Knowledge Management: Problems, Promises, Realities, and ... · Knowledge Management Knowledge...

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60 1094-7167/01/$10.00 © 2001 IEEE IEEE INTELLIGENT SYSTEMS K n o w l e d g e M a n a g e m e n t Knowledge Management: Problems, Promises, Realities, and Challenges Gerhard Fischer and Jonathan Ostwald, University of Colorado Those who cannot remember the past are condemned to repeat it. George Santayana Innovation is everywhere; the difficulty is learning from it. —John Seeley Brown T he first quote reflects the motivation underlying traditional knowledge management (KM), in which the goal is to store information from the past so that lessons will not be forgotten. This perspective implies that future information needs will be the same as past needs. Consequently, this perspective treats knowledge workers as passive recipients of information. The second quote more closely reflects a design perspective 1 of knowledge management. In this per- spective, knowledge workers constantly create new knowledge as they work. KM’s goal is to enable innovative practice at an organizational (community) level by supporting collaboration and communica- tion among knowledge workers in the same domain and across domains. This article explores the design perspective’s implications for KM. We examine the major prob- lems our approach must address, the promises it offers, the realities we have explored in our work, and the continuing challenges. Table 1 summarizes the article’s key ideas. A basic framework KM is a cyclic process involving three related activities: creation, integration, and dissemination (see Figure 1). In this model, computation supports human knowledge activities by manipulating information. An information repository stores information that was created in the past and is disseminated through- out an organization or group. We can classify KM approaches according to how they perform these basic activities. For example, different approaches might store different kinds of information, support different people to create information, or employ dif- ferent mechanisms and strategies to disseminate information. In traditional KM approaches, management col- lects and structures an organizational memory’s con- tents as a finished product at design time (before the organizational memory is deployed) and then dis- seminates the product. Such approaches are top- down in that they assume that management creates the knowledge and that workers receive it. Our design perspective is an alternative that relates working, learning, and knowledge creation. In this framework, workers are reflective practi- tioners, 2 who struggle to understand and solve ill- defined problems. Learning is intrinsic to problem solving, because problems are not given but must be framed and solved as a unique instance. This perspective has two essential aspects. First, work- ers, not managers, create knowledge at use time. Second, knowledge is a side effect of work. Table 2 compares the traditional KM perspective with our perspective. The authors’ knowledge management approach assumes that knowledge is not a commodity but that it is collaboratively designed and constructed. Authorized licensed use limited to: IEEE Xplore. Downloaded on February 12, 2009 at 23:43 from IEEE Xplore. Restrictions apply.

Transcript of Knowledge Management: Problems, Promises, Realities, and ... · Knowledge Management Knowledge...

Page 1: Knowledge Management: Problems, Promises, Realities, and ... · Knowledge Management Knowledge Management: Problems, Promises, Realities, and Challenges Gerhard Fischer and Jonathan

60 1094-7167/01/$10.00 © 2001 IEEE IEEE INTELLIGENT SYSTEMS

K n o w l e d g e M a n a g e m e n t

Knowledge Management:Problems, Promises,Realities, andChallengesGerhard Fischer and Jonathan Ostwald, University of Colorado

Those who cannot remember the past are condemned to repeat it. —George Santayana

Innovation is everywhere; the difficulty is learning from it. —John Seeley Brown

T he first quote reflects the motivation underlying traditional knowledge

management (KM), in which the goal is to store information from the past so

that lessons will not be forgotten. This perspective implies that future information

needs will be the same as past needs. Consequently, this perspective treats knowledge

workers as passive recipients of information.The second quote more closely reflects a design

perspective1 of knowledge management. In this per-spective, knowledge workers constantly create newknowledge as they work. KM’s goal is to enableinnovative practice at an organizational (community)level by supporting collaboration and communica-tion among knowledge workers in the same domainand across domains.

This article explores the design perspective’simplications for KM. We examine the major prob-lems our approach must address, the promises itoffers, the realities we have explored in our work,and the continuing challenges. Table 1 summarizesthe article’s key ideas.

A basic frameworkKM is a cyclic process involving three related

activities: creation, integration, and dissemination(see Figure 1).

In this model, computation supports humanknowledge activities by manipulating information.An information repository stores information thatwas created in the past and is disseminated through-out an organization or group. We can classify KM

approaches according to how they perform thesebasic activities. For example, different approachesmight store different kinds of information, supportdifferent people to create information, or employ dif-ferent mechanisms and strategies to disseminateinformation.

In traditional KM approaches, management col-lects and structures an organizational memory’s con-tents as a finished product at design time (before theorganizational memory is deployed) and then dis-seminates the product. Such approaches are top-down in that they assume that management createsthe knowledge and that workers receive it.

Our design perspective is an alternative thatrelates working, learning, and knowledge creation.In this framework, workers are reflective practi-tioners,2 who struggle to understand and solve ill-defined problems. Learning is intrinsic to problemsolving, because problems are not given but mustbe framed and solved as a unique instance. Thisperspective has two essential aspects. First, work-ers, not managers, create knowledge at use time.Second, knowledge is a side effect of work. Table2 compares the traditional KM perspective withour perspective.

The authors’

knowledge

management

approach assumes that

knowledge is not a

commodity but that it

is collaboratively

designed and

constructed.

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CreationKM approaches exist because work is

increasingly information intensive. Tradi-tional KM approaches assume that the criti-cal issue for workers is to find the “answers”in organizational memory that apply to thecurrent problem. A design-based approachassumes that the organizational memory willnot contain all the knowledge required tounderstand and solve such problems. So,workers must create new knowledge.

IntegrationIn the design perspective, an organizational

memory plays two roles. First, it is a sourceof information to help workers understand theproblems they face. Second, it is a receptaclefor new information and products created dur-ing work. In traditional KM approaches,knowledge engineers carefully craft a knowl-edge base that will periodically be updated.In a design-based approach, organizationalmemory is a continuously evolving informa-tion space that is fed directly by the knowl-edge created during work. So, informationrepositories and organizational memories are

not huge, impenetrable “write-only” stores.They are actively integrated into the workprocess and social practices of the commu-nity that constructs them.

Although the problems workers solve areunique in some aspects, they are also are sim-ilar to those previously solved. The challengefor knowledge integration is to make the con-nections between old and new knowledge so

that the organizational memory improves itsability to inform work. In the traditional KMapproaches, this was the knowledge engi-neer’s job. In a design-based approach, usersdo it at use time.

Knowledge integration comprises twotasks:

• Conceptual generalization—relating infor-mation from one context to informationfrom another.

• Representational formalization—puttinginformation in a form such that computa-tional mechanisms can access and inter-pret it.

Both tasks require effort beyond what mostworkers consider their core responsibility.Conceptual generalization requires an under-standing of the domain, while formalizationrequires the ability to map from domain con-cepts into the formalizations the systemrequires. A major concern for our design-based approach is to capture informationfrom the work process without extra effortby the users and then to help them formalize

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Table 1. Article overview.

Knowledge creation Knowledge integration Knowledge dissemination

Key idea Knowledge is a work product, Workers integrate new knowledge into Workers get information in the not an existing commodity. repositories at use time. context of work, not in the classroom.

Problems Creating shared understanding Putting communities in charge Alleviating information overloadExternalizations create shared Users must be empowered to manage their The limiting resource for knowledge workunderstanding. own information (and environments). is not information but attention.

Promises Social creativity Living organizational memory Attention economyWorkers are informed participants Information repositories are evolved by Information is delivered to workers when itin the creation of knowledge, not unself-conscious cultures of design. is relevant to their specific needs.consumers of prepackaged information.

Realities Envisionment and Discovery Collaboratory DynaSites Domain-Oriented Design EnvironmentsBoundary objects support communities of Open information spaces are evolved by Design tools and information repositories areinterest to build shared understanding. users with system support for integration. integrated to enable knowledge delivery.

Integration

DisseminationCreation

Knowledge

Figure 1. Knowledge management as acyclic process.

Table 2. Two perspectives on knowledge management.

Traditional perspective Our perspective

Creation Specialists (for example, knowledge engineers) Everyone (for example, people doing the work), collaborative activity

Integration At design time (before system deployment) At use time (an ongoing process)

Dissemination Lecture, broadcasting, classroom, decontextualized On demand, integration of learning and working, relevant to tasks, personalized

Learning paradigm Knowledge transfer Knowledge construction

Tasks System driven (canonical) User or task driven

Social structures Individuals in hierarchical structures, communication Communities of practice, communication primarily peer-to-peerprimarily top-down

Work style Standardize Improvise

Information spaces Closed, static Open, dynamic

Breakdowns Errors to be avoided Opportunities for innovation and learning

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the captured information incrementally,rather than at the time of capture.3

DisseminationThis activity makes information in the

organizational memory available to workersto help their problem solving. TraditionalKM approaches assume that workers per-form repetitive and predictable tasks, so theydisseminate knowledge through classroomtraining or printed reference documents.These approaches separate learning andworking. They typically use informationtechnology to broadcast information (forexample, email) or to provide searchabledatabases. As we mentioned earlier, theinformation that workers receive or accesscomes from management (or the creator ofthe training materials) rather than fromcoworkers.

In the design perspective, the specificinformation needs of workers are unpre-dictable. The need for information resultsfrom particular situations that arise from aworker’s struggle to understand a problem.The context of problem solving dictates theinformation demand and provides the con-text for learning. On-demand informationintegrates working and learning, because theneed for learning comes from work, and thelearning takes place within the context of thework situation.

ProblemsNow we look at some of the major bar–

riers to implementing a design-orientedapproach.

Creating shared understandingKM aims to increase the ability of work-

ers to perform knowledge-intensive tasks.From the perspective of work as creativedesign, we can restate this purpose simply asunderstanding the problem at hand.

External representation.An important aspectof design is the creation of artifacts that exter-nalize knowledge. This is important for threereasons:

• In so doing, we begin to move from vague,tacit conceptualizations of an idea to amore explicit representation.

• The artifact provides a means for others tointeract with, react to, negotiate around,and build on the externalized idea.

• The artifact provides an opportunity to cre-ate a common language of understanding.

We have found that using external repre-sentations exposes, and focuses discussionon, relevant aspects of the framing and under-standing of the problem being studied, suchas tacit attitudes, values, and perspectives.4

This is because designers engage in a “con-versation with the materials of a situation.”2

In this conversation, designers interact withan external representation of the problem,and the situation talks back to them, causingbreakdowns in their prior understandings. Todesigners, breakdowns are not mistakes butopportunities to create new understandings.When a breakdown occurs, designers reflecton the breakdown,2,5 learning more about theproblem, its framing, and possible solutions.

Collaborative design. Increasingly, groupsor communities working together—not indi-viduals—perform design tasks. Complexityin collaborative design arises from the needto synthesize different perspectives of a prob-lem, to manage large amounts of informationrelevant to a design task, and to understandthe design decisions that have determined adesigned artifact’s long-term evolution.

Our work focuses on two types of groups,communities of practice6 and communitiesof interest.4

Communities of practice consist of peo-ple sharing a common practice or domain ofinterest. CoPs are sustained over time. Theyprovide a means for newcomers to learnabout the practice and for established mem-bers to share knowledge about their work andto collaborate on projects. They need supportfor understanding long-term evolution ofartifacts and for understanding problemscaused by rapid change in their domain.

CoIs consist of people from different fieldswho come together to work on a particular

project or problem. They typically exist for aproject’s duration. They need support for cre-ating shared understanding among stake-holders from different backgrounds, whobring different perspectives and languages tothe problem.

CoPs are typically associated with KM.CoIs are becoming increasingly involved inKM as projects become more interdisciplinaryand as collaborative design brings togetherspecialists from many domains. The challengeis to meaningfully bridge and integrate thesevarious perspectives. Such integration requiressupport for reflection in action. For collabo-rative design, where many people work to-gether to understand a problem, designbecomes a conversation in a more literal sense.That is, external representations facilitate aconversation not only with the design situa-tion but also with other designers. In this way,externalizations expose breakdowns due to alack of understanding of the problem, conflictsamong perspectives, or the absence of sharedunderstanding. As we mentioned earlier, suchbreakdowns are opportunities to build new,shared understandings.

Collaborative designs result in work prod-ucts7 that are enriched by the multiple per-spectives of the participants and the discoursesthat result from the process. This integratesthe individual and the group knowledge inways impossible in settings that rely solely on“divide and conquer” team organization.

Putting communities in chargeThe view of workers as reflective practi-

tioners within CoPs does not correspond towhat is taught in training or what is containedin information systems supporting the tradi-tional KM view. Traditional information sys-tems are closed systems that store answersto questions that might arise during work,under the assumption that workers are per-forming tasks that have been anticipated anddescribed. This assumption is a barrier toinnovation, because it does not let workersshare their new ideas for their peers to dis-cuss, debate, or build on. Closed systems donot give communities control over their ownknowledge but put a gulf between creationand integration. So, innovations happen out-side systems, and systems contain informa-tion that is chronically out of date and thatreflects an outsider’s view of work.1

Capturing information at use time. Designcommunities have learned that anticipatingall possible uses at design time (that is, when

62 computer.org/intelligent IEEE INTELLIGENT SYSTEMS

K n o w l e d g e M a n a g e m e n t

Closed systems do not give

communities control over their

own knowledge but put a gulf

between creation and integration.

So, innovations happen outside

systems.

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the system is created) is impossible.8 Skilleddomain professionals will change their workpractices over time, and new information willbecome available. If users cannot modify asystem at use time to support new practicesand new emerging information, they will belocked into old patterns of use, or they willabandon the system for one that better sup-ports how they want to work.

An example of a successful open systemthat allows user modifications is Xerox’sEureka. It is an information repository forcopier repair representatives that the com-pany believes has saved up to $100 milliona year.1 The users create and evolve therepository’s information, subject to peerreview. Eureka represents an early approachat taking bottom-up knowledge creation seri-ously, in which users gain peer recognitionthrough their contributions to the system.

The Eureka system takes an explicit ap-proach to knowledge capture; it demands a fairamount of work by users to input their prob-lem-solving knowledge. This is because muchof the Eureka user’s work takes place outsidethe KM system and must be input later.

Integrating tools and repositories. Systems thatintegrate work tools with information reposi-tories can support more subtle information cap-ture. For example, social navigation9 and rec-ommender systems collect information in thebackground as users do their work and thenprovide this information to a wider commu-nity to inform their decision making.

These approaches advance VannevarBush’s “trailblazer” concept and Will Hilland James Hollan’s “read ware and editware” concept10 to make these uniquecontributions:

• Traces are not preplanned aspects of aspace, but rather are “grown” (or createddynamically) in a more organic, or bot-tom-up, fashion.

• They provide information that reflectswhat people actually do rather than whatsystem designers think people should bedoing.

• They rely on how people occupy spacesand transform them by leaving their markson them.

• They often rely on spatial metaphors(drawing on work in architecture andurban design).

Alleviating information overloadThe KM community has focused consid-

erable effort toward codifying and storingknowledge. Knowledge workers are nowroutinely equipped with documents such asuser manuals and online help systems thatcontain thousands of pages of information.Reading these documents from beginning toend is a waste of time. Much information willnot make sense in the abstract, and workerswill have forgotten the information by thetime it becomes necessary.

Attention, please. The scarcest resource formost of us as we try to understand and solveproblems is not information; it is attention.11

Herbert Simon said,

If computers are to be helpful to us at all, it mustnot be in producing more information—wealready have enough to occupy us from dawnto dusk—but to help us to attend to the infor-mation that is the most useful or interesting or,by whatever criteria you use, the most valuableinformation.

As this quote implies, we have more infor-mation available than we have attention tounderstand and apply it. At the same time,finding information relevant to the task athand is becoming increasingly critical.

To address information overload, KMapproaches must provide the informationworkers need, when they need it. The fol-lowing example illustrates the limitationsof traditional approaches to knowledgedissemination.

More than just access. One of our collabo-rating companies employs 1,200 help deskpeople, who help customers solve problems

over the phone. Suppose desk person Nexpends considerable effort to solve a cus-tomer’s difficult problem, generating newknowledge in the process. How should thisknowledge be documented and shared withthe other help desk people? Should N broad-cast (for example, by emailing to a compa-nywide list) this problem and its solution tothe 1,199 other help desk people, as Figure2a illustrates?

We believe the answer is no. In general,this information will not be relevant to theother help desk people at the same point intime. All these people (like most knowledgeworkers) suffer not from a scarcity of infor-mation but from information overload. Theproblem will worsen if a help desk personreceives more decontextualized informationthat appears irrelevant.

Figure 2b illustrates a more promisingstrategy. The problem-solving knowledgethat N created and documented is capturedin an organizational memory. In the future,when a help desk person encounters a prob-lem in which N’s solution is relevant, theinformation is available—if the desk personcan find it.

The standard KM approaches for knowl-edge dissemination are access approachesthat let users search for stored informationusing database queries. Although suchapproaches are necessary for locating infor-mation, they are not always sufficient. Forexample, users might not be able to articu-late their information needs in a way that theaccess mechanisms require. Also, usersmight not be motivated to search for infor-mation if they don’t know that relevant infor-

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1,199

CustomerCustomer Customer Y Customer Z

Time t 0 t 1 t 2

N

2

1

N

437

671

InformationRepository

(a) (b)

Figure 2. Two help desk scenarios: (a) broadcasting decontextualized information cancause information overload; (b) access to contextualized information supports learningon demand.

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mation exists. Or, users might not be awareof the need for information in the first place.

Design-oriented KM approaches must gobeyond the traditional forms of knowledgedissemination if they are to address infor-mation overload. The help desk example rep-resents these core technical issues for KMenvironments:

• devising computationally tractable repre-sentations of experiences,3

• developing retrieval technologies thatrecognize complex as well as surfacesimilarities,12

• capturing significant portions of knowl-edge that practitioners generate in theirwork,13 and

• nurturing a culture that motivates individ-uals to work for the good of the group ororganization.14

PromisesWe differentiate between two types of

promises. The first constitutes myths, forwhich little evidence exists, and which mightlead us to work toward questionable goals.Although KM’s promise is exciting and real,misconceptions exist that we must exposeand examine. KM shares the hype and unre-alistic expectations that have surroundedother disciplines such as expert systems andobject-oriented design.15 For example, peo-ple assumed that these technologies by them-selves would do the job.

The second type of promises offers alter-natives to these myths. These promises focuson the three basic KM activities: knowledgecreation in the context of social creativity,16

knowledge integration in the context of liv-ing information repositories,17 and knowl-edge dissemination in the context of an atten-tion economy.1

Social creativityThe first myth is that knowledge is a com-

modity. This myth has two parts:First, we can simply and explicitly “capture”

the knowledge of a 30-year expert. So, we canfire the expert and hire someone with no rele-vant skills off the street who can now use the“knowledge base” to perform like an expert.

Second, in the ideal company, informationtechnology will capture all knowledge world-wide and instantly feed it through high band-width lines to a central location. At this loca-tion, experts will make globally optimaldecisions for the entire company and feed themback to the periphery for implementation.18

Knowledge > information. John Brown andPaul Duguid1 argue convincingly that knowl-edge is more than just information because it

• usually entails a knower,• appears harder to detach than information,

and• is something what we digest rather than

merely hold.

A consequence of these observations isthat attention to knowledge (rather than justto information) requires attention to people,including their tasks, motivation, and inter-ests in collaboration. Knowledge is infor-mation that is attached to a particular context(for example, a task, problem, or question).

Although information can be easily trans-mitted from place to place and person to per-son, the underlying context cannot. Infor-mation technology is necessary to realize theKM cycle of creation, integration, and dis-semination, but technology alone is insuffi-cient. KM requires changing work practicesand attitudes to acknowledge the importanceof the knowledge worker and the contexts ofwork in transforming information into capa-bility for effective action.15

Social creativity and informed participation.The heart of intelligent human performanceis not the individual human mind but groupsof minds interacting with each other and withtools and artifacts. Social creativity growsout of the relationship between an individualand the world of his or her work, and out ofthe ties between an individual and otherhuman beings. The knowledge to understand,frame, and solve most design problems doesnot preexist but is constructed and evolvesduring problem solving. So, instead of sim-

ply providing better access, KM should sup-port informed participation1 in collaborativeproblem-solving processes and communities.

However, informed participation is impos-sible in communities that strictly separateusers from designers and developers. Thisseparation is undesirable and unproductive.

Users must acquire a new mind-set—theyare no longer passive receivers of knowledgebut are active researchers, constructors, andcommunicators of knowledge. Knowledgeis no longer handed down from above; it isconstructed collaboratively in the contextsof work.

Living organizational memoriesThe second myth is that the evolution of

complex artifacts and information spaces canbe purely self-organized (decentralized). KMcan learn some lessons from open-sourcedevelopment projects,19 which always have acore set of project leaders who have the finalsay on what course a project’s evolution takes.These people centrally integrate informationthat others have contributed in a decentral-ized manner. Contributors are explicitlyacknowledged and often assume responsibil-ity for their subsystem’s evolution. Open-source projects have many varieties of con-trol structures, but each project will havesome centralized responsibility.20 No projectpractices purely decentralized evolution.

The evolution of open KM systems mustalso have elements of decentralized evolu-tion and centralized integration. The mix ofthese modes and the means of selecting indi-viduals to assume responsibility will takemany forms. Later in this article, we presenta general framework identifying essentialactivities and roles for sustained evolution ofopen systems. A major difference betweenopen-source projects and open KM systemsis that the latter’s users are end users.

The goal of making user-modifiable sys-tems does not imply transferring the respon-sibility of good system design to the user.Normal users generally will make poorermodifications than a system specialist would.Users are not concerned with the system perse but with doing their work. On the otherhand, users are concerned with the system’sadequacy as a tool for their work. So, theyexperience how the tool’s capabilities fit, ordo not fit, their needs. This is knowledge thespecialist cannot have, because the specialistdoes not use the tool to do work. User-mod-ifiable systems let the user adapt a systemdirectly, without requiring a specialist and

64 computer.org/intelligent IEEE INTELLIGENT SYSTEMS

K n o w l e d g e M a n a g e m e n t

The heart of intelligent human

performance is not the individual

human mind but groups of minds

interacting with each other and

with tools and artifacts.

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without requiring deep knowledge of the sys-tem’s inner workings.21 Designing such sys-tems does not decrease the system special-ist’s responsibility or importance. It shifts theresponsibility from designing a finished sys-tem at design time to designing a system thatthe user can adapt and modify at use time.

A proper evolution. Living organizationalmemories offer these promises and opportu-nities:

• They are information spaces owned by thepeople and communities who use them todo work, not by management or the ITdepartment.

• They support the collaborative and evolu-tionary design of complex systems by pro-viding a means to integrate the many con-tributions of many people.

• They are open and evolvable systems,serving not only as information reposito-ries but also as mediums of communica-tion and innovation.

• They can evolve through many small con-tributions by many people rather thanthrough large contributions by a few peo-ple (as has been the case for previousknowledge-based systems).

Unself-conscious cultures of design. When anartifact’s users can recognize and repair break-downs as they use it, they are empowered tomaintain the artifact’s fit to its changing envi-ronment. The architect and design methodol-ogist Christopher Alexander wanted his build-ings to be continually maintained andenhanced in this manner by the people whoinhabited them. He coined the phrase unself-conscious culture of design22 to describe thisform of design-in-use. In unself-consciousdesign, breakdown and correction occur sideby side; no formal set of rules describes howto repair breakdowns, because the breakdowns

are unanticipated. Instead, the knowledge torepair breakdowns comes from the user, whocan best recognize the lack of fit and how tochange the artifact to improve its fit.

In an unself-conscious culture of design,an artifact’s failure or inadequacy leadsdirectly to an action to change or improve it.For example, when a house’s owner is alsoits builder, constant rearrangement of unsat-isfactory details are possible. In KM, opensystems put the owner of problems in charge.Because the owners are in charge, the posi-tive elements of the unself-conscious designculture can be exploited in the evolution oforganizational memories. In such environ-ments, the end users, not the system builders,experience breakdowns. These breakdownslead the users to continually and directlyevolve and refine their information space,without relying on professionals.

Sustaining the usefulness and usability ofliving information repositories over timeinvolves important challenges and trade-offs(summarized in Table 3). These trade-offsdepend on whether these information repos-itories are evolved by specialists or by knowl-edge workers.

Attention economyThe third myth is that “anytime and any-

where” information access will solve KMproblems. Because we believe that thescarcest resource for most people is atten-tion, we claim that the real challenge is to“say the right thing at the right time in theright way.” This is possible only with com-putational environments that take the user’scontext into account (for example, what theusers are doing, what they know, where theyare, and what have they done). KM needs toexploit computational media’s capabilitiesfor interpreting information to support atten-tion economies, in which attention is themost valued resource.

Unique capabilities of computational media.Printed media do not have interpretivepower—they can convey information, butthey cannot analyze the work products wecreate. Computational media can provide

• information relevant to the task at hand,23

thereby reducing the information overloador the need for decontextualized learning,and

• the foundation for on-demand informa-tion, detail, and learning.

Beyond access approaches. Informationdelivery complements information accessapproaches for disseminating information.While information access is a user-initiatedsearch, information delivery is a system-initiated presentation of information in-tended to be relevant to the user’s task. Table4 compares information access and deliv-ery technologies.

Support for information access is indis-pensable because designers must be able tosearch for needed information. The ability ofinformation access technologies to retrieveinformation related at levels beyond surfacesimilarities has improved. However, theyremain limited in principle because theirusers must articulate information needs.

Information delivery technologies exploitthe scarce resource of attention better, be-cause they infer a user’s information needsrather than requiring the user to explicitlyformulate a query. Information delivery isparticularly important when designers are notmotivated to look for information or whenthey are not aware of the need for informa-tion in the first place.

To deliver information relevant to theuser’s task, delivery mechanisms face twomajor challenges:

• Determining the user’s information needs.

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Table 3. Information repositories evolved by specialists versus those evolved by knowledge workers.

Evolved by specialists Evolved by knowledge workers

Examples ACM digital library Web sites of communities of practice; Eureka

Nature of individual entries Database-like entries Narratives and stories

Economics Requires substantial extra resources An additional burden on the knowledge workers

Delegation Possible in domains in which entries or Performed by problem owners, because the entries or objectsobjects are well defined are emerging products of work

Design culture Self-conscious Unself-conscious

Motivation Work assignment Social capital

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Information needs can be inferred from thetask at hand (what the user is doing andthe actions he or she has performed) andfrom the user’s intentions. Determiningthe task at hand is challenging, but infer-ring intentions is even more difficult.Delivery mechanisms must operate withincomplete information about the design-ers’ intentions because they are not com-pletely known, even by the designer.

• Intervention strategies. Although deliverymechanisms can be designed and tailoredfor minimum disruption, a conflict willalways arise between the need to informusers and the desire not to inundate themwith irrelevant messages.

These are formidable challenges. However,we believe that information delivery will ful-fill the promise of information on demand,thereby realizing the vision of an attentioneconomy.

RealitiesOver the last decade of research on our

integrated KM approach, we have developed

• conceptual frameworks, such as the seed-ing, evolutionary growth, reseeding (SER)process model; the integration of informa-tion access and information delivery; bound-ary objects; and courses-as-seeds; and

• prototype systems such as Domain-Ori-ented Design Environments, the Envi-sionment and Discovery Collaboratory,and DynaSites to validate and extend theframeworks.

Table 5 summarizes these efforts. Now welook at how this work addresses the problemsand promises we’ve discussed.

Domain-Oriented Design Environments

DODEs are a class of integrated systems

that uniquely support the KM cycle. We havebuilt DODEs in many domains. During thisprocess, we have developed a domain-inde-pendent software architecture describing thetools and knowledge-based mechanisms thatsupport the KM processes.5 We now exam-ine NetDE, a DODE that supports the cre-ation and management of knowledge in thedomain of local area network design andadministration (see Figure 3).

To create LAN layouts, users employ aconstruction worksheet (see Figure 3a and3b), in which they locate network devicesand connect them using different cables andnetwork protocols. They can use a simula-tion component to visualize dynamic be-haviors as they make changes and try newideas. A specification component (see Fig-ure 3c) lets users articulate high-level inten-tions for their project that are not explicitin the worksheet, such as a ranking of priorities.

The NetDE information repository con-sists of a group memory and a catalog. Thegroup memory (see Figure 3d) holds infor-mation from previous projects, email com-munication archives, and other textual infor-mation. The catalog (see Figure 3e) containsexample networks. Knowledge workers canuse them to see how a similar problem wassolved, to understand the evolution of a net-

work being designed, or as a starting pointfor a new design.

Like most KM environments, NetDE sup-ports information access through searchingand browsing. Unlike most KM environments,NetDE can play an active role in knowledgedissemination. Critiquing mechanisms (crit-ics) monitor the actions of users as they workand inform them about potential problems.Users can elect to see information relevant toa problem. If they do, critics place the user inthe repository where relevant information islocated. The user can then browse the prox-imity to learn more about the problem. In thisway, NetDE integrates information access anddelivery approaches.

Critics exploit the context defined by thestate of the construction worksheet and thesimulation and specification components toidentify potential problems and to determinewhat information to deliver. This contextenables precise intervention by critics, reducesannoying interruptions, and increases the rel-evance of information delivered to designers.

Critics embedded in design environmentsincrease the user’s understanding of prob-lems to be solved, point out informationneeds that might have been overlooked, andlocate relevant information in large informa-tion spaces. Embedded critics save users thetrouble of explicitly querying the system for

66 computer.org/intelligent IEEE INTELLIGENT SYSTEMS

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Table 4. Comparison of information access and delivery approaches.

Access Delivery

Examples Passive help systems, browsing, Microsoft’s “Tip of the Day,” broadcast systems, critiquing, active helpWeb search engines, bookmarks systems, agent-based systems

Strengths Nonintrusive, user controlled Serendipity, creating awareness for relevant information, rule enforcement

Weaknesses Task-relevant knowledge might remain hidden Intrusiveness, possibility of decontextualized informationbecause the user couldn’t specify it in a query

Major system Supporting users in expressing queries, better Context awareness (intent recognition, task models, user models, relevance design challenges indexing and search algorithms to a task)

Table 5. Our conceptual contributions and prototype systems.

Area Contribution Example

Creation Boundary objects (supporting Envisionment and Discoveryinformed participation); seeding, Collaboratoryevolutionary growth, reseedingprocess model

Integration and Collaborative, decentralized, DynaSitesevolution evolvable information spaces

Dissemination and Information delivery (learning on Domain-Oriented Designlearning demand, specification components, Environments

using an artifact as a query)

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information. Instead, the design contextserves as an implicit query. Rather than spec-ifying information needs, the user only hasto click on a critiquing message to obtain rel-evant information.

The Envisionment and DiscoveryCollaboratory

For our first-generation DODEs, we sim-plified the process of “context awareness,”because all activities happened inside thecomputational environment rather than in theexternal world. The Envisionment and Dis-covery Collaboratory4 represents second-generation DODEs that support social inter-action by creating shared understandingamong various stakeholders, contextualizinginformation to specific tasks, and creatingboundary objects as externalizations in col-laborative design activities. The EDC extendsthe original DODE approach by integratingcomputational environments and (computa-tionally enriched) external physical worldswith mechanisms capturing the larger (oftenunarticulated) context of what users aredoing. DODEs primarily support CoPs; theEDC also supports CoIs.

Supporting CoIs. The EDC provides objectsthat all stakeholders can understand andmanipulate. It also provides underlying com-putational support for trying out alternativesolutions, accessing relevant information,

and capturing information and design ratio-nale from the design process.

Stakeholders using the EDC convenearound a computationally enhanced table thatserves as the action space. Currently realizedas a touch-sensitive surface, the action space

lets users manipulate a computational simu-lation projected on the surface by interactingwith physical objects placed on the table. Thesimulation is an interactive model of thedesign problem that reacts to the user’s input.It lets users explore alternative solutions in apotentially complex design space. Flankingthe table is another touch-sensitive (vertical)surface that serves as the reflection space.The reflection space displays informationthat is relevant to the context as defined bythe simulation.

The EDC framework is applicable to dif-ferent domains, but our initial effort hasfocused on urban planning and decision mak-ing, specifically in transportation planningand community development. In Figure 4,neighbors are filling out a Web-based trans-portation survey associated with the simula-tion being constructed.

Boundary objects. Action space objects aredomain oriented—they look and behave likeobjects in the problem domain. These objectsand behaviors are meaningful to all stake-holders who are familiar with the domain.However, the stakeholders might not share theprecise meanings of the objects and the impli-cations of the meanings for design decisions.The objects serve as boundary objects by pro-viding a common starting ground for stake-

JANUARY/FEBRUARY 2001 computer.org/intelligent 67

Figure 3. A Domain-Oriented Design Environment for designing local area networks:(a–b) the construction worksheet; (c) the specification component; (d) the groupmemory; (e) the catalog.

(e)

(d)

(a)

(b)

(c)

Figure 4. The Envisionment and Discovery Collaboratory. In the action space(foreground), stakeholders use physical objects to interact with an underlying computational simulation environment. In the reflection space (background),stakeholders access information, fill out surveys, and add new information.

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holders to identify and explore the differencesin their understandings and to build newunderstandings that bridge the boundaries.

For example, in the transportation-planningdomain, stakeholders include transportationengineers and neighborhood residents whowill work together to improve the design ofbus routes in their neighborhood. In the actionspace, they use domain objects such as buses,bus stops, neighborhoods, and streets toexplore the problem’s different facets. Anengineer might think of a bus stop in terms ofits capacity to serve a certain-size neighbor-hood, while a resident might think of a busstop in terms of its convenience to his or herhouse or in terms of its safety at night. The busstop object in the EDC is a boundary objectfor engineers and residents to build a sharedunderstanding of the “bus stop” concept interms of the importance and implications forthe particular design. The action space simu-lation, which helps stakeholders explore alter-natives, and the reflection space, which pro-vides background information about eachperspective, enhance this process.

The seeding, evolutionary growth,reseeding process model

We developed the SER process model tounderstand the balance between centralizedand decentralized evolution in sustaineddevelopment of large systems. Our goal is toapply lessons learned from successes such asopen-source software to domains and com-munities, such as KM, that have not tradi-tionally been viewed from this perspective.

The SER model situates the KM cycle in alarger context by addressing how to initiate andsustain it (see Figure 5). The model describesthree phases of evolution in terms of the stake-holders involved and their activities. The seed-ing phase creates the initial conditions for theKM cycle. The cycle’s activities are the dri-ving force of the evolutionary-growth phase.Finally, reseeding is a periodic effort to orga-nize and tune the KM environment.

Seeding. In this phase, system developers andusers work together to develop an initial KMenvironment seed. As the name suggests, theseed is a starting point for ongoing growth.

Rather than chasing the impossible goal ofcomplete coverage, environment designerscan initially underdesign the seed. That is,the designers do not create final solutions;they design spaces that knowledge workerscan change and modify at use time.

The seeding phase requires system devel-opers because the product is a complex soft-ware system. User participation is also nec-essary, because users have the knowledgenecessary to decide what content the seedshould include and how that content willneed to evolve.

Although the SER model acknowledgesthat the initial seed cannot be complete, theseeding process still requires a substantialup-front investment. Existing software toolswill likely have to be reimplemented or sub-stantially adapted to function with informa-tion repositories. The repositories themselvesmust be designed to function with the tools(through underlying integrating mechanisms,such as critics). We have found that begin-ning with a community’s existing informa-tion repositories and tools is effective. Wethen incrementally create prototypes thathelp developers and users understand how tocast their old information and technologyinto the new framework. This approach cre-ates boundary objects for the users, lettingthem participate fully in the seeding.24

Evolutionary growth. This is the normal,operational phase of the SER model, inwhich the seed supports the three activitiesof the KM cycle. During this phase, the infor-mation repository plays two roles simulta-neously: through dissemination it informswork, and through integration it accumulatesthe work products. Figure 5 depicts theseroles as arrows.

A KM environment will experience sev-eral types of evolutionary growth, including

• implicitly captured information (for exam-ple, email and navigation traces).

• explicitly produced information, includ-ing finished work products (along withtheir rationale), which are collected in thecatalog.

• incremental formalizations, representinginformation so that it can be connectedconceptually and computationally to exist-ing information in the repository. Forexample, a design rationale created dur-ing the project might be entered into thelarger argumentative structure to show onealternative view or solution to a problem.

68 computer.org/intelligent IEEE INTELLIGENT SYSTEMS

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Knowledge creation

KM environment

Development substrates

ProjectB

Evolutionary growth

Reseeding

Time

Knowledgedissemination

Knowledgeintegration Stakeholder Knowledge

worker Developer

ProjectA

Seeding

Figure 5. Knowledge management processes in the context of the seeding, evolutionarygrowth, reseeding model. A KM environment (including its content) starts in the seedingphase. The evolutionary growth phase occurs as people use the environment on projects.Occasionally, the information integrated during this growth requires a reseeding phase.Evolutionary growth then continues until reseeding is again necessary.

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• end-user modifications, letting owners ofproblems and power users21 extend thesystems at the tool and at the content level.

An essential aspect of this phase is that theuser community is responsible for changingthe seed. Contributing domain knowledgeshould be part of everyone’s job. But formal-izing information and modifying system func-tionality might require significant program-ming knowledge. So, these tasks will be theresponsibility of power users, who are tech-nically inclined and motivated to do them.

The SER model assumes that some ele-ments of an unself-conscious culture ofdesign will emerge in the user community.Depending on this culture’s strength, the evo-lutionary growth phase might last for anextended time period. However, as we dis-cussed earlier, such decentralized evolutionhas its limits, and eventually the KM envi-ronment’s usefulness and usability will suf-fer. When this happens, developers mustcome back into the picture to reseed the KMenvironment.

Reseeding. Reseeding is necessary for manyreasons. For example, some incrementalchanges might point out fundamental limita-tions in the seed. Also, managing and com-bining many incremental changes might bedifficult, and some incremental changes mightmake future changes more difficult. Reseed-ing is a complex process by which a group ofusers together with system developers takestock of the current system, synthesize itsstate, and reconceptualize it. This process pro-duces a new system that can serve as the basisfor future evolution. The evolution and reseed-ing cycle continues as long as people are usingthe system to solve problems.

Our experience with the SER model, aswell as our observations of evolving softwaresystems, indicates that periodic reseedingwill be necessary, although the periodbetween reseeding phases differs from com-munity to community. It is necessary for tworeasons. First, KM environments are embed-ded in a changing world and therefore mustadapt. Small-scale modifications might suf-fice initially, but eventually any KM systemwill need to be modified in a way that isbeyond even power users. Second, the con-texts in which new knowledge is created aredifferent from the contexts in which it willbe reused. Restructuring this knowledgefrom its original form into a reusable formrequires substantial effort.

DynaSitesDeveloped at the University of Colorado,

DynaSites (http://seed.cs.colorado.edu/dynasites.documentation.fcgi) is an envi-ronment for creating and evolving Web-based information repositories. It serves as aKM environment substrate (see Figure 5) toinvestigate KM processes in the context ofthe SER model. DynaSites currently houses20 information spaces, all of which users canextend. It supports

• knowledge creation within the informa-tion spaces of individual projects,

• knowledge integration across the individ-ual spaces by means of shared spaces, and

• knowledge dissemination by logicallyclustering related information.

As Figure 6 shows, the individual infor-mation spaces have four main components:

A threaded discussion forum belongs to aparticular community. Dynasites currentlyhas 16 discussion forums, four of which areactive. The forums support a variety of com-munities, including university courses,research projects, and workshops. Anyonecan create a discussion forum.

Sources is a shared repository for litera-ture references, such as journal articles, con-ference proceedings, and Web sites. Eachentry has a discussion thread that lets usershold open-ended discussions. Sources isopen to all DynaSites users.

The community space holds persona pagesfor each DynaSites user. Users design per-

sonas, which contain information about theuser, such as a picture, interests, a homepageURL, and whatever else the user wishes toshare. Personas help users establish an iden-tity within DynaSites and find others withwhom to collaborate, based on mutual inter-ests or complementary experiences. Thecommunity space currently contains 200 per-sona objects.

DynaGloss is a glossary of terminologyopen to all DynaSites users, who can anno-tate terms or redefine them when desired.DynaGloss currently contains 225 definedterms.

Integration in DynaSites. We use severalstrategies to link the information spaces inDynaSites (see Figure 6). Perhaps the mostimportant are the term links, which enableDynaGloss to automatically integrate infor-mation across the entire DynaSites reposi-tory. For example, suppose the term “knowl-edge management” is defined in DynaGlossand appears in entries (shown cross-hatchedin Figure 6) of both Forum A and Forum B.A user reading the entry in Forum A wouldsee “knowledge management” representedas a link. Selecting the link would take her tothe “knowledge management” entry in Dyna-Gloss, which contains a definition and a listof all uses of the term throughout DynaSites.This list includes a link to the entry in ForumB containing “knowledge management.” Byfollowing this link, the user would be likelyto find a discussion relevant to Forum A, butpossibly expressing a different perspective.

JANUARY/FEBRUARY 2001 computer.org/intelligent 69

Author

CrossCross

To Web

Forum A

Cross

Sources

Forum B

Author

Term

Author

Term

Keyword

Community space

DynaGloss

Figure 6. DynaSites provides several means to integrate the information repository. Termlinks bidirectionally connect the use of a term and its definition in DynaGloss. Keywordlinks connect records in sources with definitions in DynaGloss. Author links connect eachcontribution to the author’s persona in the community space. Users create cross links,which connect arbitrary entries or connect an entry to any page on the Web.

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Finally, she might follow the persona link forthe entry in Forum B and become acquaintedwith a new collaborator.

The various linking strategies in Figure 6create a rich web of information that con-nects ideas, people, and literature references.Because the system automatically createsand updates most of the links, informationmust be in a form that the system can inter-pret. For example, terms must have the samespellings as the glossary entries. The Dyna-Sites information space’s overall quality andintegration requires effort and attention todetail that go beyond simply entering infor-mation. Without care, the information spacecan become unwieldy after a period of decen-tralized evolution. We are investigating howmuch extra effort users are willing to put intoentering information, and what this effort’scomponents are.

Courses-as-seedsThis educational model attempts to ex-

plore the KM cycle in the context of univer-sity courses.25 The goal is to establish a cul-ture of collaborative knowledge creation thattranscends the temporal boundaries ofsemester-based classes. In the spirit of theSER model, we conceptualize courses asseeds rather than finished products. Centralto the courses-as-seeds model is an informa-tion repository that lets each course offeringbuild on the products of prior semesters andserve as a forum for class discussions and aworkspace for projects.

We now look at our initial attempt toimplement this model. This implementationprovided a concrete way to analyze our con-ceptual frameworks, such as the KM cycleand the SER model, as well as the supportingDynaSites technology.

The University of Colorado at Boulder isdeveloping a major initiative called theAlliance for Technology, Learning, and Soci-ety (www.colorado.edu/ATLAS). Part of theAtlas initiative is the Technology Arts andMedia certificate program (www.colorado.edu/ATLAS/certific.html). In the context ofthe TAM program, we taught Designing theInformation Society of the New Millennium(www.cs.colorado.edu/~l3d/courses/atlas-2000) in the spring 2000 semester. (Wewill call this the TAM course.) This course’sadvertised goal was to let students explore hownew media will affect learning, designing, andcollaboration in the information society.

The class met twice a week; we based theactivities on a series of assigned readings. We

assigned questions for each reading andasked students to post their responses in thediscussion forum before the periods in whichthey discussed the responses. We stronglyencouraged them to read and comment oneach other’s postings. Class discussions werebased on the readings and responses but werenot necessarily restricted to the reading topic.We assigned two projects in which studentsformed groups and selected their topics. Theprojects used a DynaSites forum for coordi-nating, communicating, and storing the proj-ect products.

At the semester’s end, the forum contained362 entries. Analysis of the informationspace indicates problems that limit the infor-mation’s utility for future courses.25 In terms

of the SER model, decentralized evolutionover the semester resulted in an informationspace that required centralized integration.

The information’s structure made sense tothe creators but not to those who did not par-ticipate. During the course, the discussionthreads were created to serve an unfoldingdiscussion. As the discussions becamefocused, students articulated many niceinsights. Users have difficulty finding these“nuggets” because they must read the entirethread (including branches). In effect, thenuggets are buried in the thread structure.Search mechanisms do not completely alle-viate this problem, because a reader mustknow what to look for, and still must read theentries that the search returned.

The information produced during thecourse is also not well integrated in the largerDynaSites information space. Often, discus-sions relevant to terms defined in DynaGlossdid not use exactly the same terminology.Therefore, the term-linking mechanism didnot detect the discussions. In other cases,

forum entries mentioned literature referencesthat might be helpful to future courses, butthese references did not appear as Sourcesentries. So, they also became buried nuggets.

These situations are undesirable; theydecrease the probability that students in thenext TAM course will reuse the products.Students are unlikely to merely read them,let alone use them as building blocks, stableintermediate forms,26 patterns,27 or best prac-tices15 to develop the ideas further.

The reseeding process has involved editingthe contents, formality, and structure of infor-mation spaces to make them more useful asbuilding blocks for new knowledge. TheDynaSites developers perform reseedingwith TAM course participants, who own theinformation and therefore can best predicthow it will be reused. The developers andparticipants collect and organize buriednuggets so that users can quickly find them.They edit selected entries so that the entriesuse terminology that the term-linking mech-anism will pick up. Literature references arerepresented in Sources, where all DynaSitesusers can find and discuss them.

ChallengesAs we mentioned earlier, the design per-

spective assumes a culture in which man-agement and workers see the workers as pro-ducers and managers of knowledge, ratherthan as consumers. In this culture, workersare motivated to share their knowledge ratherthan hoard it as “job security.”Achieving thisculture, however, involves major challenges.

Creating new mind-sets and KMcultures

Our KM perspective requires a culturaltransformation in which all stakeholdersmust learn new relationships between prac-tices and attitudes. Our initial steps have beento self-apply our theories and technologiesin our university context through the courses-as-seeds model. This context is convenientbecause courses provide access to commu-nities in which the risk of trying new prac-tices is acceptable (and even educational) andbecause stakeholders will be more forgivingof immature technologies.

Education reform.What is more important, wefeel that the traditional educational model, liketraditional KM models, needs serious reform.

The courses-as-seeds model’s premise isthat the traditional education paradigm isinappropriate for studying the types of open-

70 computer.org/intelligent IEEE INTELLIGENT SYSTEMS

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The design perspective

assumes a culture in which

management and workers see

the workers as producers and

managers of knowledge,

rather than as consumers.

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ended and multidisciplinary problems thatare most pressing to our society. These prob-lems, which typically involve a combinationof social and technological issues, require anew paradigm of education and learningskills, including self-directed learning, activecollaboration, and consideration of multipleperspectives. Problems of this nature do nothave “right” answers, and the knowledge tounderstand and resolve them is changingrapidly, requiring an ongoing and evolution-ary approach to learning.

The courses-as-seeds model represents asystem of values, attitudes, and behaviorsthat is radically different from the traditionaleducational culture, which views courses asfinished products and students as consumers.Courses-as-seeds aims to create a culturebased on a “designer mind-set” that empha-sizes habits and tools that empower studentsto actively contribute to the design of theireducation (and eventually to the design oftheir lives and communities).

Beyond consumers. Evaluation of our coursesshows the difficulties of changing the mind-sets that students have been taught over yearsin the educational system and that continuein the workplace. The collaboration and evo-lutionary growth that the SER model postu-lates is impossible in communities wheremost members regard themselves as con-sumers.16 Individuals must have the opportu-nity to evolve into power users and codevel-opers who use and can, at the same time,modify and extend their KM environments ifnecessary. Toward that end, information tech-nology can help us understand and exploitsoftware’s malleability, which will let us con-struct knowledge collaboratively in the con-text of work.

Arguing for users being designers and notjust consumers requires a deep understandingof delegation in a society characterized by adivision of labor (see Table 4, row 4). Delega-tion is desirable when the delegator does notpossess the knowledge or skill to accomplisha task directly and when the task can be spec-ified in enough detail to be entrusted to some-one else. Professional expertise has its place—if it is used properly. For example, professionaldevelopers are necessary during the SERmodel’s seeding and reseeding phases, becausethey possess the technical skills necessary tosubstantially modify the KM environment.

MotivationAn important nontechnical challenge for

collaborative construction and evolution ofinformation repositories is to take motivationseriously. Our experiences with courses-as-seeds illustrates this challenge.

In the courses-as-seeds model, the instruc-tors intended to spur peer-to-peer interactionby assigning reading materials and requiringstudents to post their responses in the forum.The instructors reasoned that because student’spostings would be available to their peers,interesting discussion based on these postingswould follow. Because instructors assumedthat students would be intrinsically motivatedto interact with their peers, they did not makethis an explicit part of the grading criteria.

The instructors’ assumption did not hold.The reading assignments dominated activity

in the forum; students posted long responsesbut only extremely rarely commented onanother student’s response. The high partic-ipation rates and considerable length of theassigned postings show that students weremotivated to spend considerable time andeffort fulfilling the explicit requirements fora good grade. But they were not motivated tospend the additional time required to readand comment on the responses of their peers.

The course design did not consider care-fully enough the competing demands fromother classes for the student’s time and atten-tion. In effect, the course design sent studentsa mixed message. The graded assignmentpolicy reinforced the traditional model towhich the students were accustomed, andmight have led them to do only what theyconsidered necessary for a good grade. Onthe other hand, the course’s content and therhetoric of the instructors implied a differentmodel that assumed students would be moti-vated to go beyond the minimum.

Sustained collaborative work practices

require an incentive to create social capital28

by rewarding stakeholders for contributingand receiving knowledge as a member of acommunity. Social capital is based on theseconcepts:

• Human beings have an innate drive tocompete for social status.

• What you give away, not what you con-trol, determines social status.

• Prestige is a good way to attract attentionand cooperation.

• Utilization is the sincerest form of flattery.

The Experts Exchange (www.experts-exchange.com) is an example of a gift cul-ture that provides social capital. It is a vast,evolving repository of answers to a wide vari-ety of technical questions. Users compete forexpert points by giving good answers toquestions from other users. Users amassingthe highest number of expert points receiverecognition from the community and arelisted in the “winner’s circle” for all to see.

Users can spend question points to askquestions or see previously answered ques-tions. When users become a member ofExperts Exchange, they receive enoughpoints to see the answers to approximately15 questions. However, if they wish to retaintheir privileges for a sustained time, theymust earn more through various activities.

As you can see, Experts Exchange is aknowledge-sharing culture built on a mutuallybeneficial relationship: questioners receiveanswers, and experts gain social capital.

Technology alone will not solve the dif-ficult problems of KM.1,15 Knowing

is a human act. Although new technologiesare important and necessary for progress inKM, they are insufficient.

KM forces us to transcend individual per-spectives. Until recently, computational envi-ronments focused on the needs of individualusers. As more people use computers formore complex tasks, we are realizing that weneed environments supporting social inter-actions among communities of practice and

JANUARY/FEBRUARY 2001 computer.org/intelligent 71

Sustained collaborative work

practices require an incentive to

create social capital by rewarding

stakeholders for contributing and

receiving knowledge as a member

of a community.

The paradise of shared knowledge isn’tjust happening. Knowledge isn’tshared because management does notwant to share authority and power.

—Shoshana Zuboff

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communities of interest. However, this per-spective does not necessitate the develop-ment of environments in which the group’sinterests inevitably supersede the individ-ual’s. Individuality makes a difference, andcommunities get their strength to a largeextent from the creativity and engagement ofthe individual. An important challenge willbe to gain a better understanding of the rela-tionship between the individual and the community.

Ongoing collaborative knowledge con-struction and sharing (in the context of cre-ative design activities) are difficult processes.To make real progress with KM requireschanging work practices, mind-sets, andreward structures. A student participating inour course characterized the ultimate chal-lenge to KM: “Collaborative systems will notwork in a noncollaborative society.”

AcknowledgmentsWe thank the members of the University of Col-

orado’s Center for LifeLong Learning and Design,who have made major contributions to the con-ceptual frameworks and systems described in thisarticle. National Science Foundation Grants REC-9631396 and IRI-9711951; Office of NavalResearch Cooperative Agreement N66001-00-1-8964; Software Research Associates, Tokyo; PFU,Tokyo; and the Coleman Foundation all supportedthis research.

References

1. J.S. Brown and P. Duguid, The Social Life ofInformation, Harvard Business School Press,Boston, 2000.

2. D.A. Schön, The Reflective Practitioner: HowProfessionals Think in Action, Basic Books,New York, 1983.

3. F. Shipman and R. McCall, “SupportingKnowledge-Base Evolution with Incremen-tal Formalization,” Human Factors in Com-puting Systems, INTERCHI ’94 Conf. Proc.,ACM Press, New York, 1994, pp. 285–291.

4. E.G. Arias et al., “Transcending the Individ-ual Human Mind: Creating Shared Under-standing through Collaborative Design,” ACMTrans. Computer–Human Interaction, vol. 7,no. 1, Mar. 2000, pp. 84–113.

5. G. Fischer, “Domain-Oriented Design Envi-ronments,” Automated Software Eng., vol. 1,no. 2, June 1994, pp. 177–203.

6. E. Wenger, Communities of Practice: Learn-ing, Meaning, and Identity, Cambridge Univ.Press, Cambridge, UK, 1998.

7. J. Bruner, The Culture of Education, HarvardUniv. Press, Cambridge, Mass., 1996.

8. J. Greenbaum and M. Kyng, eds., Design atWork: Cooperative Design of Computer Sys-tems, Lawrence Erlbaum Associates, Hills-dale, N.J., 1991.

9. A. Dieberger et al., “Social Navigation: Tech-niques for Building More Usable Systems,”Interactions, vol. 7, no. 6, Dec. 2000, pp.36–45.

10. W.C. Hill et al., “Edit Wear and Read Wear,”Proc. CHI ’92 Conf. Human Factors in Computing Systems, ACM Press, New York,1992, pp. 3–9; www.acm.org/pubs/articles/proceedings/chi/142750/p3-hill/p3-hill.pdf.(current 14 Feb. 2001).

11. H.E. Pashler, The Psychology of Attention,MIT Press, Cambridge, Mass., 1998.

12. T.K. Landauer and S.T. Dumais, “A Solutionto Plato’s Problem: The Latent SemanticAnalysis Theory of Acquisition, Inductionand Representation of Knowledge,” Psycho-logical Rev., vol. 104, no. 2, Feb. 1997, pp.211–240.

13. J. Orr, Talking about Machines: An Ethnog-raphy of a Modern Job, ILR Press/CornellUniv. Press, Ithaca, N.Y., 1996.

14. J. Grudin, “Groupware and Social Dynamics:Eight Challenges for Developers,” Comm.ACM, vol. 37, no. 1, Jan. 1994, pp. 92–105.

15. R.G. Smith and A. Farquhar, “The RoadAhead for Knowledge Management: An AIPerspective,” AI Magazine, vol. 21, no. 4,Winter 2000, pp. 17–40.

16. G. Fischer, “Social Creativity, Symmetry ofIgnorance and Meta-Design,” Knowledge-Based Systems J., vol. 13, nos. 7–8, Dec.2000, pp. 527–537.

17. L.G. Terveen, P.G. Selfridge, and M.D. Long,“Living Design Memory: Framework, Imple-mentation, Lessons Learned,” Human–Com-puter Interaction, vol. 10, no. 1, 1995, pp. 1–37.

18. J. Thomas, Welcome to Information aboutKnowledge Management, 2001, www.truthtable.com/know.html. (current 26 Feb.2001).

19. T. O’Reilly, “Lessons from Open Source Soft-ware Development,” Comm. ACM, vol. 42,no. 4, Apr. 1999, pp. 33–37.

20. E.S. Raymond, The Cathedral and the Bazaar, 1998, www.tuxedo.org/~esr/writings/homesteading/cathedral-bazaar (current 15Feb. 2001).

21. B.A. Nardi, A Small Matter of Programming,MIT Press, Cambridge, Mass., 1993.

22. C. Alexander, Notes on the Synthesis of Form,Harvard Univ. Press, Cambridge, Mass., 1964.

23. G. Fischer et al., “Embedding Critics inDesign Environments,” Readings in Intelli-gent User Interfaces, M.T. Maybury and W.Wahlster, eds., Morgan Kaufmann, San Fran-cisco, 1998, pp. 537–561.

24. J. Ostwald, Knowledge Construction in Soft-ware Development: The Evolving ArtifactApproach, Ph.D. dissertation, Dept. of Com-puter Science, Univ. of Colorado, Boulder,Colo., 1996; www.cs.colorado.edu/~ostwald/thesis (current 15 Feb. 2001).

25. R. dePaula, G. Fischer, and J. Ostwald,“Courses as Seeds: Expectations and Reali-ties,” to be published in Proc. Euro-CSCL2001, Maastricht, Netherlands, 2001.

26. H.A. Simon, The Sciences of the Artificial,3rd ed., MIT Press, Cambridge, Mass., 1996.

27. C. Alexander et al., A Pattern Language:Towns, Buildings, Construction, Oxford Univ.Press, New York, 1977.

28. E.S. Raymond, Homesteading the Noosphere,2000, www.tuxedo.org/~esr/writings/home-steading/homesteading (current 15 Feb.2001).

72 computer.org/intelligent IEEE INTELLIGENT SYSTEMS

Gerhard Fischer is a professor of computer science, a fellow of the Insti-tute of Cognitive Science, and the director of the Center for Lifelong Learn-ing and Design (L3D) at the University of Colorado at Boulder. His researchincludes new conceptual frameworks and new media for learning, working,and collaboration; human–computer interaction; cognitive science; artificialintelligence; (software) design; and domain-oriented design environments.Contact him at the Center for LifeLong Learning and Design, Dept. of Com-puter Science, Univ. of Colorado, Campus Box 430, Boulder, CO 80309-0430; [email protected]; www.cs.colorado.edu/~gerhard.

Jonathan Ostwald is a research fellow at the Center for Lifelong Learning andDesign (L3D) at the University of Colorado at Boulder. His research interestsinclude human–computer interaction; computer support for design, learning, andcollaboration; and evolutionary and participatory models of software develop-ment. He received his PhD from the University of Colorado in Boulder. Con-tact him at the Center for LifeLong Learning and Design, Dept. of ComputerScience, Univ. of Colorado, Campus Box 430, Boulder, CO 80309-0430; [email protected]; www.cs.colorado.edu/~ostwald.

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