit task descriptions. 3.1 The Holsapple-Joshi model The most recent contribution to the discussion about knowledge management tasks can be found in the interesting paper by Holsapple & Joshi (2003). What they try to do is to clarify the main concepts that make up the notion of knowledge management, thus addressing the disarray referred to in Chapter 1. It should be noted that their model is based on empirical research (expert opinions) as reported in Holsapple & Joshi (2002). Their starting point is what they call a “Knowledge management episode”: a pattern of activities performed by multiple processors with the objective of meeting some knowledge need. Figure 3 below gives the architecture of a knowledge management episode. Figure 3: Architecture of a knowledge management episode (Holsapple & Joshi, 2003) Based on the architecture shown in Figure 3 they develop a knowledge management ontology, a set of ordered concepts that characterize knowledge management. For the purpose of this report two elements of Figure 3 are important: the nature of a knowledge management episode and the knowledge management influences. I will deal with episodes first. An episode consists of a configuration of what they call “Knowledge manipulation activities”. Their definition of “knowledge manipulation” is not precise, but the general idea is that this is a skill to handle knowledge resources. Figure 4 below depicts their general model of knowledge manipulation activities that can play a role in an episode. M E T I S D 4 . 3 11
    • Figure 4: Knowledge manipulation activities according to Holsapple & Joshi (2003).Figure 4 gives the high-level knowledge manipulation tasks, these are further decomposedbelow.Acquiring knowledge: identifying knowledge in the environment and transforming it into arepresentation that can be internalised and/or used. Subtasks • Identifying: locating, accessing, valuing, and/or filtering knowledge from outside sources. • Capturing: extracting, collecting, and/or gathering knowledge deemed to be sufficiently valid and useful. • Organizing captured knowledge: distilling, refining, orienting, interpreting, packaging, assembling, and/or transforming captured knowledge into representations that can be understood and processed by another knowledge management manipulation activity. • Transferring organized knowledge: communication channel identification and selection, scheduling and sending.12
    • Selecting knowledge: identifying needed knowledge within an organisation’s existingknowledge resources and providing it in an appropriate representation to an activity thatneeds it.SubtasksThese are basically the same as under “Acquiring knowledge”, but the domain is within theorganisation.Internalizing knowledge: incorporating or making the knowledge a part of the organisation.Subtasks • Assessing and valuing: determining the suitability of the knowledge. • Targeting: identify knowledge resources that are to be impacted by the knowledge produced by internalisation. • Structuring: representing knowledge to be conveyed in the appropriate form for the targets. • Delivering: modifying existing knowledge resources, depositing, storing, updating, disseminating, distributing and sharing knowledge, it also involves channel identification and choice, scheduling, and sending.Using knowledge: this is decomposed in generating knowledge and externalizingknowledge.Generating knowledge: produces knowledge by processing existing knowledge.SubtasksMonitoring the organisation’s knowledge resources and the external environment by invokingselection and/or acquisition activities as needed.Evaluating selected or acquired knowledge in terms of its utility and validity.Producing knowledge from a previously existing base, this can involve creating, synthesizing,analysing, and constructing knowledge.Transferring the produced knowledge for externalisation and/or internalisation, includeschannel identification and choice, scheduling and sending.M E T I S D 4 . 3 13
    • Externalizing knowledge: using existing knowledge to produce external outputs for releaseinto the environment.SubtasksTargeting the output: determining what needs to be produced.Producing: applying, embodying, controlling and leveraging existing knowledge to produceoutput.Transferring: packaging and delivering the projections that have been produced for thetargets, this includes channel identification and choice, scheduling and sending.As can be easily seen from the text above, the task decomposition for some subtasks goeseven further. Verbs like “creating”, “locating”, “packaging” clearly refer to tasks, but these arenot described in detail by the authors. Neither do they say anything about the ordering ofthese terms. However, with some effort some orderings can be imposed. For example, the“Identifying” subtask from “Acquiring knowledge” consists of the lower level tasks locating,accessing, valuing and filtering, and the order of enumeration can be seen as a “natural”sequence of carrying out these tasks.A slightly confusing aspect of this model is the recurrence of tasks in different parts of themodel. For example, the task “channel identification” appears in the main tasks Acquiring,Internalizing, Generating and Externalizing. Though precise performing of these tasks isobviously context dependent, a more specific naming could have been chosen.More worrying is the question how knowledge management specific this model is. One couldtry to replace all occurrences of “knowledge” in the text above with another resource, e.g., oil,and see whether the model still makes sense. In my opinion the major part of the tasks arethen still meaningful and executable. One could argue that the model gets it’s “flavour” fromdealing with a very specific resource type, but the authors fail to explain how this influencesthe way to carry out the tasks.The second aspect of the architecture depicted in Figure 3 are the knowledge managementinfluences. These “managerial influences” can be seen as management in a more limitedsense. They consist of four management activities: • Coordination: this involves the determination of what knowledge activities to perform in what sequence, which participants will perform them, and what knowledge resources will be operated on by each. • Control: ensuring that the needed knowledge resources and processes are available in sufficient quality and quantity.14
    • • Measurement: the valuation of knowledge resources and processors. • Leadership: establishing enabling conditions for fruitful knowledge management.These four activities are supported by a set of factors to be considered, phrased as questions.In parallel to what I did with the JIT-model in Chapter 1, these questions can be re-phrased astasks (i.e., finding an answer to the question). In Table 2 below I reproduce Holsapple &Joshi’s terminology, but the reader should substitute the “What” and “How” with somethingline “Find out” or “Investigate”. I have numbered the tasks and questions, this will be re-usedin Chapter 5.Managerial Influence Factors to consider (tasks) A.1 Is there top level commitment to KM initiatives? How does it manifest? Does it align with the organization’s purpose and strategy? A.2 How is KM leadership cultivated at lower levels?A. Leadership A.3 How are condition created that allow processors to do their best individual and collective knowledge work? A.4 How is a culture appropriate to knowledge work established? A.5 Is there technological support for KM leadership? B.1 What knowledge activities are performed? B.2 How are they organized to accommodate dependencies? B.3 Which processors perform them?M E T I S D 4 . 3 15
    • B. Coordination B.4 What knowledge resources are used and/or changed? B.5 Is the knowledge processing self- directed, guided or dictated? B.6 What incentive structures are in place to secure efforts? B.7 How is the knowledge processing integrated with other operations? B.8 How are best KM coordination practices recognized/preserved/applied? B.9 Is there technological support for KM coordination? C.1 What regulations are in place to ensure quantity, quality and security of knowledge resources and processors? C.2 How are knowledge resources protected from loss, obsolescence, improper exposure/modification, and erroneous assimilation? Via legal, social, technical means?C. Control C.3 What validation controls are used to ensure sufficient accuracy, consistency, and certainty of knowledge resources? C.4 What utility controls are used to ensure sufficient clarity, meaning, relevance and importance of knowledge resources? C.5 How are best KM control practices recognized/preserved/applied? C.6 Is there technological support for KM control? D.1 How are knowledge resources valued?16
    • D.2 How are processors evaluated? D.3 In what ways are effectiveness of knowledge activities, coordination approaches, knowledge controls, and knowledge management leadership assessed?D. Measurement D.4 What are the impacts of an organization’s KM on its competitiveness and bottom-line performance? D.5 How is effectiveness of these measurement practices gauged? D.6 How are best KM measurement practices recognized/preserved/applied? D.7 Is there technological support for KM measurement? Table 2: Managerial influences on knowledge management episodes (adapted from Holsapple & Joshi, 2003)One of the problems with Table 2 is that some tasks seem to be “meta” knowledgemanagement tasks (e.g., D.6), that is, they are meant to assess the knowledge managementtasks themselves. Though certainly relevant from an organizational perspective, they areprobably outside the scope of knowledge maps for the support of the knowledge managementtasks proper.Summarizing: the Holsapple & Joshi model combines at least three different conceptuallevels, the work-related knowledge management episodes, the managerial influences whichdirectly impinge upon these episodes and the “meta” assessment of these managerialinfluences. In Chapter 5 I will return to how these levels can be handled for knowledgemapping purposes.3.2 The CIBIT modelOriginally CIBIT worked in their consultancy practice with a version of the Wiig et al. (1997)model (see Figure 11), which goes back to a model by Van der Spek & Spijkervet (1996)which is a modified version of the model by Van der Spek & de Hoog (1994),Based on their experiences in many consultancy projects, they created the model shown inFigure 5. The main reasons for this shift were:M E T I S D 4 . 3 17
    • 1) Without a clear focus many knowledge management initiatives are doomed to fail. They drift off into personal hobbies or technology driven “improvements”. 2) The focus must be linked to key performance indicators of an organisation. A knowledge management initiative that cannot show how and what it will contribute to those indicators will not live long.The model consists of three main phases (Focus, Organize and Perform) and one ongoingtask Communicate. Subtasks are linked to questions, for example “What would we like to be”,which in turn are linked to “How” tasks (e.g., “Aligning KM with the business strategy”). Themain difference with the Wiig et al. model can be found in the Focus phase. The model usedby CIBIT contains more details about tasks than the ones shown in Figure 5. However, part ofthis is proprietary.18
    • Figure 5: The CIBIT© modelM E T I S D 4 . 3 19
    • Nevertheless, the operational character of these tasks is used in the KM Quest knowledgemanagement simulation game (Leemkuil et al., 2003). This simulation game is an attempt tooperationalize (parts of) knowledge management to a level where it can be executed by acomputer. This is not the place to give an extensive description of the KM Quest game, butsome screen-dumps will give an indication how it deals with knowledge management tasks.The screen-dump in Figure 6 shows the entry (main) screen of KM Quest. The figure on thecomputer represents the knowledge management task model at the highest level ofabstraction. The characters are short-hands for Focus, Organize, Implement and Monitor. Thewhiteboard represents the state of the organisation in which the knowledge management taskis situated. Shown are three key performance indicators: profit, level of sales and customersatisfaction. In this way the conceptual separation between knowledge management as amanagement task and the results operational (work) processes is visualised. Figure 6: First layer of specification of knowledge management tasksThe knowledge management tasks on the computer screen in Figure 6 can be made morespecific by clicking on them. This will result in the screen-dump as shown in Figure 720
    • Figure 7: Second layer of specification of knowledge management tasksFigure 7 shows the second layer of tasks in the Focus phase. The main goal of this phase isto determine where the main emphasis of knowledge management will be for the comingperiod(s). This is in line with the first observation on the reasons behind the CIBIT model.The next level of specification of a knowledge management task is accessible by clicking onone of the boxes in Figure 7 when the screen shown in Figure 8 pops up.M E T I S D 4 . 3 21
    • Figure 8:Third layer of knowledge management tasksIn Figure 8 one can see the more detailed description of the task “Focus on properties ofknowledge domains” from Figure 7. What is presented is a method or tool to support theexecution of this task. Additional task information is available through the “What to….” and“How to….” buttons. These provide a fourth layer of specification. The “How to….” screenassociated with Figure 8 is shown in Figure 9.22
    • Figure 9: Fourth layer of knowledge management task specificationAs can be seen from Figure 9, additional information is provided about how to perform thetasks as well as about connections to other tasks.The sequence of screen-dumps in Figure 6 to Figure 9, shows how the knowledgemanagement tasks in the KM Quest simulation, which are quite similar to the CIBIT model,are decomposed onto a level where they are supportable by computer based tools. This is notbecause computer based tools are favoured over other ones, but only to demonstrate thelevel of preciseness of the task description. I will return to this issue of task detail in section3.5.3.3 The Wiig et al. modelIn several papers (see for example Wiig et al. (1997) and de Hoog (2000)) I have proposed ageneral framework for conceptualising knowledge management. At the core of this frameworklies the distinction between knowledge management activities and knowledge work activities.Below this framework is described.In economic theory an entrepreneur is seen as a person that configures production factors insuch a way that organisational value is created. As an entrepreneur is just another term for aM E T I S D 4 . 3 23
    • manager, this entrepreneurial brief is equally applicable to management and managers. So ifone wants to take the term “management” in knowledge management serious, this brief has tobe the starting point. This brief consists of a task, “configuring”, and a result, “a specificconfiguration of production factors”. If we want to create a theory for knowledge managementthe first objective is to specify the nature of the task, the result and how they are related.The notion of a knowledge management task and a result, or the subject of the task, can beconveniently shown in a simple figure (see Figure 10). Knowledge management task Actions Status to change of configuration configuration Organisational resources configuration Figure 10: Knowledge management as a task and resource(s) configuration as a resultIn this framework a definition of the knowledge management task entails a more detailedmodel of the upper ellipse in Figure 10. The knowledge management interventions on workprocesses belong to the downward arrow “actions to change organisational resourcesconfiguration”. It should be noted that this framework accepts any definition of knowledgemanagement tasks as long as it keeps the distinction between the upper and the lowerellipses. For example, the JIT knowledge management task as outlined in section 2.1 can betaken as a specification of content of the upper ellipse. Also parts of the Holsapple-Joshimodel can be used to fill it with more detail. Another example of how this upper ellipse can bemodelled is shown in Figure 11 (taken from Wiig et al. 1997).24
    • Inventarisation of Analysis strong & weak External & Internal knowledge & points External & Internal developments organisational context developments Conceptua- lise Evaluation Definition results of required improvements Reflect Review Comparison Planning old and new of im- situation provements Act Development Consolidation of knowledge of knowledge External & Internal Distribution Combination External & Internal of knowledge of knowledge developments developments Figure 11: Knowledge management task model by Wiig et al. (1997).The tasks depicted in Figure 11 are described below.Reviewing means checking what has been achieved in the past, what the current state ofaffairs is. Conceptualise is sitting back and try to get a view on the state of the knowledge inthe organisation and analyzing the strong and weak points of the knowledge household.Reflect is directed towards improvements: selecting the optimal plans for correctingbottlenecks and analyzing them for risks which accompany their implementation. Act is theactual effectuation of the plans chosen previously. Most of the time the actions will be eitherone or a combination of generic operations on knowledge:• develop the knowledge (buy it, learning programs, machine learning on databases)• distribute the knowledge (to the points of action, KBS’s, manuals, network connections• combine the knowledge (find synergies, reuse existing knowledge)• consolidate the knowledge (prevent it from disappearing, KBS’s, tutoring programs, knowledge transfer programs)The lower level tasks from Figure 11 are defined as follows:M E T I S D 4 . 3 25
    • Review • Comparison of old and new situation. Monitoring the performance of an organisation from a knowledge management perspective requires that the appropriate monitoring procedures are in place and operational. These procedures will of course depend on the kind of measures taken earlier and must be tailored to them. • Evaluation results. The monitoring must be evaluated in the light of the original objectives: did the implemented improvement plans led to the results envisioned?Conceptualize • Inventarisation of knowledge and organisational context. One of the most important elements for effective knowledge management is to get a picture of the knowledge in the organisation. This amounts to finding answers to the question what uses the knowledge, which knowledge is used, where the knowledge is used, when the knowledge is used, and which organisational role provides the knowledge • Analysis of strong and weak points. Based on the results from the inventarisation an analysis must be made of the strong and weak points of the current state of the knowledge household. This can be done by subtasks like bottleneck analysis or SWOT approaches.Reflect • Definition of required improvements. The Conceptualize phase will, as has been mentioned above, produce a set of bottlenecks, problems, opportunities, weaknesses etc. for which improvements must be identified. This identification process is of utmost importance and it is absolutely crucial to keep the analysis of problems and bottlenecks apart from the definition of improvements until this stage. After improvements have been identified they must receive a priority, because most of the time they cannot be implemented together due to constraints in time and money. Selection of improvements is thus needed. • Planning of improvements. After improvements have been chosen it is necessary to translate them into operational plans. Most of the time this will amount to starting one or more projects. As each of these projects will be instantiated differently, depending on the context, not much more can be said about them. However, the risks involved in carrying out improvement plans must be carefully assessed.26
    • The Act phase was described already above. It should be mentioned that in the Wiig et al.framework the actions comprising the Act phase are not seen as knowledge management.Most of the actions belong to other domains of expertise, like human resource management,education and training, information- and knowledge engineering, for example. They representthe downward arrows in Figure 10.The authors address the question about the knowledge management specific nature of theirmodel, by enumerating several aspects of knowledge that makes it different from otherorganisational resources:Positive • Growth through use, instead of being consumed. By applying knowledge agents can increase their knowledge by absorbing new insights or by replacing obsolete knowledge by more up-to-date knowledge. At the same time, using the knowledge does not “destroy” it. • Non-rival. Knowledge can be used simultaneously in different processes. • No natural/physical limits. Apart from the energy needed by agents handling knowledge there is no natural or physical limit to the amount of knowledge. • Free from location and time constraints. Knowledge can be applied anywhere and at any time, when needed. It is only weakly tied to a physical substrate other than the agent that embodies the knowledge.Negative • Embodied in agents with a will. Mostly knowledge is embodied in agents with a will of their own: humans. This makes accessibility and applicability sometimes difficult, as the willingness of these agents is an important factor in using the knowledge. • Intangible and difficult to measure. We cannot directly point to “knowledge” as a physical quantity. And as we cannot see it easily we cannot measure it in a straightforward manner. This makes “controlling” it a major challenge. • Volatile. Apart from the fact that knowledge is often embodied in humans, which are free to leave your organisation, sudden discontinuities in social, scientific and organisational areas can make knowledge obsolete overnight. • Value paradox. Boisot (1998) has pointed out that knowledge suffers from a value paradox. In order to extract value from knowledge we have to codify, abstract and diffuse it. But in this process knowledge will loose it’s scarcity and as a consequence the opportunity to extract value from it.M E T I S D 4 . 3 27
    • • Long lead times. Knowledge cannot be conjured out of a hat. Mostly it takes years to build expertise in a certain domain. This requires long term planning.In their perspective knowledge management should be concerned with maximizing thepositive aspects and minimizing the negative ones. The resource view implies that themeasure of success is the providing of knowledge to organisational processes that need it, atthe right time, in the right shape, at the right location, with the required quality against thelowest possible costs, in order to achieve organisational goals.3.4 The Duineveld et al. modelThe last model discussed in this chapter is a mixture of the CIBIT model (as expanded in theKM Quest simulation environment) and the Wiig et al. model. It was developed by Duineveldet al. (2001) in the context of the EU funded IBROW project. Figure12 shows their model. Figure 12: The Duineveld et al. modelFigure 12 is quite similar to Figure 11 up to some re-labeling of tasks. It also containselements (e.g., Focus) which are derived from the CIBIT model (see Figure 5). The model isbased on a comparison of 16 papers containing knowledge management process models.A more interesting part of their work is the Knowledge management task ontology theypropose (see Figure 13). As this is presented as a formal ontology, implying a hierarchicalstructure, the sequence from Figure 11 is lost. Nonetheless, the tree consists of a list of tasksthat has similarities with the list proposed by Holsapple (see section 3.1). The list is also moredetailed than the list accompanying Figure 11. However, the paper itself lacks a more precisedescription of what these tasks entail.28
    • Figure 13: The knowledge management ontology by Duineveld et al.Another thing that is lacking is a rationale for the grouping of these tasks. Interestingly, thetask “Combination” has no subtasks. This is an indication that this is one of the more difficultthings to imagine.3.5 Selection/construction criteria for a knowledge management model and knowledge management tasksIn this chapter and Chapter 2 several knowledge management models and tasks werepresented and sometimes criticized. This raises the question what a “good” model and “good”tasks are. One answer is to carry out empirical research to determine either the normative ordescriptive “goodness”. In the context of the Metis knowledge mapping task this not a goodsolution at the moment. The use of the knowledge management task ontology is for enrichingthe to be designed knowledge cockpit and only when this enriching has taken place empiricalresearch into the quality can take place. This implies that one has to select/construct a modelfirst, which brings one back to the question about what is “good”. Below I will present andM E T I S D 4 . 3 29
    • discuss several criteria that seem to me to be important. Of course they are subjective, butthat holds for all criteria. As the ultimate arbiter is reality, it is better to make a choice first andtest it, instead of discussing forever thecriteria.The following criteria seem to me important for selecting/constructing knowledge managementmodels and tasks.CyclicThere can be no doubt about the fact that knowledge management is an ongoing concern,just like other forms of management. Management only stops when the managed processesdisappear. This criteria implies that cyclic models, i.e. models that have a loop structure, areto be preferred over linear (one way) models (see for an example Tiwana, 2000, this has been ndmodified in the 2 Edition (Tiwana, 2003)).Knowledge specificWhen discussing the models, several times the issue was raised whether the model isapplicable to managing any organisational resource or specific for knowledge management.The more knowledge management specific the better it seems, because this is a way to provethat knowledge management is different from resource management in general (if it is!). Thiscan be achieved in at least two ways: 1) by including management tasks that only make sense in a knowledge management context; 2) by setting goals for knowledge management tasks which are derived from unique properties of knowledge as an organisational resource.Thus models following one, or preferably both, ways are to be preferred over models that omitthem.Separate management from workThough this may be my pet, I still feel that is extremely important to make a conceptualdistinction between doing the work and managing the work, even if they are often closelyintertwined. Driving the bus is not the same as managing the bus company, though bothbelong to the same domain. The most straightforward analogy is with project management.The project manager performs tasks like making a work-breakdown, estimating effort andresources, scheduling project tasks, monitoring outcomes and dealing with events. The workconsists of, for example, writing code, gathering requirements, building UML models andtesting the code. This distinction is also reflected in the kind of tools that are available to thedifferent tasks. MS Project®, for example, is designed to support the project managementtasks, while programming languages, such as Java, are designed to support the developmenttasks. Mixing the two will lead to immense confusion in a project.30
    • Appropriate grain sizeKnowledge management tasks can be described at different levels of specificity (see, forexample, Figures 6-9). The question is “What is the appropriate level” which, of course,passes the bucket to what we mean with “appropriate”. People working with taskdecompositions are very aware of this nasty problem. A task like “Boiling water” can bedescribed as “Fill a kettle”, “Light the fire”, “Wait till the kettle whistles”, but the “Light the fire”task can be described in more detail, e.g., “Take the box with matches”, “Take a match fromthe box”, “Light the match”, “Turn the knob of the stove” etc.Unfortunately there is no general solution to this problem. The overriding idea is to take thecompetence of the executor of the task into consideration. Most of the time this leads to moregeneral task descriptions for more competent executors, simply because we can rely on theexecutor to “fill in” the details. A variant of this principle is proposed by Lauesen (2003). Hiscriterion for a task is closure: the user must feel he or she has achieved something when thetask is complete. This is almost equivalent to saying that meaningful task descriptions musthave attached to them at least one goal of which one can more or less unambiguouslyestablish whether it was achieved or not.However, as will be discussed in the next chapter, sometimes a “bottom up” approach canhelp. This takes tasks of a fairly low grain size as a starting point and tries to cluster them intomeaningful larger tasks.Tool independentOne the problems in knowledge management is the inclination of tool vendors to position theirtool as “the“ knowledge management tool. This generates much confusion, the more sobecause it is an illusion that one tool can cover all aspects of knowledge management. Thus aknowledge management process model should be tool independent. This does not imply thatthere can be no tools that can be used to support (sub)tasks. One of the long-term goals ofknowledge management research and development is design and implement a rich repertoireof tools. But these tools must be seen as “plug in” tools that can be selected at will by anyoneinvolved in knowledge management processes.Time horizonFrom experience it is known that knowledge management tends to come in two flavours: shortterm and event driven, or strategic and driven by long-term goals. A knowledge managementprocess model must accommodate both flavours to be applicable in organisations.In Chapter 5 I will review the proposed knowledge management – knowledge properties onthese criteria.M E T I S D 4 . 3 31
    • 4 Knowledge properties As has been said before, knowledge management tasks and properties of knowledge about which data is needed to perform these tasks, are inextricably intertwined. In Chapter 2 in the context of knowledge mapping, it emerged that most of this mapping was based on properties of the knowledge domain (i.e. content), which may be less relevant for knowledge management tasks (see for example the data base schema in Table 1). In this chapter I will review several proposals from the literature that enumerate these properties. Fortunately Holsapple (2003) has already written another seminal paper in which he gives an overview of properties of knowledge that have been proposed in the literature.I will briefly enumerate them in Table 3. Property Range/definition Mode Tacit vs explicit Type Descriptive vs procedural Domain Domain where knowledge is used Orientation Domain vs relational vs self Applicability From local to global Management level Operational vs control vs strategic Usage Practical vs intellectual vs recreational vs spiritual vs unwanted Accessibility From public to private Utility Progression of levels from a clear representation to one that is meaningful, to one that is relevant, to one that is important Validity Degree of accuracy or certainty Proficiency Degree of expertise embodied in knowledge Source Origin of knowledge 32
    • Immediacy Latent vs currently actionableAge From new to established to oldPerishability Shelf-life of knowledgeVolatility Degree to which knowledge is subject to changeLocation Position of knowledge (e.g ontological, organisational, geographic locus)Abstraction From concrete to abstractConceptual level Automatic vs pragmatic vs systematic vs idealisticResolution From superficial to deepProgrammability Degree to which knowledge is transferable or easy to useMeasurability Degree to which knowledge can be measuredRecursion Knowledge vs meta-knowledge vs meta- meta-knowledge Table 3 : Knowledge properties taken from Holsapple (2003).Though the author states that this list contains “potential levers for practitioners to wield intheir KM efforts” (p.186) and mentions that it can be an addendum to the ontology ofknowledge resources (see section 3.1), he does not clearly link these properties to knowledgemanagement tasks. The list is also rather disorganized, it could be structured not only bylinking it to knowledge management tasks, but also by grouping similar properties under amore general heading. For example properties like Type, Management level, Abstraction,Resolution and Recursion are all referring to general content properties which do not dependon a particular domain. Properties like Usage, Origin, Immediacy and Location seem to bemore connected to the organisational aspects of knowledge. What is also lacking inHolsapple’s paper is an indication of the grain size of knowledge, the “chunk” of knowledgeone wants to characterize with these properties.M E T I S D 4 . 3 33
    • The grain size problem is tackled in the paper by Wiig et al. (1997), based on earlier work byWiig. Table 4 shows a hierarchy of knowledge ranging from very general to very specific.Knowledge Span ExamplesKnowledge domain Domains 1. Internal Medicine 2. Mechanical Engineering 3. Business ManagementKnowledge Region Regions 1. Urology 2. Automotive Mechanical Design 3. Product MarketingKnowledge Section Sections 1. Kidney diseases 2. Transmission Design 3. New Product PlanningKnowledge SegmentsSegment 1. Diagnosis of kidney diseases 2. Gear Specification and Design 3. Product MarketabilityKnowledge Element Elements 1. Diagnostic strategies, such as “When considering which disease is present, first collect all symptoms, then try to explain as many of them as possible with one disease candidate”Knowledge FragmentsFragment 2. “If the symptom is excruciating pain, then consider kidney stone” 3. “When there are too many gears in the transmission, the energy loss will be excessive”Knowledge atom 1. “Excruciating pain is a symptom” 2. “Use case hardening of gear surfaces in pressure range 4” Table 4: Hierarchy of knowledge description levels (from Wiig et al., 1997)A comparison between Tables 3 and 4 shows that not all properties listed in Table 3 can bemeaningfully attached to every description level in Table 4. Thus selecting properties isconditional on selecting a level in the hierarchy in Table 4 first. This is not easy to do in a waythat is applicable and valid for many contexts. Based on experiences in practical situations, Ifound that the region/section/segment level is most appropriate for short term knowledgemanagement. The lowest three are mainly relevant for knowledge engineering, the domainlevel is probably more suitable for strategic knowledge management. However, this is notsomething that one should take for granted in every situation.34
    • Based on the choice for the region/section/segment level as being the most appropriate, Wiiget al. (1997) give another table (a knowledge description frame) that contains properties(called “identifiers”) of knowledge (see Table 5). Name: the name of the knowledge asset (at segment or section level Domain: the knowledge domain to which the asset belongs Business processes: the business processes in which theGeneral identifiers knowledge asset is used as a resource Organisational role the organisational role to which the knowledge asset is usually attached Current agents: agents (persons, computer programs, books etc.) carrying the knowledge asset at the moment of analysis Nature: the characteristics of the knowledge asset in terms of quality (heuristic, formal, complete, under developmentContent identifiers etc.) Current proficiency levels: the level of proficiency at which the knowledge asset is available to the organisation Stability: the rate of change of the content (fast, slow etc.) Time: when the knowledge asset is available for business processes (e.g., working days from 9-5) Location: the physical location of the knowledgeAvailability identifiers asset (e.g., the main office, department of mortgages) Form: the physical and symbolical embodiment of the knowledge asset (paper, in a computer program, in the mind of an agent etc., language, format etc.) Table 5: Knowledge description frame taken from Wiig et al. (1997)Several properties from Table 5 can also be found in Table 3, which indicates that they areseen as important. A combination of Table 3 and Table 5 will give a fairly complete overviewof relevant properties. This is shown in Table 6, which uses a refined categorization describedbelow.The following main categories can be discerned: • General/organisational: properties that reflect the organisational embedding of the knowledge • Content: properties of the content which are not related to the domain of the knowledge, but can be seen as properties that are applicable across all domainsM E T I S D 4 . 3 35
    • • Use: properties indicating aspects that influence the use of the knowledge in the organisation • Availability: properties related to physical and logical positioning of the knowledge Name: The name of the knowledge “chunk” Domain: The knowledge domain to which the knowledge “chunk” belongs Business processes The business processes in which the knowledge is used as a resource Role: The organisational role to which the knowledge isGeneral/organisational usually attached Current agents: Agents (persons, computer programs, books etc.) carrying the knowledge at the moment of analysis Perishability: The degree to which the knowledge can be kept in the organisation Age: The time the knowledge is present in the organisation Nature: The characteristics of the knowledge in terms of quality (heuristic, formal, complete, under development etc.) Type: The methodological characterization of the knowledge (causal, descriptive, procedural) Management perspective: The knowledge management perspective for which the knowledge is relevant (operational, control, strategic) Abstraction: The degree to which the knowledge is general or specific36
    • Content Resolution: The degree to which the knowledge is superficial or deep Recursion: The distance between the domain of application and the knowledge (knowledge vs meta-knowledge) Conceptual level: The categorization of the knowledge in terms of automatic, pragmatic, systematic or idealistic Validity: The degree to which the knowledge has been proven Stability: The rate of change of the content (fast, slow etc.) Proficiency: The level of proficiency at which the knowledge is available to the organisation Applicability: The range in which the knowledge is used in the organisation (local vs global) Usage: The prevalent way of using the knowledge (practical, intellectual) Utility: The value of the use of the knowledge for theUse organisation Immediacy: The degree to which the knowledge can be immediately employed (latent vs currently actionable) Transferability:: The degree to which the knowledge can be transferred between processes and agents Measurability: The degree to which to knowledge lends itself to be measuredM E T I S D 4 . 3 37
    • Time: When the knowledge is available for business processes (e.g., working days from 9-5) Location: The physical location of the knowledge (e.g., the main office, department of mortgages)Availability Form: The physical and symbolical embodiment of the knowledge (paper, in a computer program, in the mind of an agent etc., language, format etc.) Accessibility: The roles, persons and organisations that can have access to the knowledge Mode: Tacit vs explicit Table 6: Summary of important knowledge propertiesHowever, Table 6 does not tell the whole story as it does not address the more dynamicaspects of knowledge processes in organisations.The KM Quest knowledge management simulation game discussed in section 3.2 includes anexecutable model of the behaviour of a fictitious organisation in which the knowledgehousehold must be managed. In the screen dumps shown in Figures 6 and 7 one can see thatone of the knowledge management tasks is to monitor the state of the knowledge household.This monitoring requires information concerning the knowledge, or, in other words indicatorsor properties. To support this monitoring a rich set of indicators is included. Below these willbe discussed briefly. However, just as for the previous proposals, one should keep in mindthat defining a property or indicator is not the same as measuring it. Thus when reading theelaboration of the KM Quest properties and how they are displayed it is important to realizethat in the system the measurements are assumed to exist, which in practice may be quitedifficult to do.As the fictitious company is characterized as a product leadership organisation (Treacy &Wiersema, 1995), the crucial knowledge domains in the organisation are marketing,production and R&D, so these are represented in the category knowledge related variables.For these domains there are several core knowledge processes which merit attention (basedon Probst et al., 1999; Wiig et al., 1997; Choo, 1998): Knowledge gaining: a measure for the acquisition of knowledge from outside the organisation Knowledge development: a measure for the internal development of knowledge38
    • Knowledge utilisation: a measure for how the knowledge is actually used Knowledge retention: a measure for the success with which loss of knowledge is prevented Knowledge transfer: a measure for the transfer of knowledge between the three domains, which is also crucial for product leadership organisationsThese core knowledge processes are similar to several knowledge management tasks asdescribed in Chapter 3. Several occur, for example, in the Holsapple-Joshi model, the Wiig etal. model and the Duineveld et al. model, which is not too surprising as the identification ofthese processes was based on a study of the literature. These core knowledge processes arecharacterized by three properties reflecting their quality: • Speed: the time needed for the process • Effectiveness: the actual result of the process • Efficiency: the "cost"/"benefit" ratio of the previous two propertiesKnowledge domains, core knowledge processes and quality properties of the latter, form athree-dimensional space representing the knowledge management focus. This results in anextensive list of indicators which are triplets of the type <knowledge domain, process,property>, for example <marketing,development,speed>. Some theoretical combinations arenot meaningful, like those combining “retention” and “speed”, these are excluded.In addition the KM Quest approach assumes that the state of these knowledge processesinfluence the level of competence in the three knowledge domains. To the list of indicators areadded three indicators for the levels of competence. Together this leads to a list of 42indicators. Of course it will not be easy to actually measure several of these indicators. Forexample, something like “effectiveness of knowledge utilisation in marketing” can be hardlymeasured in a straightforward way. However, with some creativity one could come up with atleast some proxy measures, just like the way other authors (Sveiby, 1997, Edvinsson &Malone, 1997) use these for rather abstract concepts.In the KM Quest these indicators are displayed together in a “knowledge map” which can beseen as a kind of “knowledge cockpit” that allows a knowledge manager to monitor the stateof the knowledge household. Figure 14 below is a screen-dump from the KM Quest systemshowing the knowledge map.M E T I S D 4 . 3 39
    • Figure 14: The knowledge map in the KM Quest systemThe window in Figure 14 shows the knowledge map at the beginning of the game. The threelarge squares represent the knowledge domains (Marketing, Production and Research). Eachsquare is divided into six rectangles, one for each knowledge process. These rectangles aretagged with a symbol that is explained at the right hand side. Within each rectangle the valueof two properties of each process is shown. Effectiveness by means of a colour code, green isfine, red is bad. Speed by means of abbreviations, for example “S=vf” means “speed is veryfast”. Adding also the “efficiency” of the processes to the map would clutter it too much. Thevalue of these indicators is shown in a graph that displays together the efficiency of allprocesses for a knowledge domain (see Figure 15).40
    • Figure 15: Displaying efficiency of knowledge processes in MarketingIn Figure 15 the user can tailor the graph by toggling the checkboxes at the right hand side.Removing the “ ” at utilisation will delete the “utilisation” line from the graph. ٧Experiments with the KM Quest system have shown that having access to these “knowledgemaps” contribute to the capability of players to achieve good results.When I summarize the three examples of how to deal with properties of knowledge which arerelevant for knowledge management, two conclusions seem to be warranted: 1. Properties and indicators should combine static and dynamic aspects. The properties proposed by Holsapple and Wiig et al. and summarized in Table 6, mainly address static aspects. Though they can, of course, change their values over time, they are attached to “chunks” of identifiable knowledge. The properties included in the KM Quest system focus mainly on the dynamics of knowledge processes. 2. Identifying knowledge management tasks can also proceed in a “bottom up” fashion. One can rephrase each indicator as a task by stating that taking care of that indicator is a knowledge management task, for example, taking care of the speed of knowledge transfer between research and marketing. By clustering and sequencing these tasks one can arrive at a knowledge management process model. This is evident from the fact that the knowledge processes in the KM Quest system occur as knowledgeM E T I S D 4 . 3 41
    • management tasks in several knowledge management process models described in Chapters 2 and 3.Following the second observation, I will use in the next chapter a combination of the “topdown” (from Chapter 3) and the “bottom up” (from this chapter) approach to construct aknowledge management-knowledge properties model that can serve as a specification forknowledge management focused knowledge mapping.42
    • 5 A knowledge management – knowledge properties model for knowledge mapping In the previous two chapters I have presented quite some information referring to knowledge management process models and knowledge properties. In this chapter a synthesis will be presented. Several criteria for such a synthesis were discussed in section 3.5. The synthesis will consist of three parts: • characteristics of knowledge that sets it apart from other organisational resources which should be the main concern of knowledge management (the unique aspects for knowledge management), • organisational goals that should be attained with knowledge management, • the knowledge management-knowledge properties model that should support the first two aspects 5.1 Characteristics of knowledge Though there may be other, probably more epistemologically oriented, characteristics of knowledge, I think that the ones enumerated in section 3.3 are relevant for the present purpose. In Table 7 below I first list all the positive properties and indicate which tasks and properties are related to each of them. In Table 7 I’m mapping the tasks from Chapter 3 on these characteristics. In the “Tasks” column, in each cell the section from Chapter 3 is indicated by the section number, followed by the tasks from that chapter that seem to address that particular characteristic. Before embarking on this, I must say a few words on how to handle the Holsapple-Joshi model. If I apply the criteria put forward in 3.5 it can be argued that only the part of that model dealing with managerial influences (see Table 2) can be said to satisfy the criterion of separation between management and work. The knowledge manipulation activities as depicted in Figure 4 basically can be seen as belonging to the “work” aspect. However, there are several ambiguities in Figure 4 and the resulting task decomposition as presented in 3.1. One could argue that several tasks from Figure 4 can also be interpreted as being more general knowledge management tasks, instead of only knowledge manipulation activities performed in the context of a knowledge management episode. For example, the internalizing task is described as “incorporating or making the knowledge part of the organization”, which can hardly be seen as related only to a work task. This holds more or less also for the tasks selecting and acquiring. When it comes to finding examples of both types of activities Holsapple is not very consistent either. In Holsapple & Singh (2003) empirical evidence is presented on the efficacy of the activities, based on a review of the (knowledge) management literature. The cases described can quite often be seen as belonging to either the “episode” part or to the “managerial influences” part. In order to deal with this ambiguity I have decided to incorporate both perspectives in the tables, leaving it to the prospective user to choose between them or accept them both. The entries M E T I S D 4 . 3 43
    • for the tasks from Figure 3 and Table 2 are made from my interpretation as seeing them asprimarily knowledge management (related) tasks.The entries and coding in the tables are as follows: • 3.1 Holsapple-Joshi model (knowledge manipulation activities, managerial influences) • 3.2 CIBIT/KM Quest model • 3.3 Wiig et al model • 3.4 Duineveld et al. modelFor tasks the notation convention is “name-of-general-task(subtasks)”. For example“generating(transferring)” means the generating task with the subtask transferring fromsection 3.1. Tasks taken from the Holsapple-Joshi model’s managerial influences areindicated by their codes as given in Table 2. For properties the same convention is used, butthis is only relevant for process-oriented properties from the KM Quest system. Otherproperties are taken from Table 6.It should be noted that this classification is based on my interpretation and must be seen astentative. Moreover, as will become clear, in many cases a unique classification cannot beachieved. Several tasks and properties can belong to more than one class.Positive characteristics Tasks PropertiesGrowth 3.1 Generating(producing), stability, usage, A6, B8, C5, D6 development(speed, effectiveness) 3.2 Organize(how to improve the knowledge and learning infrastructure), Focus(Which knowledge areas should we focus on?) 3.3 Conceptualize(analysis of strong and weak points), Development, Combine 3.4 Gaining(acquire,capture), Combination44
    • Non-rival 3.1 Internalizing(delivering), transferability, immediacy, Acquiring(organizing), accessibility, mode, Internalizing(targeting), applicability, abstraction, Internalizing(delivering), utilisation(effectiveness), Generating(transferring), B1, transfer(speed, B2, B4 effectiveness) 3.2 Organize(how to improve the knowledge and learning infrastructure) 3.3 Distribute 3.4 Distribution(identify source), Distribute(identify destination)No physical limits 3.1 None None 3.2 None 3.3 None 3.4 NoneFree from time/location 3.1 Largely the same as Non- time, location,constraints rival, B3, B4 transfer(speed) 3.2 Organize(how to improve the knowledge and learning infrastructure) 3.3 Distribute 3.4 Distribution(identify source), Distribute(identify destination) Table 7: Positive characteristics of knowledge and their relation with tasks and propertiesM E T I S D 4 . 3 45
    • In Table 8 below, the same procedure is followed for the negative characteristics.Negative characteristics Tasks PropertiesAgents with a will 3.1 A2, A3, B6, D2 accessibility, immediacy, role, current agents, form, 3.2 Organize(How to make it proficiency, perishability happen?) 3.3 Reflect(planning improvements), 3.4 Utilization(monitor use), Utilization(stimulate use)Intangible 3.1 Acquiring(capturing), mode, accessibility Generating(monitoring), B4, D1 3.2 None 3.3 Conceptualize (inventarisation) 3.4 Gaining(acquiring, capturing)Volatile 3.1 Internalizing(delivering), retention C2 3.2 Organize(how to improve the knowledge and learning infrastructure) 3.3 Consolidate 3.4 Consolidation(retention)46
    • Value paradox 3.1 Externalizing(producing) utility, transferability 3.2 None 3.3 None 3.4 NoneLong lead times 3.1 Acquiring(locating), age, gaining(speed, Generating(monitoring) effectiveness) 3.2 Organize(how to improve the knowledge and learning infrastructure) 3.3 Conceptualize(analysis of strong and weak points) 3.4 Gaining(identify needed knowledge), Utilization(monitor needs) Table 8: Negative characteristics of knowledge and their relation with tasks and properties5.2 Knowledge management goalsFor knowledge management goals, the same kind of table can be constructed as in section5.2. I will also use the list of goals discussed in section 3.3 (see Table 9 below). In the samevein, I will use tasks and properties from Chapters 3 and 4, just as in Tables 7 and 8.Goal Tasks PropertiesTime 3.1 Acquiring(transferring), time Internalizing(delivering), Generating(transferring), B1, B2, B3, B4, B7 3.2 Organize(how to improveM E T I S D 4 . 3 47
    • the knowledge and learning infrastructure) 3.3 Conceptualize (inventarisation), Conceptualize(analysis of strong and weak points), Distribute 3.4 Distribution(identify source), Distribute(identify destination)Shape 3.1 Internalizing(structuring), form Acquiring(organizing), C4 3.2 None 3.3 Conceptualize (inventarisation), Conceptualize(analysis of strong and weak points) 3.4 Distribute(customize), Distribute(choose format), Consolidate(structure)Location 3.1 Generating(transferring), location Acquiring(transferring), B1, B2, B3, B4 3.2 Organize(how to improve the knowledge and learning infrastructure) 3.3 Conceptualize (inventarisation), Conceptualize(analysis of strong and weak points), Distribute48
    • 3.4 Distribution(identify source), Distribute(identify destination)Quality 3.1 Generating(evaluating), nature, resolution, validity, Acquiring(identifying), proficiency, conceptual level Internalizing(assessing and valuing), Generating(evaluating), C1, C2, C3 3.2 None 3.3 Conceptualize(analysis of strong and weak points), Distribute 3.4 Consolidate(validation), Consolidate(check consistency with existing knowledge), Maintain(verify), Maintain(correct mistakes), Maintain(update), Maintain(version control) Gaining(acquire, validate)Lowest costs 3.1 Acquiring(valuing), utility Internalizing(assessing and valuing), D1, D2 3.2 None 3.3 None 3.4 NoneAchieving organisational 3.1 D4 None directly for knowledge,goals for goals in generalM E T I S D 4 . 3 49
    • measures like profit, 3.2 Focus(What would we customer satisfaction, market like to be?), Focus(Which share etc. performances would we like to improve?) 3.3 Conceptualize(Analysis of strong and weak points) 3.4 None Table 9: Organisational goals and their relation with knowledge management tasks and properties of knowledgeTables 7-9 can be summarized in a table that shows how “well” characteristics and goals arecovered by tasks and properties. An overview of this may contain clues about areas whereadditional work in terms of defining knowledge management tasks and knowledge propertiesis needed. This is shown in Table 10.Characteristic/goal Number of tasks Number of propertiesGrowth 12 4Non-rival 12 9No physical limits 0 0Free from time/location 11 3constraintsAgents with a will 8 7Intangible 7 2Volatile 5 1Value paradox 1 2Long lead times 6 350
    • Time 14 1Shape 8 1Location 12 1Quality 16 1Lowest costs 5 1Achieving organisational 4 n.a.goals Table 10: Summary of coverage of characteristics and goalsIn Table 10 the numbers in the cells corresponding with the properties and goals should notbe taken literally. They are based on the single indicators in Table 9. With some effort moreindicators from Table 6 could have been added. It is somewhat surprising to see thatconcerns, which are particularly relevant for higher management (lowest costs, achievingorganisational goals, value paradox), are less well covered than other issues. Also, volatilityseems to be underrepresented, which seems to be out of synch with all the known effortsspent on knowledge repositories.5.3 Summary and reflectionThe three tables in the previous two sections together form an initial knowledge management-knowledge properties model. The interesting aspect is that, compared to the original startingpoint, identifying tasks leading to goals leading to relevant properties for knowledge mapping,the tables also permit a reversal of goals and tasks and properties. From a design viewpointthis means that access to relevant properties of knowledge can be achieved in two ways: • Identify a goal or several goals, find the associated task(s) and the needed knowledge properties. For example: if the main concern is about capitalizing on non-rivalness and location, then the relevant tasks and properties can be read from the cells in Tables 7-9. • Identify a task or several tasks and find the needed knowledge properties from the cells in the last column of Tables 7-9.One can even start with a property and derive which tasks and goals could be associated withthat property.If I compare this initial model with the criteria put forward in section 3.5 the conclusions belowappear to apply.M E T I S D 4 . 3 51
    • Cyclic: This model is clearly a static model. It does not impose a sequence of goals and/ortasks. The question is whether for knowledge mapping purposes such a sequence is neededas long as it is emphasized that knowledge management is an ongoing activity.Knowledge specific: The characteristics leading to goals are very knowledge specific.Several tasks also make only sense in the context of knowledge (structuring, transforming,validating, consistency, etc.).Separate management from work: The majority of the tasks are management tasks, thoughthere are some boundary cases, in particular for the Holsapple-Joshi model.Appropriate grain size: The tasks derived from the literature differ in grain size. As aconsequence the tasks in Tables 7-9 differ also. It seems that tasks derived from theHolsapple-Joshi model and the CIBIT/Expanded KM Quest (not completely shown) have thebest grain size.Tool independent: The tasks in Tables 7-9 can be supported by a wide variety of tools.Future work in Metis could be directed to adding a fourth column to these tables, indicatingthe availability, and maybe the evaluation, of tools.Time horizon: The Tables 7-9 are not particularly strong in supporting more long term, orstrategic, aspects. For this they rely for the major part on the CIBIT/KM Quest approach.Summarizing, the initial model meets several of the criteria, but can be improved. This can beone of the future activities in the framework of the Metis project. At the same time, it will benot too difficult to build a simple demonstrator of a tool that “animates” Tables 7-9. However,one should remember that identifying properties is not the same time as actually measuringthem. To build a useful operational tool, quite some effort is needed to find suitable indicatorsfor these properties that can be measured in a reliable, cost-effective and non-threateningway. Only if this work is completed we can start talking about a “real knowledge managementcockpit”.52
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