The knowledge chain model: activities for competitiveness

The knowledge chain model: activities for competitiveness

C.W. Holsapplea,*, M. Singhb

aSchool of Management, Carol M. Gatton College of Business and Economics, University of Kentucky, Lexington, KY 40506-0034, USAbDepartment of Management, M. J. Neeley School of Business, Texas Christian University, Box 298530, Fort Worth, TX 76129, USA

Abstract

Today, there is a growing recognition by researchers and practitioners about the importance of managing knowledge as a critical source for

competitive advantage. Various assertions about competitiveness through knowledge management (KM) are consistent with results of

empirical studies and lessons learned on the knowledge highways and byways. In spite of these macro-level contentions and success stories,

there has been little investigation of a systematic means for studying connections between KM activity and competitiveness. This paper

advances a knowledge chain model that identi®es and characterizes KM activities an organization can focus on to achieve competitiveness.

The model is analogous to Porter's value chain and is grounded in a descriptive KM framework developed via a Delphi-study involving

international KM experts. It is comprised of ®ve primary activities that an organization's knowledge processors perform in manipulating

knowledge resources, plus four secondary activities that support and guide their performance. Each activity is discussed in detail, including

examples. Evidence is provided from the literature illustrating each activity's role in adding value to an organization to increase its

competitiveness through improved productivity, agility, reputation, and innovation. In conclusion, we present some observations about

avenues for future research to extend, test, and apply the model in business practices. q 2001 Elsevier Science Ltd. All rights reserved.

Keywords: Competitiveness; Knowledge chain; Knowledge management activities; Model

1. Introduction

In an economy where the only certainty is uncertainty,

one source of lasting competitive advantage is knowledge

and its manipulation (Nonaka, 1991). Today, there is a

growing recognition in the business community about the

importance of managing knowledge as a critical source for

competitive advantage (Dutta, 1997). Researchers in the

®eld of sustainable competitive advantage have discovered

that knowledge, which includes what the organization

knows, how it uses what it knows and how fast it can

know something new, is the only thing that offers an orga-

nization a competitive edge (Prusak, 1996). Knowledge is

the thermonuclear competitive weapon of our time; knowl-

edge and its management are more valuable and more

powerful than natural resources, big factories, or fat bank-

rolls (Stewart, 1997). Robert Hiebler of Arthur Anderson

writes: ªThose companies that develop best practices for

managing knowledge capital will be the ones that ride this

competitive waveº (Hiebler, 1996).

Such assertions about competitiveness through knowledge

management (KM) are consistent with results of empirical

studies and lessons learned on the knowledge highways and

byways. According to a The Delphi Group, Inc. study, 85% of

respondents from more than 700 organizations see knowledge

management as providing logistical or strategic value to the

organization (Industry Trend or Event, 1997). Hughes Space

and Communications Co. and Ford Motor Co. are two compa-

nies that recognize and employ KM deftly to minimize costs

and cycle times while maintaining the companies' ability to

innovate (Strategic Leadership Forum, 1996). On the other

hand, one of the major reasons for three recent failures of

risk management Ð at Barings Bank, Kidder Peabody, and

Metallgesellschaft Ð appears to be due to unmanaged orga-

nizational knowledge (Marshall, Prusak, & Shpilberg, 1996).

A joint survey conducted by Business Intelligence and the

Ernst and Young Center for Business Innovation of 431 U.S.

and European organizations reports: `¼that more active

management of knowledge is possible and advisable Ð

indeed, that it is critical if a ®rm is to gain and sustain a

competitive advantage' (Ernst & Young, 1997). In the same

study, 87% of respondents describe their businesses as knowl-

edge-intensive, indicating knowledge and its manipulation as

being critical to their competitiveness. Similarly, in a survey

conducted by the Journal of Knowledge Management, over

90% of respondents perceived their organizations to be

knowledge intensive (Chase, 1997). However, a mere 6% of

the organizations were characterized as `very effective ' in

leveraging knowledge to yield better performance. As a step

Expert Systems with Applications 20 (2001) 77±98PERGAMON

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* Corresponding author. Tel.: 11-606-257-5236; fax: 11-606-257-8031.

E-mail address: [email protected] (C.W. Holsapple).

toward better leverage, it is helpful to have a model that iden-

ti®es the possible fulcrums. These fulcrums are the knowledge

management activities that can yield competitive advantage if

designed and executed better than those of other organizations.

All businesses involve creation, dissemination, renewal,

and application of knowledge toward organizational suste-

nance and survival in the face of increasingly discontinuous

environment change (Malhotra, 1998). Knowledge manage-

ment involves the use of categorical and structured

approaches that enable organizations to be competitive

(Ostro, 1997). But aside from macro-level contentions that

KM is a basis for competitiveness and an assortment of

success stories supportive of these contentions, there has

been little investigation of the connections between KM

and competitiveness. Speci®cally, what KM activities can

be contributors to competitiveness? An answer to this ques-

tion would identify key activities that deserve careful atten-

tion in an organization's quest to leverage its knowledge

into a competitive advantage. Each such activity can be a

focal point for improvements that match or surpass compe-

titors' executions of the same activity. Each is a candidate

for enhancements that add value to an organization.

This paper advances a model that identi®es and character-

izes KM activities an organization can focus on to enhance

its competitiveness. Called the Knowledge Chain model

(Holsapple & Singh, 2000), it is grounded in a descriptive

KM framework developed via a Delphi-study involving an

international panel of KM experts and is somewhat analo-

gous to Porter's value chain model. The value chain model,

a basic tool for diagnosing competitive advantage and ®nd-

ing ways to enhance it, identi®es technologically and

economically distinct activities (called `value activities')

that an organization performs in the course of doing busi-

ness (Porter, 1985). These value activities fall into nine

generic categories: ®ve primary and four secondary, and

translate an organization's broad competitive strategy into

speci®c action steps required to achieve competitiveness.

Similarly, the knowledge chain model posits nine

distinct, generic activities that an organization performs in

the course of managing its knowledge resources. These are

focal points for achieving competitiveness through knowl-

edge management. The knowledge chain model is

comprised of ®ve primary activities that an organization's

knowledge processors perform in manipulating knowledge

resources, plus four secondary activities that support and

guide performance of the primary activities. The model

gives a characterization of each activity. Evidence support-

ing its potential contribution to competitiveness is drawn

from the literature. The model is descriptive in nature. As

such, it is a basic tool for diagnosing knowledge-based

competitiveness and ®nding ways to enhance it.

The rest of the paper is organized as follows: Section 2

summarizes Porter's value chain model and its relationship

to competitiveness. In Section 3, a brief overview of knowl-

edge management is furnished. Section 4 provides an intro-

duction to the knowledge chain model. Sections 5 and 6

describe and illustrate the primary and secondary KM activ-

ities, respectively, and present literature support for the role

of each in competitiveness. Section 7 discusses some

avenues for future related research.

2. Competitive advantage and porter's value chainmodel

Potential sources of competitive advantage are every-

where in a ®rm (Porter, 1985). To highlight the idea that

competitive advantage grows fundamentally out of the

value a ®rm is able to create for its clientele, Porter invented

the value chain model comprised of nine value-adding

activities: ®ve primary and four secondary. These value

activities form a bridge between competitive strategy

formulation and implementation.

As Table 1 indicates, primary value activities involve mate-

rials handling, creating products, marketing and selling them,

delivery to buyers, and post-sale support and services. Second-

ary activities are development and operation of ®rm infra-

structure, human resource management, technology

development, and procurement. These support activities

provide inputs and infrastructure that allow the primary activ-

ities to take place. Every primary activity employs procured

inputs, human resources, and a combination of technologies.

Firm infrastructure, including such functions as general

management, legal work, and accounting, supports the entire

chain.

Improvements in the design and execution of value

activities can take such forms as cycle time reductions,

productivity increases, various types of cost reductions,

C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±9878

Table 1

Value chain activities

Activity De®nition

Primary

1. Inbound logistics Receiving, storing, and distributing

materials to manufacturing premises

2. Operations Transforming inputs into ®nished

products

3. Outbound logistics Storing and distributing products

4. Marketing and Sales Promotion and sales efforts

5. Service Maintain or enhance product value

through post-sale services.

Secondary

1. Corporate infrastructure Support for the entire value chain,

including general management,

planning, ®nance, accounting, legal

services, government affairs, and

quality management

2.Human resources management Recruiting, hiring, training, and

development of employees

3. Technology development Improving products and

manufacturing process

4. Procurement Purchasing inputs

opening new marketing and distribution channels, or

enabling just-in-time delivery. These improvements in

value activities can lead to competitive advantage for an

organization. Porter supports his model through ®eld

research, much of it personally conducted, and illustrations

of both successful and failed attempts to manage the value

chain (Porter, 1985).

The value chain model disaggregates a ®rm and system-

atically examines all the discrete but interrelated primary

and secondary activities that the ®rm performs. The result is

a means for analyzing the sources of competitive advantage.

According to Porter, there are two basic types of competi-

tive advantage: low cost and differentiation. Each of the

value activities, individually and/or in a complementary

fashion, can contribute to a ®rm's relative cost position

and create a basis for differentiation. Value activities are

therefore the discrete building blocks of competitiveness.

How each activity is performed combined with its econom-

ics, will determine whether a ®rm is high or low cost relative

to competitors. How each value activity is performed will

also determine its contribution to meeting customer needs

and hence differentiation. Comparing the value chains of

competitors exposes differences that determine competitive

advantage.

Porter and Miller (1985) point out that information perme-

ates the value chain, underlying the performance of every

value activity and the linkages among them. They conclude

that information can therefore give an organization competi-

tive advantage. From one organization to another, differences

in information and information handling within and across

value activities lead to differences in their competitive stand-

ings. Differences in information handling can be methodo-

logical and/or technologically based. In any case, the

information an organization has and its approaches to handling

that information in the conduct of value activities can form a

basis for implementing competitive strategies of cost reduc-

tion and differentiation.

3. Knowledge management: a brief background

The Porter and Miller view that information is a source of

competitive advantage is fully consistent with the emerging

perspective outlined in the introduction: namely that knowl-

edge management is a battleground for competitiveness.

However, KM involves much more than information.

Indeed, information is the name commonly given to one

type of knowledge: descriptive knowledge, which refers to

characterizations of past, current, or hypothetical states of

some world of interest. Other types of knowledge include

procedural, which characterizes how to do something, and

reasoning, which characterizes the extent to which particu-

lar conclusions are valid under particular circumstances

(Holsapple & Whinston, 1988; Holsapple, 1995). An

organization's processes and potentials derive from these

knowledge types. Any of the knowledge types can exist in

explicit or implicit modes in an organization (Nonaka &

Takeuchi, 1995). Each knowledge type is subject to manip-

ulation by human and/or computer-based processors.

An organization has both schematic and content knowledge

resources (Joshi, 1998). Schematic knowledge resources

include an organization's infrastructure, culture, strategy,

and purpose. Content knowledge resources include knowledge

held by the organization's human participants, by its compu-

ter-based processors, and by artifacts (e.g. books, production

equipment, audio tape library). Both schematic and content

resources can be studied in terms of knowledge type and

knowledge mode, as well as other knowledge attributes (e.g.

age, degree of perishability, subject domain). An organiza-

tion's knowledge resources are manipulated by organizational

participants, both human and computer. Mechanisms

employed in a given instance of knowledge manipulation

depend on speci®c attributes of the particular knowledge

resources being used, as well as the skills and predilections

of processors in action.

Recognition of a knowledge need within an organization

signals the start of a KM episode, which culminates with

either the satisfaction of that need or abandonment of the

effort (Joshi, 1998). Each KM episode can link with others

and can spawn a host of subsidiary KM episodes. Within a

KM episode, processors manipulate knowledge, but not in a

random fashion. At a meta level, other KM activities orches-

trate the patterns of these manipulation activities, as well as

the patterns of KM episodes. It follows that if KM is a key to

competitiveness for knowledge-based organizations in the

emerging knowledge economy, then the knowledge

management activities that comprise the dynamic fabric of

such organizations are keys in determining competitiveness.

Permeating the value chain, KM activities become mechan-

isms for achieving competitiveness through individual or

combinations of value activities.

In the knowledge economy, the value of knowledge as input

and output is growing, knowledge is a key ingredient of what is

bought and sold (both explicitly and implicitly), knowledge

resources are rising in importance relative to traditionally

recognized resources, and new technologies and techniques

for managing knowledge resources are emerging (Stewart,

1998). Knowledge management is concerned with ensuring

that the right knowledge is available in the right form to the

right processors at the right time for the right cost. Execution of

the KM activities undertaken in pursuit of this objective result

in a panorama of knowledge ¯ows within a knowledge-based

organization. In many cases, the manipulation activities and

the ¯ows that connect them can be performed, enabled, or

facilitated electronically.

Skills in executing knowledge management activities can

promote growth by allowing a ®rm to launch marketing and

business initiatives, as well as gain cost and other advan-

tages by improving and facilitating operational ideas

(Trussler, 1998). The emerging knowledge economy is

creating a revolution that is forcing companies to look for

ways to reinvent themselves, and the successful companies

C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±98 79

will be those that value knowledge and have a strategy for

systematically managing it (Tapp, 1997). Companies can

bene®t from knowledge management because it allows

them to innovate along the competitive edge of today's

business environment (Dykeman, 1998). However, while

most ®rms have technology for a knowledge management

program, few are exploiting it fully, says a report by consult-

ing ®rm KPMG (Black, 1998). According to IBM's Larry

Prusak: ªEvery company does manage knowledge to some

degree, but they can do it more effectively and more ef®-

cientlyº (PC Week Executive, 1996).

We contend that one key to more fully exploiting the

competitive potential of knowledge management is a

model that identi®es value-adding KM activities. Practi-

tioners could use the model to structure their consideration

and evaluation of KM initiatives. Researchers could use the

model to structure their exploration of connections between

KM and competitiveness. Educators and students could use

the model to help structure coverage of KM activities and

their impacts. These motivations, coupled with the absence

of such a model in the literature, lead us to advance the

knowledge chain model.

4. Overview of the knowledge chain model

The knowledge chain model is based on a descriptive KM

framework developed via a Delphi-study involving an inter-

national panel of prominent KM practitioners and academi-

cians (Joshi, 1998). This framework identi®es ®ve major

knowledge manipulation activities that occur in various

patterns within KM episodes. It also identi®es four major

managerial in¯uences on the conduct of knowledge

management. Respectively, these form the ®ve primary

and four secondary KM activities in the knowledge chain

model (Holsapple & Singh, 2000). As Fig. 1 suggests, these

activities yield organizational learning (i.e. changes in an

organization's state of knowledge) and projections (i.e.

organizational resources being released into the environ-

ment). A basic premise of the knowledge chain model is

that how well an organization learns and how well it

projects are important determinants of the organization's

viability and success in a competitive environment. The

remainder of this paper examines the nine KM activities

that underlie learning and projection, and offers evidence

from the literature that each of them can add value and be a

source of competitiveness.

The model's set of interrelated knowledge activities

appears to be common across diverse organizations. It

asserts that these are major activities with which a chief

knowledge of®cer needs to be concerned. KM skills of an

organization's participants need to be cultivated, harnessed,

and organized in the performance of these activities. Thus,

will an organization's knowledge resources lead to

enhanced competitiveness through the learning and projec-

tions it produces.

The primary activities that an organization's knowledge

processors perform in manipulating knowledge resources

are summarized in Table 2. When a particular instance of

a knowledge manipulation activity occurs in an organiza-

tion, it is performed by one or more processors. Some

processors are human and others may be computer-based.

Many processors may be capable of performing a given type

of primary activity. Conversely, multiple types of primary

activities may be performed by a given processor. More-

over, each primary activity involves sub-activities as

detailed in the next section.

An organization may possess the best knowledge

resources and the best knowledge manipulation skills, but

they are of no use until they are effectively applied during

the conduct of KM (Joshi, 1998). The Delphi study identi-

®ed three major kinds of forces that conspire to in¯uence

how the conduct of KM ultimately unfolds in an organiza-

tion: managerial in¯uences, resource in¯uences, and


Fig. 1. The knowledge chain model.

environmental in¯uences (Joshi, 1998). Because the

managerial in¯uences denote meta activities that impact

or determine the deployment of resources and patterns of

manipulation activities, they are included as secondary

activities in the knowledge chain model. Table 3

summarizes these secondary KM activities that support

and guide the performance of primary KM activities.

There is also an interplay among the secondary activities;

one may support or guide the performance of another.

As management scholars have pointed out, `espoused

theory' tells us little about real behavior; we need to study

`theory in practice' Ð i.e. view the actions that re¯ect

managerial conduct (Leonard-Barton, 1995). In this spirit,

we discuss each primary and secondary activity in more

detail in Sections 5 and 6, respectively. Examples of each

activity are presented, as well as evidence of its role in

adding value, either directly or indirectly, to an organization

and hence increasing its competitiveness. Some of the

evidence involves use of technology to better perform a

KM activity; some is not technology-based. Competitive-

ness due to KM practices can manifest itself in such ways as

increasing pro®ts and bolstering an organization's reputa-

tion, employees' creativity, productivity, ef®ciency, ¯ex-

ibility, and innovation (Strategic Leadership Forum,

1996). Therefore, we examine support for the competitive

role of each activity in terms of one or more of the following

standpoints: improving productivity (e.g. lower cost, greater

speed), enhancing reputation (e.g. better quality, depend-

ability, brand differentiation), enhancing organizational

agility (e.g. greater ¯exibility, rapid responsiveness, change

pro®ciency), and fostering innovation (e.g. new knowledge

products, services, processes).

5. Primary knowledge chain activities andcompetitiveness

This section examines primary activities that can be

carried out in the conduct of KM. We provide, together

with examples, a detailed characterization for each primary

activity and the sub-activities included in it. The primary

activities and their corresponding sub-activities are not

necessarily performed in any strict pattern, but rather there

can be various sequences, overlaps, and iterations among

them. The nature of these variations is in¯uenced by the

secondary activities. For each activity, evidence is provided

that suggests it can contribute to competitiveness. These

illustrations are representative rather than exhaustive and

some involve a combination of multiple activities which,

when performed in a superior fashion, lead to enhanced

competitiveness.

5.1. Knowledge acquisition

Knowledge acquisition refers to the activity of identify-

ing knowledge in the organization's external environment

and transforming it into a representation that can be inter-

nalized, and/or used for knowledge generation or externa-

lization. Sub-activities involved in acquiring knowledge

include:

Identifying appropriate knowledge from external sources

by locating, accessing, valuing, and/or ®ltering; capturing

the identi®ed knowledge from external sources by extract-

ing, collecting, and/or gathering knowledge deemed to be of

suf®cient reliability, relevance, and importance; organizing

the captured knowledge by distilling, re®ning, orienting,

interpreting, packaging, assembling, and/or transforming it

into usable representations; transferring the organized

knowledge to a processor(s) that immediately uses it or

internalizes it within an organization for subsequent use;

the activity receiving the transferal may or may not be

performed by the same processor that did the acquisition

(Joshi, 1998).

5.1.1. Examples of knowledge acquisition

Some examples of knowledge acquisition include,

conducting an external survey, acquiring a knowledge-rich

company, subjecting employees to external training, hiring

an employee (thereby bringing that person's knowledge into


Table 3

Secondary activities in the knowledge chain model

Knowledge leadership Establishing conditions that enable and

facilitate fruitful conduct of KM

Knowledge coordination Managing dependencies among KM

activities to ensure that proper

processes and resources are brought to

bear adequately at appropriate times

Knowledge control Ensuring that needed knowledge

processors and resources are available

in suf®cient quality and quality, subject

to security requirements

Knowledge measurement Assessing values of knowledge

resources, knowledge processors, and

their deployment

Table 2

Primary activities in the knowledge chain model

Knowledge acquisition Acquiring knowledge from external

sources and making it suitable for

subsequent use

Knowledge selection Selecting needed knowledge from

internal sources and making it suitable

for subsequent use

Knowledge generation Producing knowledge by either

discovery or derivation from existing

knowledge

Knowledge internalization Altering the state of an organization's

knowledge resources by distributing

and storing acquired, selected, or

generated knowledge

Knowledge Externalization Embedding knowledge into

organizational outputs for release into

the environment

the organization), purchasing data sets, monitoring the tech-

nological advances, purchasing a patented process, and

gathering knowledge via competitive intelligence.

Each of these examples can be examined in greater detail

via the sub-activities. A particular type of knowledge acqui-

sition depends on the nature of the processor doing the

acquisition, the nature of external knowledge resources

and their attributes, the way knowledge is represented in

those sources, and various constraints such as time, cost,

and quality. Also there can be variations in the emphasis

that a particular sub-activity gets within a particular act of

knowledge acquisition and the amount of work required to

accomplish it. In a given instance of knowledge acquisition,

the identi®cation sub-activity may be more challenging and

dif®cult than other sub-activities. In another instance, the

transferring sub-activity may involve little or no effort.

Moreover, a particular sub-activity is more or less amenable

to technology support, depending on the speci®cs of the

knowledge acquisition instance.

Consider the case of a data set purchased from an elec-

tronic brokerage on the Web staffed by soft OR agents

(Kalakota, Stallaert, & Whinston, 1996). The user needs

this data set to internalize it for future use, or apply it imme-

diately in generating new knowledge, or for immediate

externalization (e.g. resale) of the knowledge. The soft

OR agents, which actually carry out the knowledge acquisi-

tion, have built-in evaluation schemes to minimize knowl-

edge search costs for various pertinent data resources. Based

on a user's incoming request and budget, these agents locate

and collect an appropriate data set from data servers on the

Internet, evaluate and convert it if necessary according to a

user's requirement. Finally, the organized knowledge is

transferred to the user as an HTML document for immediate

use or to internalize it for subsequent use.

As a non-technological example, consider employees

engaging in external training to acquire knowledge. This

involves identifying appropriate training programs. If alter-

native candidates are located, then determining which is

appropriate can involve evaluations such as calculating

costs and assessing knowledge quality. During the training

session, employees capture knowledge from instructors via

lectures, discussion, hands-on practice, and/or role-playing.

Each employee personally organizes and internalizes

knowledge. After the training is over, employees may trans-

fer their acquired knowledge to their organization (e.g. by

conducting presentations to their colleagues). Or, they use

their acquired knowledge to generate other knowledge (e.g.

make decisions).

5.1.2. Competitiveness via knowledge acquisition: some

evidence

Table 4 summarizes some evidence from the literature indi-

cating that competitiveness can be achieved through knowl-

edge acquisition. The ®rst two of these are described here.

Chaparral Steel CEO Gordon Forward states that `One of

our core competencies is monitoring, rapid acquisition and

realization of new technological advances into steel

products' (Leonard-Barton, 1995). Chaparral very actively

identi®es external sources of expertise through more than

the usual publication channels because Forward says: `By

the time you hear about a technology in a paper at a confer-

ence, it is too late.' After identifying the best suppliers of

technical expertise, the company pushes them to distill and

re®ne their offerings far beyond current designs and

products. The re®ned knowledge is captured and then

used to constantly improve production processes. This

policy has been rewarded by the market, and in an almost

two-decade-long history, Chaparral has set world records

for productivity a number of times.

Digital Equipment Corporation's Corporate Library

Group has introduced the WebLibrary. Its main focus is

using Web technology to identify, evaluate, analyze, synthe-

size, qualify, and accumulate externally created knowledge

content (Kennedy, 1997). Its value proposition is to provide

consistent, reliable, authoritative external content, and cred-

ible content expertise for effective in-house decision making

and timely transference of knowledge anytime, anywhere in

the organization. It ensures that the corporation has the

external knowledge it needs to run its businesses, and

more importantly, ensures the acquired knowledge is busi-

ness-driven, can be tied to business impact, and ultimately

affects decision outcomes. This has earned DEC a respected

reputation for reliable, consistent, trustworthy content that is

applied and used to create and build internal knowledge

assets.

5.2. Knowledge selection

Selecting knowledge refers to the activity of identifying

needed knowledge within an organization's existing knowl-

edge resources and providing it in an appropriate represen-

tation to an activity that needs it (i.e. to an acquiring,

internalizing, generating, or externalizing activity). Sub-

activities in selecting knowledge include: Identifying appro-

priate knowledge within the organization's existing

resources by locating, accessing, valuing, and/or ®ltering;

capturing the identi®ed knowledge from internal sources by

extracting, collecting, and/or gathering knowledge deemed

to be of suf®cient reliability, relevance, and importance;

organizing the captured knowledge by distilling, re®ning,

orienting, interpreting, packaging, assembling, and/or trans-

forming understandable representations; and transferring

the organized knowledge to a processor(s) that immediately

uses it or internalizes it within an organization for subse-

quent use; the activity receiving the transferal may or may

not be performed by the same processor that did the selec-

tion (Joshi, 1998).

Knowledge selection is analogous to acquisition, the

main distinction being that it manipulates knowledge

resources already existing in the organization, rather than

those in the environment. It plays a pivotal role in the

conduct of KM in an organization. It is through this activity



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ina

fast

-movin

g

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sin

ess

lik

eb

iote

ch

Pro

duct

ivit

y,

innovat

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ate

4.

Leo

nar

d-B

arto

n,

19

95

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dak

'scu

ltu

ral

min

dse

tal

low

sit

tohir

eth

ebes

tch

emic

al

eng

inee

rsth

ereb

yen

han

cing

Kodak

'sre

puta

tion

and

innovat

ion

in

chem

ical

pro

cess

ing

Rep

uta

tion,

innovat

ion

Lit

tle

5.

Wri

gh

t,1

99

8B

lack

and

Dec

ker

's`S

trik

eF

orc

e'(r

ovin

gban

ds

of

emplo

yee

s)

ob

serv

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sto

mer

sin

thei

r`n

ativ

e'en

vir

onm

ent

beh

avin

gfr

eely

wit

hp

roto

typ

ical

tools

;it

elic

its

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captu

res

cust

om

erfe

edbac

k

via

ora

lfo

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op

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nded

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tions,

and

body

languag

e;an

d

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quir

edto

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ket

ing

and

pro

duct

dev

elo

pm

ent

for

kn

ow

ledge

gen

erat

ion.

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has

hel

ped

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ckan

d

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ker

no

to

nly

tocr

eate

new

pro

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san

dse

rvic

esbut

also

to

enh

ance

its

imag

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Rep

uta

tion,

innovat

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Lit

tle

that the other activities interact with the existing knowledge

resources. Hence, it acts as an interface between organiza-

tional knowledge resources and other knowledge manipula-

tion activities. Also, the link between selecting and

internalizing is crucial. Only knowledge that has already

been internalized can be selected, and poor internalization

will lead to poor selection even in the presence of good

selection skills. To foster intelligent and customized knowl-

edge selection, it is crucial to internalize knowledge about

knowledge (i.e. meta-knowledge). Meta-knowledge allows

knowledge selection based on context as well as content

(Joshi, 1998).

5.2.1. Examples of knowledge selection

Some examples of knowledge selection include, selecting

quali®ed employees to participate in a product development

team, selecting an appropriate procedure for forecasting,

extracting needed information from a repository database

(e.g. a corporate library, employee's memory), or observing

behaviors of participants in an organization.

As with knowledge acquisition, speci®c instances of

knowledge selection can be investigated in greater detail,

there are variations in the emphasis that a particular sub-

activity gets and the amount of work required to accomplish

it, and in its amenability to technology support.

As an example, consider the case of the Folio Viewswprocessor executing a query to select needed information

from a database. The related processor, Folio Builderw,

internalizes knowledge from multiple sources into a reposi-

tory known as a Folio infobase from which Folio Views

selects knowledge in response to a seeker's request (Holsap-

ple & Joshi, 1999). This selection involves the identi®cation

and capture of needed knowledge. The captured knowledge

can be organized in various ways as speci®ed by the knowl-

edge seeker. For instance, it can create a dynamically linked

table of contents for captured knowledge that helps a seeker

use selection results in an organized fashion. Folio Views

transfers captured knowledge to the user via e-mail or it can

be internalized on an Intranet server.

According to Drucker (1993), knowledge workers will

tend to operate more in task forces involving specialists

from various functions to work together to accomplish

some task. Selecting quali®ed employees to participate in

a product development task team may be regarded as a non-

technological example of knowledge selection. Each

employee has descriptive, procedural, and reasoning knowl-

edge in explicit and/or tacit modes. An employee's knowl-

edge is made available to an organization by means of that

employee's knowledge manipulation skills (Joshi, 1998).

Forming a team is essentially an act of knowledge selection

in which appropriate employees (i.e. appropriate knowl-

edge) are identi®ed, assigned to the team (captured), and

in an organized fashion, given responsibilities according

to the knowledge they bring to bear on the team's product

development work. Effective knowledge selection results in

an atmosphere conducive not only to subsequent knowledge

sharing, but also to true problem solving and value creation

for an organization (Markland, Vickery, & Davis, 1995).

5.2.2. Competitiveness via knowledge selection: some

evidence

After the U.S. airline industry was deregulated in 1978,

American Airlines installed a SABRE reservation system

that was used not only to book its own ¯ights but also

those of its competitors (Applegate, McFarlan, & McKen-

ney, 1996). As a result, American had internalized in its

database knowledge of reservation levels of all ¯ights

offered by those competitors. SABRE's selection facility,

which was not fully available to its competitors, enabled

American to locate, ®lter, and extract knowledge about

competing ¯ights that are performing well. The selected

knowledge was used in generating its own aggressive

competitive countermeasures for those routes. This rapid

responsiveness had given American a ®rst-mover advan-

tage.

EstateCo, a mid-sized manufacturing ®rm, knew that one

of its products was not selling well and it was not under-

stood why this was so (Broadbent, 1998). The product had

taken many years and dollars to develop. The information

systems gave accurate information about how much of the

product was selling and where it was selling, but could offer

no reasons why or what could be done about the poor sales

performance. Consequently, a system was developed to

identify, value, and capture the insights (i.e. tacit knowl-

edge) of ®eld staff about why the product was not attractive

to customers. This knowledge selection approach consisted

of both face-to-face debrie®ngs with sales executives and

electronic discussion involving the sales executives,

marketers, product developers, and ®eld staff. Following

the debrie®ngs and electronic discussions, some minor but

important changes in the product design were identi®ed and

the product was re®ned within a short turnaround time. A

renewed marketing effort was made by the sales staff and

the product became very successful.

Table 5 summarizes representative examples of contribu-

tions knowledge selection approaches have made to compe-

titiveness.

5.3. Knowledge generation

Generation is an activity that produces knowledge by

discovering it or deriving it from existing knowledge,

where the latter has resulted from acquisition, selection,

and/or prior generation. Sub-activities involved in generating

knowledge include: Monitoring the organization's knowl-

edge resources and the external environment by invoking

selection and/or acquisition activities as needed; evaluating

selected or acquired knowledge in terms of its usability for

the generation task; producing knowledge from a base of

existing knowledge by creating, synthesizing, analyzing,

and constructing; transferring the produced knowledge for

externalization and/or internalization; the activity receiving



Tab

le5

Know

ledge

sele

ctio

npra

ctic

esre

sult

ing

inco

mpet

itiv

enes

s

Cit

atio

nA

ctio

nto

add

val

ue

Sta

ted

com

pet

itiv

eim

pac

tD

egre

eof

tech

nolo

gy

involv

ed

1.

Apple

gat

eet

al.,

1996

Am

eric

anA

irli

ne'

sS

AB

RE

rese

rvat

ion

syst

emlo

cate

s,®

lter

s,an

d

extr

acts

kn

ow

led

ge

about

com

pet

ing

and

succ

essf

ul

¯ig

hts

off

ered

by

com

pet

itors

asa

bas

isfo

rgen

erat

ing

know

ledge

about

com

pet

itiv

e

counte

rmea

sure

sto

imple

men

tth

ose

route

s

Agil

ity,

innovat

ion

Exte

nsi

ve

2.

Bro

adb

ent,

19

98

Est

ateC

oid

enti

®es

,val

ues

,and

captu

res

the

insi

ghts

(i.e

.tac

itknow

ledge)

of

®el

dst

aff

about

why

the

pro

duct

was

not

attr

acti

ve

tocu

stom

ers.

This

acqu

ired

kn

ow

led

ge

isuse

dto

re®

ne

pro

duct

des

ign

wit

hgre

atsu

cces

s

Innovat

ion

Moder

ate

3.

Lau

do

nan

dL

audon

,1

99

8F

ord

's`v

irtu

alco

-lo

cati

on'

des

ign

team

iden

ti®

esan

dca

ptu

res

uniq

ue

know

ledge

from

its

emplo

yee

ssc

atte

red

thro

ughout

the

worl

dfo

r

sub

sequ

ent

use

ing

ener

atin

gca

rdes

igns.

Des

ign

and

dev

elopm

ent

tim

eis

redu

ced

sig

ni®

can

tly

Pro

duct

ivit

y,

agil

ity

Moder

ate

4.

Wri

gh

t,1

99

8P

lati

nu

mT

ech

nolo

gy's

sale

sfo

rce,

dea

ling

wit

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pid

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wth

and

70

rapid

-®re

acqu

isit

ions,

use

sa

quer

yover

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anet

sto

sele

ctup-t

o-d

ate

pro

du

ctan

dco

mp

any

know

ledge,

for

use

ingen

erat

ing

dec

isio

ns.

The

resu

ltis

a7

%p

rod

uct

ivit

yim

pro

vem

ent.

Pro

duct

ivit

yE

xte

nsi

ve

5.

Rif

kin

,1

99

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man

agin

gd

irec

tor

of

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man

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s'A

sian

faci

liti

esre

ques

ted

kn

ow

led

ge

abo

ut

pit

ch-c

ontr

ol

stra

tegie

sfr

om

all

emplo

yee

sw

orl

dw

ide

usi

ng

aknow

ledge

sele

ctio

nsy

stem

-K'N

etix

.W

ithin

afe

whours

K'N

etix

loca

ted

,co

llec

ted,pac

kag

edth

eap

pro

pri

ate

know

ledge

rece

ived

from

11

sou

rces

and

tran

sfer

red

itto

the

per

son

reques

ting

the

know

ledge

thus

enab

lin

gh

imto

secu

rea

$6

mil

lion

ord

er

Pro

duct

ivit

y,

agil

ity

Exte

nsi

ve

the transferal may or may not be performed by the same

processor that did the generation (Joshi, 1998).

Derivation involves the use of process knowledge (e.g.

procedures, rules) and descriptive knowledge (e.g. data,

information) to generate new process and/or descriptive

knowledge employing KM skills that are of an analytical,

logical, and constructive nature. Although the result is `new'

to the processor that derives it, it may have previously

existed but not have been externalized or it may already

exist elsewhere in the organization but not be subject to

facile selection. Discovery generates knowledge in less

structured ways, via skills involving creativity, imagination,

and synthesis.

5.3.1. Examples of knowledge generation

Knowledge generation examples include, deriving a fore-

cast, making a decision, recognizing or solving a problem,

inventing a process, brainstorming, devising a promotional

strategy, constructing a software routine, discovering a

pattern, and achieving a creative insight.

As with acquisition and selection, each instance of

knowledge generation can be investigated in terms of sub-

activities, with the emphasis, challenges, and technology of

sub-activities varying from one instance of generation to

another.

Consider decision making, a knowledge-intensive

process that aims to produce new knowledge indicating a

commitment to some course of action (Holsapple, 1995).

Before a decision is made, the knowledge about what course

of action will be taken does not exist. A decision is not

acquired, nor is it selected. It is produced by deriving it

from existing knowledge that has been acquired, selected,

and/or previously generated. This derivation typically

involves the use of procedural knowledge and reasoning

knowledge, as well as descriptive knowledge (Holsapple

& Whinston, 1996). Aside from analysis and reasoning,

discovery can occur in the production of a decision. Discov-

ery refers to the use of creativity, insight, and intuition in

generating new knowledge.

Brainstorming is another example of knowledge genera-

tion. Its objective is the creation of new knowledge through

an interaction of processors that brings their different skills

and knowledge sets into contact, resulting in knowledge that

could not be readily acquired or selected. Knowledge

generation can also happen in problem-solving. That is, a

problem's solution may be generated via derivation and/or

discovery if it cannot be readily selected or acquired.

5.3.2. Competitiveness via knowledge generation: some

evidence

Table 6 summarizes some examples of enhancing compe-

titiveness achieved through a superior approach to knowl-

edge generation. The ®rst three of these are described in

more detail here.

Cadila Laboratories, a major pharmaceutical company,

achieved a distinct competitive advantage over other

pharmaceutical companies in India by building a compu-

ter-based expert system for drug preformulation (Ramani,

Patel, & Patel, 1992). Preformulation consists of investigat-

ing a drug's physical, chemical, and biological properties

alone and in combination with other chemicals (called exci-

pients) included for their therapeutic and production-

process properties. Using the properties of the main drug

as inputs, the expert system's inference engine drew on the

content of the knowledge base to generate and convey

advice (i.e. to derive knowledge) in two steps. By helping

R and D staff in identifying compatible excipients, the

expert system has reduced the time required to make prefor-

mulation decisions by 35%.

Some companies succeed in de®ning new industries by

generating new knowledge and opportunities based on exist-

ing knowledge embedded in existing products and

processes. Until the early 1990s, Enron was a gas pipeline

transmission company like many others. But its managers

realized that embedded in what appeared to be a commodity

gas business was valuable knowledge about product ¯ow,

supply, and demand (McKinsey Quarterly, 1998). The

company exploited this wealth of knowledge to generate

an innovative range of risk management contracts handled

by a new business called Enron Capital and Trade

Resources. This enterprise helped Enron grow its sales by

7% per year and its shareholder returns by 27% per year

between 1988 and 1995.

Many of Chase Manhattan Bank Corp.'s corporate custo-

mers call to ask to have processing fees waived or lowered.

But waived fees are a special privilege the bank wants to

reserve only for its best customers. Because of the $16

million investment in a decision support system called Rela-

tionship Management System (RMS), Chase now knows in

real time who its best customers are (Cole-Gomolski, 1997).

Using RMS, 2,500 Chase employees can monitor and

analyze customers' loan histories, deposits, investments,

and other knowledge in real time. Based on this existing

knowledge, RMS can generate new knowledge in the

form of complete customer pro®les. Then, using a built-in

evaluator in RMS on customer pro®les, Chase can make

consistent, accurate, and faster decisions about customers'

requests. More than a year after RMS was developed, it has

delivered at least $11 million in increased revenues and

reduced costs. Much of the ®nancial gain can be attributed

to increases in productivity of decision making.

5.4. Knowledge internalization

Internalizing is an activity that alters an organization's

knowledge resources based on acquired, selected, or gener-

ated knowledge. It receives knowledge ¯ows from these activ-

ities and produces knowledge ¯ows that impact the

organization's state of knowledge. Sub-activities include:

Assessing knowledge to be internalized with requisite cleans-

ing, re®ning, and ®ltering; targeting knowledge resources that

are to be impacted by internalization; structuring knowledge



Tab

le6

Know

ledge

gen

erat

ion

pra

ctic

esre

sult

ing

inco

mpet

itiv

enes

s

Cit

atio

nA

ctio

nto

add

val

ue

Sta

ted

com

pet

itiv

eim

pac

tD

egre

eof

tech

nolo

gy

involv

ed

1.

Co

le-G

om

ols

ki,

19

97

Chas

eM

anhat

tan

Ban

k's

RM

Sdec

isio

nsu

pport

syst

emra

pid

lybuil

ds

com

ple

te

cust

om

erpro

®le

sra

pid

lyfr

om

exis

ting

know

ledge

and

der

ives

eval

uat

ions

of

them

.

Chas

euse

sth

isknow

ledge

ingen

erat

ing

consi

sten

t,ac

cura

te,

and

fast

erdec

isio

ns

abo

ut

cust

om

ers

Pro

duct

ivit

y,

agil

ity

Exte

nsi

ve

2.

McK

inse

yQ

uar

terl

y,

19

98

En

ron

dis

cov

ers

val

uab

leknow

ledge

about

pro

duct

¯ow

,su

pply

,an

ddem

and

embed

ded

init

sg

asb

usi

nes

s.F

rom

this

itder

ived

anin

novat

ive

range

of

risk

man

agem

ent

con

trac

tsle

adin

gto

anew

busi

nes

sth

atin

crea

sed

shar

ehold

erre

turn

Agil

ity,

innovat

ion

Lit

tle

3.

Had

ad,

19

98

IBM

isim

ple

men

tin

g`K

now

ledge

Cock

pit

'sy

stem

emplo

yin

gad

van

ced

know

ledge-

min

ing

tech

niq

ues

toev

aluat

e,dis

cover

,an

dsy

nth

esiz

ehig

h-q

ual

ity

know

ledge

for

its

pro

fess

ional

sto

use

inhel

pin

gcl

ients

tobe

succ

essf

ul

inth

eir

busi

nes

s

Pro

duct

ivit

y,

agil

ity,

reputa

tion

Exte

nsi

ve

4.

Go

tsch

all,

19

99

Usi

ng

spec

iali

zed

bio

tech

nolo

gy

and

bio

med

ical

know

ledge

man

agem

ent

soft

war

e,

Info

rMax

anal

yze

sex

isti

ng

know

ledge

about

DN

Aan

dpro

tein

mole

cule

sto

hel

p

dis

cover

new

know

ledge

about

how

tosp

eed

up

the

even

tual

crea

tion

of

those

mole

cule

sin

the

lab

.T

hes

enew

mole

cule

sar

eth

enuse

dto

crea

tenew

dru

gs

fast

er

and

more

accu

rate

lyth

anin

the

pas

tan

dw

ith

less

cost

lydru

gtr

ials

.

Pro

duct

ivit

y,

agil

ity,

innovat

ion

Exte

nsi

ve

5.

Had

ad,

19

98

Usi

ng

curr

ent

kn

ow

led

ge

about

var

ied

level

sof

emplo

yee

skil

lsan

dex

per

ience

s,

CIG

NA

gen

erat

edan

opti

mal

stra

tegy

for

the

com

pan

yto

`go

from

bei

ng

agen

eral

ist

insu

rer,

that

would

insu

rean

yty

pe

of

busi

nes

s,to

asp

ecia

lty

insu

rer,

only

acti

ve

in

cert

ain

mar

ket

s,in

cert

ain,

geo

gra

phic

regio

ns'

Rep

uta

tio

n,

innovat

ion

Moder

ate

to be conveyed into representations appropriate for the

targeted resources, including abstracting, indexing, sorting,

labeling, categorizing, and integrating; delivering the knowl-

edge representations to targeted knowledge resources. This

distribution and sharing results in modi®cation to these

resources (Joshi, 1998). Internalizing knowledge is a culmi-

nating activity in organizational learning.

5.4.1. Examples of knowledge internalization

Possible examples of knowledge internalization include,

knowledge sharing, in-house training, populating a data

warehouse, posting an idea on an intranet, publishing a

policy manual, broadcasting a new regulation via e-mail,

modifying organizational culture or infrastructure, making

experts' knowledge available by developing expert systems.

As with previously discussed KM activities, there is consid-

erable variation in how particular instances of knowledge

internalization are performed. One of the issues related to

internalization is how to internalize knowledge to facilitate

quick future knowledge selection (Joshi, 1998).

As an example, consider McKinsey and Bain and Co. It

has established a computer system that holds experiences

from various team assignments (Sveiby, 1997). Knowledge

generated from each assignment is structured into a compu-

ter-usable representation and internalized in the system's

database. Later, it can be selected by employees for future

assignments. In this example, suitability of knowledge to be

internalized is assessed by team members. They then target

a computer system where it is to reside and complete the

internalization by depositing it there.

Modifying an organization's culture is an example of

internalization that deals with schematic rather than content

knowledge. An organization's values, principles, norms,

tacit rules and procedures comprise its cultural knowledge

resource (Joshi, 1998). Instead of penalizing risk taking and

failure, management may change the culture to encourage

and promote the values of high tolerance for risk taking

(Leonard-Barton, 1995). The knowledge that a positive atti-

tude towards risk taking is crucial to the organization's

success becomes ingrained in its culture. This cultural

change can manifest as frequent experimentation performed

by employees to solve problems that allow the organization

to be innovative and creative.

5.4.2. Competitiveness via knowledge internalization: some

evidence

Hoffman-LaRoche embarked on implementing knowl-

edge management practices in the early 1990s as part of

its commitment to excellence and innovation in manage-

ment (Broadbent, 1998). On assessing its product develop-

ment plans and their implementations, Roche found that it

did not always communicate consistent key messages and

sometimes included contradictory, ambiguous, and inap-

propriate information. Moreover, it concluded that its

employees did not have access to the company's knowledge

and were not adequately sharing knowledge or a vision of its

product. In an effort to overcome this, Roche's KM project

team developed a corporate knowledge map as a basis for

better internalizing the rich pool of knowledge that was

previously buried in relatively inaccessible pockets within

the company. The result was a computer system for facil-

itating knowledge sharing. The system helped Roche get

more drugs to market and get them more quickly.

The thoroughness of McDonald's best practice approach

is legendary (McKinsey Quarterly, 1998). McDonald's

pursues an essentially centralized model in which the

corporation re®nes and de®nes rigid standards not only for

its products but also for the processes that deliver them. It

also benchmarks performance, sets aspirations, and makes

product mix and service decisions. This generated knowl-

edge is internalized by targeting it to restaurant managers

who attend Hamburger University in Illinois for 1,500 to

3,000 hours of training. In many service industries, a key

driver of value is not only the ability to generate (discover or

derive) best practices, but to internalize them workforce.

Such a strategy can create powerful brands that are conti-

nually refreshed as knowledge about how to serve custo-

mers better travels across the network (McKinsey Quarterly,

1998). McDonald's has indeed created competitive advan-

tage through brand differentiation.

Table 7 summarizes representative cases where particular

knowledge internalization approaches contribute to compe-

titiveness.

5.5. Knowledge externalization

Externalizing knowledge is an activity that uses existing

knowledge to produce organizational outputs for release

into the environment. It yields projections (i.e. embodiments

of knowledge in outward forms) for external consumption,

in contrast to internalization which may also yield projec-

tions, but which are retained as knowledge resources. Exter-

nalization is only partially a KM activity because it also can

involve physical activities such as production through raw

material transformation. Sub-activities involved include

targeting the output. This is concerned with recognizing

what needs to be produced for targeted elements of the

environment; producing the output by applying, embody-

ing, controlling, and leveraging existing knowledge to

produce output for the target. This output is a representation

of the knowledge used to produce it; transferring the output

by packaging and delivering the projections that have been

produced for targets in the environment (Joshi, 1998). The

process of effective projection adds value to an organiza-

tion. The value can be added in various forms such as prof-

its, image, customer loyalty, and visibility. Once

externalization occurs, its impact can be captured through

the knowledge acquiring activity (Joshi, 1998).

5.5.1. Examples of knowledge externalization

Examples of knowledge externalization include,

providing technical support, giving lectures/presentations,


manufacturing a product, developing an advertisement,

producing a market research report and publishing.

Instances of externalization can be examined in terms of

the sub-activities, the processors that perform them, or the

mechanisms used in executing them.

For example, manufacturing a product involves targeting

the product to a speci®c market (low income group vs. high

income group). Manufacturing the product is an exercise in

applying the product design knowledge and process knowl-

edge. This externalization culminates with the transferal of

the product into the external environment. A product thus

released into the market is a representation of knowledge

used to build it. Disassembling the product during reverse

engineering would reveal at least some knowledge that went

into manufacturing it (Joshi, 1998). The knowledge

projected via externalization can vary from tacit to explicit.

The approaches to delivery depend on the nature of the

product, so that externalizing them may involve physical,

electronic, and/or audio/visual means.

5.5.2. Competitiveness of knowledge externalization: some

evidence

Genentech lets its scientists publish their ®ndings imme-

diately in leading journals that target audiences in their

®elds (Allee, 1997). In the past, publication delays made

it impossible for the scientists to be ®rst in their ®elds to

transfer their knowledge into the environment, which is

important for career recognition. An approach to externali-

zation that allows immediate submission of ®ndings for

publication has enhanced Genentech's competitiveness by

helping it to recruit top talent leading to a rank of fourth

among research institutions in molecular biology and genet-

ics.

A large manufacturer of industrial machinery has

installed an expert system on its home-of®ce computer to

support effective and ef®cient product maintenance to its

customers (Applegate et al., 1996). When a machine failure

occurs on a customer's premises, the machine is connected

via telephone to the manufacturer's computer, which

performs an analysis of the problem, develops procedural

knowledge in the form of instructions, and delivers them to

the machine operator. This knowledge externalization meth-

odology has decreased service visits by 50%, while custo-

mer satisfaction has increased signi®cantly.

Additional evidence that contribution to competitiveness

can be achieved through knowledge externalization is

summarized in Table 8.

6. Secondary knowledge chain activities andcompetitiveness

This section concentrates on secondary activities that

support and guide the performance of primary knowledge

manipulation activities. As in the case of primary activities,

we provide examples and detailed characterization for each


Tab

le7

Know

ledge

inte

rnal

izat

ion

pra

ctic

esre

sult

ing

inco

mpet

itiv

enes

s

Cit

atio

nA

ctio

nto

add

val

ue

Sta

ted

com

pet

itiv

eim

pac

tD

egre

eof

tech

nolo

gy

involv

ed

1.

Bro

adb

ent,

19

98

Ro

che'

sK

Mp

roje

ctte

amdev

eloped

aco

rpora

teknow

ledge

map

toin

tern

aliz

eit

sri

chpool

of

kn

ow

led

ge.

Th

esy

stem

fost

ers

know

ledge

shar

ing

that

hel

ps

Roch

eto

get

more

dru

gs

to

mar

ket

more

qu

ick

ly

Pro

duct

ivit

y,

agil

ity

Exte

nsi

ve

2.

McK

inse

yQ

uar

terl

y,

19

98

McD

onal

ds

gen

erat

esk

now

ledge

per

tain

ing

tost

andar

ds,

aspir

atio

ns,

pro

duct

mix

and

serv

ice

dec

isio

ns.

Eff

ecti

vel

yin

tern

aliz

ing

this

know

ledge

isa

key

dri

ver

of

val

ue

Rep

uta

tion

Lit

tle

3.

Cal

law

ay,

19

96

Hil

ton

Ho

tels

Co

rp.

has

esta

bli

shed

adat

abas

eof

more

than

400

solu

tions

topro

ble

ms

Hil

ton

use

rsfr

equ

entl

yen

counte

r.In

the

cours

eof

inte

rnal

izin

gth

isknow

ledge,

its

suit

abil

ity

isas

sess

edby

hote

lm

anag

emen

tex

per

ts.

Sel

ecti

ng

from

this

inte

rnal

ized

kn

ow

led

ge,

hel

pd

esk

emplo

yee

sso

lve

more

than

80%

of

use

rs'

pro

ble

ms

inth

e®

rst

call

Pro

duct

ivit

y,

reputa

tion

Moder

ate

4.

Gal

agan

,1997

Cooper

san

dL

ybra

nd

launch

edan

Intr

anet

syst

emca

lled

Know

ledge-

Curv

eth

atas

sess

es

and

inte

rnal

izes

all

kin

ds

of

com

pan

yan

dco

mpet

itor

info

rmat

ion

inone

easy

-to-r

each

pla

ce.T

he

syst

emsa

ves

the

com

pan

ym

ore

than

$1

mil

lion

ayea

rif

itsh

aves

just

one

hour

a

wee

kfr

om

the

tim

eth

atau

dit

ors

and

consu

ltan

tssp

end

sele

ctin

gre

levan

tknow

ledge

Pro

duct

ivit

y,

agil

ity

Exte

nsi

ve

5.

Alt

er,

1997

By

inte

rnal

izin

gbes

tpra

ctic

eson

anIn

tran

et,C

hev

ron

saved

$170

mil

lion

thro

ugh

reduce

d

use

of

elec

tric

po

wer

and

fuel

Pro

duct

ivit

yM

oder

ate


Tab

le8

Kn

ow

led

ge

exte

rnal

izat

ion

pra

ctic

esre

sult

ing

inco

mp

etit

iven

ess

Cit

atio

nA

ctio

nto

add

val

ue

Sta

ted

com

pet

itiv

eim

pac

tD

egre

eof

tech

nolo

gy

involv

ed

1.

All

ee,

1997

Gen

ente

chle

tsit

ssc

ienti

sts

publi

shth

eir

®ndin

gs

imm

edia

tely

inle

adin

gjo

urn

als

inth

eir

®el

ds.

This

exte

rnal

izat

ion

pra

ctic

ehas

hel

ped

Gen

ente

chre

cruit

top

tale

nt

and

rank

hig

hly

among

pee

rre

sear

chorg

aniz

atio

ns

Rep

uta

tion

Lit

tle

2.

Ap

ple

gat

eet

al.,

19

96

Ala

rge

man

ufa

ctu

rer

of

indust

rial

mac

hin

ery

has

inst

alle

dan

exper

tsy

stem

on

its

hom

e-

of®

ceco

mp

ute

rto

per

form

effe

ctiv

e,ef

®ci

ent,

and

rapid

exte

rnal

izat

ion

of

pro

duct

mai

nte

nan

cek

no

wle

dge

toit

scu

stom

ers.

When

am

achin

efa

ilure

occ

urs

on

acu

stom

er's

pre

mis

es,

the

mac

hin

eis

connec

ted

over

ate

lephone

line

toth

em

anufa

cture

r's

com

pute

r,

wh

ich

per

form

san

anal

ysi

sof

the

pro

ble

man

ddev

elops

pro

cedura

lknow

ledge

inth

efo

rmof

inst

ruct

ion

san

dd

eliv

ers

them

toth

em

achin

eoper

ator

Pro

duct

ivit

y,

agil

ity,

reputa

tion

Exte

nsi

ve

3.

Ste

war

t,1

99

8L

inco

lnR

e,o

ne

of

the

larg

est

and

bes

tre

gar

ded

com

pan

ies

inth

ehea

lth

and

life

rein

sura

nce

bu

sin

ess,

pro

vid

esa

hig

her

know

ledge-

conte

nt

solu

tions

than

thei

rco

mpet

itors

by

anal

yzi

ng

ever

yp

oin

to

fth

ecu

sto

mer

'sval

ue

chai

n,

mat

chin

git

snee

ds

agai

nst

Lin

col

Re'

sex

per

tise

,

and

ensu

rin

gth

atth

isk

now

ledge

isad

ded

toth

eso

luti

on.

This

exte

rnal

izat

ion

appro

aches

incr

ease

shar

eho

lder

val

ue

mea

sure

dby

dis

counte

dca

sh¯

ow

test

s

Pro

duct

ivit

y,

Rep

uta

tion

Lit

tle

4.

Ste

war

t,1998

Tie

Logis

tics

sele

cts,

org

aniz

es,dis

till

s,an

dpac

kag

esex

isti

ng

know

ledge

about

rail

carr

iers

and

use

rsto

`pro

du

ctiz

e'it

thro

ugh

the

Com

man

dan

dR

EZ

1co

mpute

rsy

stem

s.T

hes

e

syst

ems

exte

rnal

ize

ase

ries

of

off

erin

gs

for

its

clie

nte

lera

ngin

gfr

om

serv

ices

for

trac

kin

g

ship

men

tsto

dev

elo

pin

gin

dust

rybes

tpra

ctic

es.

Thes

enew

serv

ices

hav

elo

wer

ed

cust

om

ers'

cost

Pro

duct

ivit

y,

Innovat

ion

Exte

nsi

ve

5.

Ern

stan

dY

ou

ng,

19

97

Ern

ie,

ano

n-l

ine

con

sult

ing

syst

emfr

om

Ern

stan

dY

oung,

exte

rnal

izes

know

ledge

cust

om

ized

for

targ

eted

clie

nte

lew

ho

cannot

affo

rdin

-house

consu

ltan

ts.

Ern

ieal

low

s

consu

ltan

tsto

be

more

pro

duct

ive

and

mak

esth

eir

know

ledge

more

read

ily

avai

lable

toa

new

mar

ket

Pro

duct

ivit

y,

reputa

tion

Exte

nsi

ve

secondary activity. For each secondary activity, representa-

tive examples are provided which illustrate that it can

contribute to competitiveness.

6.1. Knowledge measurement

Measurement involves the valuation of knowledge

resources and knowledge processors, including quantitative

methods, qualitative assessment, performance review, and

benchmarking. It is a basis for evaluation of control, coor-

dination, and leadership; for identifying and recognizing

value-adding processors and resources; for assessing and

comparing the execution of KM activities; and for evaluat-

ing the impacts of an organization's conduct of KM on

bottom-line performance. Interestingly, this is an under-

implemented area, but organizations that are able to create

and use a set of measures that are tied to ®nancial results to

guide their knowledge management activities seem to come

out ahead in the long run (Hiebler, 1996).

6.1.1. Examples of knowledge measurement

Examples of knowledge measurement include appraising

intangible assets, evaluating knowledge manipulation skills

inventory, reviewing employee KM performance, measuring

the effects of individual knowledge manipulation activities or

combined activities, estimating intellectual capital ROI, eval-

uating coordination of knowledge processors and resources

in the conduct of KM. These and other examples can be

studied in terms of the foregoing characterization of knowl-

edge measurement. For example, consider the productivity of

labor. It is not only a matter of wages. Productivity comes

from knowledge capital aggregated in employees as a result

of training and relevant experience (Strassman, 1999). It is

possible to count the worth of the people who possess the

accumulated knowledge about a company. These are carriers

of knowledge capital. They possess something for which

they have spent untold hours listening and talking. Their

minds have become repositories of an accumulation of

insights on `how things work here'. If organizations spend

their money well, employees with years of experience will be

worth more than what the company pays them; the company

will be recovering the investment on its knowledge capital as

incremental pro®ts (Strassman, 1999).

In appraising its intangible assets, a business can follow

such guidelines as: (1) ask `what synergies might be driving

a buyer that wouldn't be self-evident?', (2) form a brain

trust of trusted business leaders to help hash out valuation

of the business, (3) determine the measurement purpose

(e.g. raise capital, sell the business) which should drive

the valuation process, and (4) consider factors other than

book value (Stettner, 1999).

Some organizations have developed indicators to

measure and evaluate knowledge resources and/or knowl-

edge manipulation activity. For example, the Swedish ®rm

Celemi published the world's ®rst audit of intangible assets;

Skandia uses non-®nancial indicators to measure their

processes and published the ®rst annual report supplement

on intellectual capital (Sveiby, 1997). In an effort to help

distinguish the good KM initiatives from the bad, Eli Lilly

and Co. invented a ®ve-dimensional assessment tool (Perez

& Hynes, 1999). This tool allows knowledge workers to

look at technology, process, context, people, and content

of the KM process and highlight trouble spots and increase

the probability of the success of KM initiatives.

The feasibility of measuring knowledge resources or

processes and linking them to ®nancial results and competi-

tiveness is not only dif®cult but also controversial. Two

schools of thought exist in this regard: one believes knowledge

assets and processes can be measured (Lev, 1997; Malone

1997; Stewart 1997) and the other does not (Rutledge,

1997). Baruch Lev has formulated the Knowledge Capital

Scoreboard, which is a tool for measuring the economic conse-

quences of investment in knowledge assets (Mintz, 1999).

Delphi Group President Tom Koulopoulos suggests that a

knowledge audit is a good ®rst step to manage knowledge

effectively in an organization (Delphi Group, 1999). With it,

the leadership can get an overview of the strengths and weak-

nesses of the company as a basis for analyzing their potential

for competitive advantage; without it, they may never know

what they know (Delphi Group, 1999).

6.1.2. Competitiveness via knowledge measurement: some

evidence

Table 9 summarizes some examples where knowledge

measurement has contributed to competitiveness. For

instance, Skandia Insurance Company supplements tradi-

tional accounting measures with three additional measures

from a knowledge management perspective: measure of

content knowledge resources such as customer lists and

employee competence, measure of knowledge processors

(i.e. effectiveness of internal business processes and compu-

ter systems), and measures of learning (Bassi, 1997). By

taking such measurements, Skandia has achieved an

increased earning capacity.

Abbott Laboratories is an example of a company that has

accumulated knowledge capital faster than equity capital

(Strassman, 1999). It has a stock market valuation that is a

large multiple of its ®nancial assets. Abbott's productivity

gains have not been achieved through accumulation of ®nan-

cial assets, but by using the capabilities of employees more

effectively. Behaviors of `successful' employees are observed

and classi®ed as a basis for market values of their output; thus,

it is possible to assign a dollar value to the intellectual capital

they create and use in their work. This activity of measuring

employees' KM contributions has created a competitive edge

by increased earnings and productivity.

6.2. Knowledge control

Control is concerned with ensuring that needed knowl-

edge resources and processors are available in suf®cient

quantity and quality subject to required protection and


constraints. Quality is controlled with respect to two dimen-

sions: knowledge validity (accuracy and consistency) and

knowledge utility (relevance and importance). Controlling

the quality of knowledge is a signi®cant issue for KM.

Because the value of knowledge and returns achieved

from knowledge resources depend on its quality. Protection

involves protection from loss, obsolescence, unauthorized

exposure, unauthorized modi®cation, and erroneous assim-

ilation. Approaches include legal protection, social protec-

tion, and technological protection.

6.2.1. Examples of knowledge control

Some examples of control as a KM activity include,

ensuring quality of knowledge resources and processors,

ensuring suf®ciency of knowledge resources and processors,

developing technological protection of organizational

knowledge (e.g. security safeguards), ensuring legal protec-

tion of knowledge (e.g. securing patents or copyrights), and

establishing or enforcing controls on the performance of

knowledge manipulation activities. Consider, for example

a company's patented production process, which is repre-

sented via two knowledge artifacts: the physical production

system and a patent document describing the process

(thereby preserving and protecting it). Although the process

knowledge can also reside with employees, it is the repre-

sentation as a patent document that provides legal protection

and preservation.

Having the ability to measure knowledge resources and

processors can enhance the ability to control knowledge and

this, in turn, can lead to effective management of knowledge

activities (Lotus Development Corporation, 1998). This

phenomenon is demonstrated in management consulting

®rms. More than in any other industry, the competitors in

this ®eld compete directly on the basis of knowledge and its

management.

Andersen Consulting understood that to make its knowl-

edge repository useful, fresh, and of high quality, it would

have to be more than a dumping ground of documents. It

needed to look like a library Ð complete with librarians.

Andersen spelled out speci®c job requirements for knowl-

edge professionals. These professionals, called knowledge

stewards, are subject matter experts who cull through large

quantity of documents to ensure accuracy, consistency, rele-

vance, importance, and currency, and summarize and cate-

gorize them appropriately. This set of standards established

for knowledge control has helped Andersen enormously to

effectively acquire and internalize suf®cient quantity of

knowledge without redundancy or obsolescence, to easily

select worthwhile `knowledge gems', and to externalize

them by sharing them with their clients (Lotus Development

Corporation, 1998).

An organization that intends to stay in business must have

some security measures in places to decrease the frequency

of loss. As Thomas Davenport states: `Knowledge is costly

but so is stupidity.' Knowledge management is analogous to

`risk management', because it is somewhat of a predictor to


Tab

le9

Kn

ow

led

ge

mea

sure

men

tp

ract

ices

resu

ltin

gin

com

pet

itiv

enes

s

Cit

atio

nA

ctio

nto

add

val

ue

Sta

ted

com

pet

itiv

eim

pac

tD

egre

eof

tech

nolo

gy

involv

ed

1.

Bas

si,

19

97

Sk

and

iaIn

sura

nce

Com

pan

yval

ues

its

cust

om

erli

st,

emplo

yee

s'co

mpet

ence

,co

mpute

rsy

stem

s

and

bu

sin

ess

pro

cess

esy

ield

ing

gre

ater

pro

duct

ivit

y,

enhan

ced

imag

e,an

din

crea

sed

inco

me.

Pro

duct

ivit

y,

reputa

tion

Lit

tle

2.

Str

assm

an,

19

99

Ab

bo

ttL

abo

rato

ries

mea

sure

sK

Mca

pab

ilit

ies

of

its

emplo

yee

sre

sult

ing

inpro

duct

ivit

yan

d

enh

ance

dea

rnin

gca

pac

ity

Pro

duct

ivit

yL

ittl

e

3.

Ste

ttn

er,

19

99

As

are

sult

of

mea

suri

ng

kn

ow

ledge

and

know

ing

the

true

wort

hof

its

busi

nes

s,T

VO

nT

he

Web

,

aR

esto

n,

Vir

gin

ia-b

ased

inte

ract

ive

TV

net

work

was

able

toat

trac

t®

nan

cing

from

ast

rate

gic

inves

tor

that

hel

ped

the

com

pan

ygro

w.

An

indep

enden

tap

pra

iser

assi

gned

aval

uat

ion

toa

know

ledge

reso

urc

eco

mpri

sed

of

ali

bra

ryof

10,0

00

vid

eota

pes

.T

his

mea

sure

men

tac

tivit

y

add

edv

alue

toth

eb

usi

nes

san

den

han

ced

its

imag

e

Rep

uta

tion

Lit

tle

4.

Wah

,1

99

9B

riti

shP

etro

leu

m,a

nad

van

ced

pra

ctit

ioner

of

KM

,use

scl

earl

yde®

ned

obje

ctiv

esth

athav

em

ade

the

mea

sure

men

to

fK

Mre

sult

sm

uch

easi

er.

Whic

hin

turn

hel

ps

This

has

resu

lted

insi

gni®

cant

bu

sin

ess

imp

rov

emen

tsan

dhel

ped

convin

cese

nio

rm

anag

emen

tof

the

ben

e®ts

of

KM

Pro

duct

ivit

yM

oder

ate

how much security the corporation has (Marshall et al.,

1996). At Kidder Peabody, for example, Joe Jett (a govern-

ment securities trader) managed to create $350 million in

phantom pro®ts from his manipulation of the ®rm's trading

and accounting system because KM control functions were

inadequate (Marshall et al., 1996).

6.2.2. Competitiveness via knowledge control: some

evidence

Peapod, described as the leading Internet supermarket,

serves eight metropolitan markets in the US and has

surpassed one million orders placed via its online service

(Gotschall, 1999). The quantity and quality of consumer

knowledge Peapod collects via its Web site is so valuable

that it has enabled an initiative with large packaged goods

companies such as Kraft Foods, Nestle, and Ralston Purina.

Under this initiative, Peapod designs and executes research

projects for these companies regarding the effectiveness of

many marketing tactics unique to the Internet distribution

channel. Thus, the attention given to securing suf®cient

quantity and quality of knowledge has enhanced Peapod's

reputation and resulted in more productive (i.e. pro®table)

use of its knowledge.

Sometimes, the value of a knowledge business can be

boosted by a knowledge control policy that is not too

heavy-handed. A case in point is Incyte Pharmaceutical. It

achieved a market capitalization of over $600 million in six

years by licensing its gene sequencing knowledge non-

exclusively to large pharmaceutical companies (McKinsey

Quarterly, 1998). In so doing, it acquired access to the

knowledge of its partners and using it generated a standard

platform for the provision of all genomic data that becomes

increasingly valuable as more companies use it (McKinsey

Quarterly, 1998). Another example is Netscape, which is

apparently giving away knowledge that cost millions of

dollars to generate Ð knowledge that most companies

would guard jealously. It has made the source code of its

browser products available, at no cost and under generous

licensing provisions, to anyone who visits its Internet Web

site with the hope that the efforts of many programmers

outside the company will turn its products into a valuable

standard (McKinsey Quarterly, 1998).

Dow Chemical's patent archive used to be so disorga-

nized as to severely limit its usability (Mullin, 1996).

Recognizing this, a control initiative was launched in

which Dow undertook the task of putting the knowledge

resources in order by assessing, categorizing, and protecting

its content from loss and obsolescence. This initiatives has

fostered greater productivity in the use of patents, earning

Dow higher income through licensing its technology. It has

also enhanced Dow's agility by ensuring that knowledge

can be more readily selected by research, manufacturing,

and marketing staff.

Table 10 summarizes several examples that suggest how

knowledge control contributes to competitiveness.

6.3. Knowledge coordination

Coordination refers to guiding the conduct of KM in an

organization. It involves managing dependencies among

knowledge resources, among knowledge manipulation

activities, between knowledge resources and other resources

(i.e. ®nancial, human, and material), and between knowl-

edge resources and KM activities. It involves marshaling

suf®cient skills for executing various activities, arrangement

of those activities in time, and integrating knowledge

processing with an organization's operations. Coordination

approaches suggested and used include linking incentives to

desired KM behaviors and outcomes, guiding knowledge

manipulation activities, establishing facile communications

channels for knowledge ¯ows, and constructing programs to

encourage learning. An organization's approach to problem

solving, decision making, experimentation, and organiza-

tional learning Ð all of which are knowledge-intensive

endeavors Ð can depend on how it coordinates its KM

activities.

6.3.1. Examples of knowledge coordination

Representative examples of knowledge coordination

activity include, establishing incentives for appropriate

KM behaviors, determining appropriate communication

channels for knowledge ¯ows, installing programs to

encourage learning, structuring of the patterns of knowl-

edge work within a KM activity, con®guring knowledge

activities within a KM episode, con®guring knowledge

management episodes, and assigning appropriate proces-

sors to KM activities within and across KM episodes.

Each of these examples can be examined in much greater

detail and in terms of speci®c instances. A speci®c

instance of the ®rst example is found at Integral Inc.,

which made knowledge sharing part of the performance

review of employees; to get good reviews and bonuses

employees had to share their knowledge (Crowley,

1997). Similarly, at Buckman Labs, incentive, evaluation,

and promotion systems are structured to reward employ-

ees who share and transfer knowledge and punish those

who do not (Rifkin, 1997). Thus, the conduct of KM in the

organization can be guided by encouraging desired KM

behaviors. Another example of coordination is seen in

programs that promote organizational learning. For

instance, at one consulting ®rm, professionals are

expected to document what they have learned about

what works and what does not work, and they are partially

compensated based on how often their documentation is

accessed from a central knowledge repository (Marshall et

al., 1996). Here again, coordination practices guide KM

behaviors of an organization's participants.

6.3.2. Competitiveness via knowledge coordination: some

evidence

Table 11 summarizes several illustrations of situations

where coordination activities contribute to competitiveness.



Tab

le1

0

Know

ledge

contr

ol

pra

ctic

esre

sult

ing

inco

mpet

itiv

enes

s

Cit

atio

nA

ctio

nto

add

val

ue

Sta

ted

com

pet

itiv

eim

pac

tD

egre

eof

tech

nolo

gy

involv

ed

1.

Go

tsch

all,

19

99

Usi

ng

up

gra

ded

tech

nolo

gy

and

consu

mer

dir

ecti

ons

init

iati

ves

,P

eapod

acquir

edco

nsu

mer

kn

ow

led

ge

of

suf®

cien

tquan

tity

and

qual

ity

(fro

mit

sW

ebsi

te)

tose

llto

larg

epac

kag

edgoods

com

pan

ies

such

asK

raft

Foods,

Nes

tle,

and

Ral

ston

Puri

na.

This

has

enhan

ced

Pea

pod's

rep

uta

tio

nan

dp

ro®

ts

Pro

duct

ivit

y,

reputa

tion

Moder

ate

2.

McK

inse

yQ

uar

terl

y,

1998

By

lice

nsi

ng

its

gen

ese

quen

cing

know

ledge

non-e

xcl

usi

vel

yto

larg

ephar

mac

euti

cal

com

pan

ies,

Incy

teP

har

mac

euti

cal

acquir

edac

cess

toth

eir

know

ledge

and

wit

hit

gen

erat

eda

stan

dar

dp

latf

orm

for

all

gen

om

icdat

ath

atbec

om

esin

crea

singly

val

uab

leas

more

com

pan

ies

use

it

Pro

duct

ivit

yL

ittl

e

3.

McK

inse

yQ

uar

terl

y,

19

98

Net

scap

eh

asm

ade

the

sourc

eco

de

of

its

bro

wse

rpro

duct

avai

lable

atno

cost

and

under

gen

ero

us

lice

nse

pro

vis

ions

wit

hth

ehope

that

outs

ide

pro

gra

mm

ers

wil

ltu

rnit

spro

duct

sin

to

av

aluab

lest

andar

d

Pro

duct

ivit

yL

ittl

e

4.

Mu

llin

,1

99

6D

ow

un

der

too

kp

aten

tm

ainte

nan

ceby

asse

ssin

g,

cate

gori

zing,

and

pro

tect

ing

this

val

uab

le

kn

ow

led

ge

reso

urc

efr

om

loss

and

obso

lesc

ence

.T

hes

epat

ent

mai

nte

nan

cein

itia

tives

hav

e

earn

edD

ow

ah

igh

inco

me

thro

ugh

lice

nsi

ng

its

tech

nolo

gy

and

mak

eth

atknow

ledge

more

read

ily

acce

ssib

leto

rese

arch

,m

anufa

cturi

ng,

and

mar

ket

ing

staf

f

Pro

duct

ivit

y,

agil

ity

Moder

ate

5.

Do

nlo

nan

dH

aap

anei

mi,

19

97

Cap

ital

On

ese

eks

tose

cure

know

ledge

qual

ity

by

recr

uit

ing

the

smar

test

peo

ple

bec

ause

`sca

le

and

ef®

cien

cyin

its

trad

itio

nal

mea

nin

gis

n't

asim

port

ant

asknow

ing

the

cust

om

eran

d

cate

ring

tohis

/her

real

pre

fere

nce

s.'

This

poli

cyhas

hel

ped

Cap

ital

One

togai

na

larg

er

nu

mb

ero

fcu

sto

mer

s

Rep

uta

tion

Lit

tle

6.

Bro

adben

t,1998

When

Hoff

man

-LaR

och

eev

aluat

edit

spro

duct

dev

elopm

ent

know

ledge

and

corr

espondin

g

KM

acti

vit

ies,

itfo

und

that

that

itdid

not

alw

ays

com

munic

ate

consi

sten

tkey

mes

sages

and

som

etim

esin

clu

ded

contr

adic

tory

,am

big

uous,

and

inap

pro

pri

ate

info

rmat

ion.

This

led

Hoff

man

todev

elop

asy

stem

atic

corp

ora

teknow

ledge

map

allo

win

gti

mel

y,

accu

rate

,an

d

con

sist

ent

(i.e

.q

ual

ity)

know

ledge

dis

trib

uti

on

toem

plo

yee

s.T

his

resu

lted

insh

ippin

gm

ore

dru

gs

mo

rera

pid

ly

Pro

duct

ivit

y,

agil

ity

Exte

nsi

ve

At Honda, coordination practices in the conduct of KM are

driven by a set of objectives that link KM practices to the

speed of development processes (Broadbent, 1998). These

objectives include: establishing a communications network

for quick and easy knowledge sharing on global scale;

establishing communication systems to facilitate high qual-

ity person-to-person interaction among R&D staff and

between R&D, production, operations and marketing

personnel; ensuring that these communications facilities

support the transfer of sophisticated design concepts, data,

and documentation in a high quality and cost-ef®cient

manner. To meet these objectives, Honda established a

full-service international communications network system

(called Pentaccord) and a system to manage selected data-

bases (sales, ®nance, and part ordering) on a global basis

with considerable synergies between these systems for

effective coordination of knowledge sharing. Sharing of

expertise, rapid exchange of R&D knowledge, and technical

and human communications capabilities have given Honda

characteristics, structures and processes of a learning orga-

nization and have resulted in a competitive advantage

through greater R&D productivity and innovation, plus

development agility.

At Buckman Labs, competitiveness depends on training

and educating employees about the latest industry develop-

ments and the businesses of their customers (Lotus Devel-

opment Corporation, 1998). To guide such behavior,

employees are incented to actively seek out continuing

opportunities for competency development. Buckman has

installed an on-line training program to augment employee

learning and education with such coordination features as

allowing students to study as their schedule allowed,

encouraging students/employees to work together on

assignments, and interaction between students and instruc-

tors at any level the student desires Ð privately with the

instructor, privately with one or more other students, or

publicly with the entire class.

6.4. Knowledge leadership

Of the four secondary KM activities, leadership is primary.

It sets the tone (i.e. shapes the culture) for coordination,

control, and measurement that manifest. It quali®es the expres-

sion of each primary activity. In short, leadership establishes

enabling conditions for achieving fruitful KM through the

other eight activities. The distinguishing characteristic of

leadership is that of being a catalyst through such traits as

inspiring, mentoring, setting examples, engendering trust

and respect, instilling a cohesive and creative culture, estab-

lishing a vision, listening, learning, teaching, and knowledge

sharing. A study by Andersen and APQC stated that one

crucial reason why organizations are unable to effectively

leverage knowledge is because of a `lack of commitment of

top leadership to sharing organizational knowledge or there

are too few role models who exhibit the desired behavior'

(Hiebler, 1996). `¼knowledge management is as much


Tab

le1

1

Know

ledge

coord

inat

ion

pra

ctic

esre

sult

ing

inco

mpet

itiv

enes

s

Cit

atio

nA

ctio

nto

add

val

ue

Sta

ted

com

pet

itiv

eim

pac

tD

egre

eof

tech

nolo

gy

involv

ed

1.

Bro

adb

ent,

19

98

Ho

nd

au

ses

busi

nes

s,in

form

atio

nan

dte

chnolo

gy

obje

ctiv

esas

stan

dar

ds

for

coo

rdin

atin

git

sK

Mac

tivit

ies

ina

way

that

spee

ds

dev

elopm

ent

pro

cess

esan

d

has

led

toco

mpet

itiv

ead

van

tage.

Pro

duct

ivit

y,

reputa

tion,

agil

ity,

innovat

ion

Exte

nsi

ve

2.

Lotu

sD

evel

opm

ent

Corp

ora

tion,

1998

Buck

man

Lab

s'em

plo

yee

sar

ein

cente

dto

acti

vel

yse

ekout

conti

nuin

g

opport

unit

ies

for

com

pet

ency

dev

elopm

ent

via

hig

hly

coord

inat

eddis

tance

lear

nin

gsy

stem

s

Pro

duct

ivit

y,

reputa

tion,

innovat

ion

Exte

nsi

ve

3.

McC

un

e,1

99

9W

.L

.G

ore

and

Ass

oci

ates

has

esta

bli

shed

aco

mm

unic

atio

ns

stru

cture

usi

ng

Lo

tus

No

tes

and

anIn

tran

etth

atal

low

speo

ple

inth

e®

eld

toquic

kly

rela

y

cust

om

ers'

nee

ds

toa

pro

duct

dev

elopm

ent

team

,w

hic

hin

turn

can

quic

kly

dev

ise

cust

om

ized

pro

duct

s.T

his

pro

cess

,w

hic

hto

ok

wee

ks

inth

epas

t,now

tak

eso

nly

day

sor

even

hours

tore

spond

tocu

stom

erord

er

Pro

duct

ivit

y,

agil

ity

Exte

nsi

ve

4.

McC

artn

ey,

1998

To

pro

mote

effe

ctiv

eco

ord

inat

ion,

DuP

ont

and

Co.

isbre

akin

gdow

nth

e

trad

itio

nal

org

aniz

atio

nal

hie

rarc

hy

on

its

pla

nt

¯oors

This

faci

lita

tes

com

mun

icat

ion

chan

nel

sfo

rknow

ledge

¯ow

sam

ong

work

ers

and

has

mad

e

them

mo

repro

duct

ive

Pro

duct

ivit

yL

ittl

e

about leadership, culture, and behavior,' states Neil Ashton,

Head of British Petroleum's Information Technology Archi-

tecture and Strategy (Microsoft, 1999). John Kotter, a Harvard

professor, makes distinctions between management (i.e. plan-

ning and budgeting; organizing and staf®ng; controlled

problem-solving; predicting results) and leadership (i.e. vision

of the future; aligning people; motivating and inspiring; creat-

ing change) (Amidon, 1997).

6.4.1. Examples of knowledge leadership

Some senior management started to see the value of KM

once they began cruising the Information Highway and this

has lead to creating conditions conducive to effective under-

taking of KM activities in their own organization (McCart-

ney, 1998). CEOs come to realize that they have to manage

their organization's intellectual assets the same way they

manage physical assets. This means ®nding, understanding,

and reusing best practices in bringing products to market,

cutting cycle time, and improving defect analysis and custo-

mer service.

To put knowledge into action in an organization Sutton and

Pfeffer, authors of the book The Knowing-Doing Gap, give the

following suggestions for top executives 1) To make a ®rm

action-oriented in KM, senior managers will have to reward

half-baked ideas as they allow coworkers to visualize solutions

easily and make follow-on suggestions. 2) Senior managers

also have to drive the sti¯ing fear of making a mistake out of

the organization by building relationships based on trust

between managers and subordinates, and between peers and

coworkers. 3) People need to focus more on external compe-

titors rather than on internal competitors. Management needs

to put in place a corporate culture based not on competition but

on cooperation by forming cross-functional teams, for exam-

ple. Instead of reining in such teams after they get a good start,

managers should just get out of the way except when they need

to settle con¯icts. 4) To move knowledge into action,

managers should keep rules to a minimum. Instead of setting

down rules that hem in people's creativity, managers need to

give them tools to raise their productivity (Achstatter, 1999).

6.4.2. Competitiveness via knowledge leadership: some

evidence

British Petroleum is one of the world's largest petroleum

and petrochemicals companies. CEO John Browne has been

making his vision of BP a reality as a world-class, global,

agile learning organization via knowledge management

(Microsoft, 1999). BP's knowledge management initiative

was established in January 1997. It was believed that a

signi®cant improvement in business performance would

result if BP could harness what it already knows, rapidly

learn from others, and quickly apply that knowledge to busi-

ness situations, through collaboration of teams and indivi-

duals independently of the organizational structure.

To make BP's vision a reality, the Common Operating

Environment (COE) system that standardizes the software

and hardware platforms of BP of®ces globally has been

C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±9896T

able

12

Know

ledge

lead

ersh

ippra

ctic

esre

sult

ing

inco

mpet

itiv

enes

s

Cit

atio

nA

ctio

nto

add

val

ue

Sta

ted

com

pet

itiv

eim

pac

tD

egre

eof

tech

nolo

gy

involv

ed

1.

Mic

roso

ft,

19

99

BP

'sle

ader

ship

'sv

isio

no

fb

ein

ga

worl

d-c

lass

,glo

bal

,ag

ile

lear

nin

gorg

aniz

atio

nvia

know

ledge

man

agem

ent

is

imple

men

ted

via

the

CO

Esy

stem

.C

OE

isnot

just

about

tech

nolo

gy

but

also

about

chan

gin

gbeh

avio

ran

dcu

lture

ina

way

that

add

edv

alu

esi

gn

i®ca

ntl

yto

the

org

aniz

atio

n

Pro

duct

ivit

y,

reputa

tion,

agil

ity,

innovat

ion

Exte

nsi

ve

2.

Cohen

,1998

Sat

urn

'sle

ader

ship

in¯

uen

ces

org

aniz

atio

nal

cult

ure

by

dem

onst

rati

ng

the

know

ledge

acti

vit

ies

itis

tryin

gto

fost

er,as

wel

las

by

reco

gn

izin

gan

dre

war

din

gth

atbeh

avio

rin

oth

ers.

This

has

enhan

ced

emplo

yee

s'm

ora

lean

d

pro

duct

ivit

ysi

gn

i®ca

ntl

y

Pro

duct

ivit

y,

reputa

tion

Lit

tle

3.

Bu

sot,

19

99

Th

eC

arib

bea

nan

dL

atin

Am

eric

an(C

AL

A)

div

isio

nof

Nort

elN

etw

ork

sC

orp

.pro

duce

san

dai

rsit

sV

irtu

al

Lea

der

ship

Aca

dem

y(V

LA

)li

ve

sho

wto

47

countr

ies

once

am

onth

usi

ng

one-

way

vid

eoan

dtw

o-w

ayau

dio

.VL

A

educa

tes

wid

ely

dis

per

sed

emplo

yee

sab

outst

rate

gic

ally

crit

ical

issu

esan

dle

tsth

emta

pin

tole

ader

ship

abil

ity

and

exper

tise

thro

ughout

the

com

pan

y.

Ithel

ps

emplo

yee

sunder

stan

dN

ort

el's

stra

tegy

and

how

toca

rry

itout

Pro

duct

ivit

y,

innovat

ion

Moder

ate

4.

Co

hen

,1

99

8A

tP

ills

bury

,th

ech

ief

tech

nic

alo

f®ce

r's

bel

ief

inth

eim

port

ance

of

KM

bro

ught

the

subje

ctto

the

atte

nti

on

of

the

CE

Oan

dC

IO.

Th

eco

mm

itm

ent

of

all

thre

ecr

eate

dco

ndit

ions

for

effe

ctiv

eK

Mvia

reso

urc

esan

dvis

ibil

ity

for

Pil

sbu

ry's

Tec

h-K

no

w-N

etin

itia

tiv

e,an

exte

nsi

ve

Note

s-bas

edsy

stem

that

incl

udes

anex

per

tdat

abas

e,co

llec

tions

of

team

dat

a,an

da

vas

tco

llec

tio

no

fco

rpora

tein

form

atio

n

Pro

duct

ivit

y,

reputa

tion

Exte

nsi

ve

5.

LaP

lan

te,

19

97

Th

ele

ader

ship

atM

etro

po

lita

nL

ife

Insu

rance

Co.

isch

angin

git

sm

ind-s

etab

out

its

ITcu

lture

.It

wan

tsth

e

wo

rldw

ide

staf

fo

fp

rog

ram

mer

san

dan

alyst

sto

shar

eth

eir

know

ledge

and

exper

tise

.T

he

pra

ctic

eof

coll

ecti

ve

exp

erie

nce

,k

no

w-h

ow

and

wis

do

m,

and

mak

ing

itav

aila

ble

toan

yone

who

nee

ds

itvia

KM

has

signi®

cantl

y

boost

edem

plo

yee

s'pro

duct

ivit

yan

dcr

eati

vit

y

Pro

duct

ivit

y,

innovat

ion

Lit

tle

deployed. Each user is given one-day training in the use of

COE. `The COE isn't just about technology,' explains Neil

Ashton, Head of BP's Information Technology Architecture

and Strategy, `its about behavior, culture, and thinking glob-

ally while acting locally. We can provide the tools and teach

people to use them, but that is only the ®rst step. They have

to be given incentives to help them learn and share knowl-

edge before they can exploit the full potential of the orga-

nization or its underlying infrastructure. The culture has to

say that it is right to take time to re¯ect on what you did last

time in order to improve what you do next time. It is part of

the evolution of our culture towards becoming a world-class

learning organization.' The vision of BP's leadership and its

creation of conditions for effective KM performance has

helped make BP a true learning organization with more

responsive business processes and increased employee

productivity.

Saturn wants to become a `learning organization with a

bias for action,' but reaching that goal depends on building

new cultural habits and assumptions (Cohen, 1998). Saturn

recognizes that leaders can have a powerful in¯uence on

organizational culture by demonstrating the knowledge

activities they are trying to foster, as well as by recognizing

and rewarding that behavior in others. To help create this

coherent culture, senior managers (including CEOs) teach

courses explaining their perspectives on the business. Such

participation gives employees a context for understanding

their own work and a clear indication that they are all in it

together and each has a stake in the performance of every

member of the organization. This KM leadership activity

has improved employees' morale and productivity signi®-

cantly. Illustrations suggestive of the KM leadership activi-

ty's contribution to competitiveness appear in Table 12.

7. Conclusions

For an organization to have a competitive advantage, it is

imperative that it adopts, designs, and executes knowledge

management activities better than other organizations. This

paper has presented a model of nine speci®c KM activities

that appear to be common across various organizations. The

model contends that individually and in combination these

KM activities can be contributors to competitiveness.

Evidence from the literature provides support for this conten-

tion. Thus, rather than simply saying that KM can yield a

competitive advantage, the knowledge chain model provides

structure to researchers and practitioners for considering

speci®c KM activities that can be sources of competitiveness.

The KM activities identi®ed in the model are not the only

determining factors that can lead to competitiveness. There are

other forces that in¯uence how the conduct of KM ultimately

unfolds in an organization: resource in¯uences and environ-

mental in¯uences (Joshi, 1998). This suggests that the model

portrayed in Fig. 1 could be extended to include resource and

environmental factors, which both constrain and enable the

execution of KM activities. One future research direction is

to investigate this extension. Another direction is to add

greater depth to the model via further detailing of the KM

activities. In this regard, further evidence in support of the

knowledge chain model should be sought (e.g. through case

studies or a survey of chief knowledge of®cers).

The model being advanced here is descriptive in nature.

The intent is to identify KM activities that researchers and

practitioners need to consider in managing knowledge to

achieve competitiveness. An obvious next step is to explore

on how to actually use the model effectively in framing

prescriptions for KM. Arthur Andersen has developed a

tool that outlines 27 best practices in knowledge manage-

ment, some of which are technology-based (Wah, 1999).

Similarly, prescriptive issues with regard to the knowledge

chain model may include such matters as identifying `best

practices' for contributing to competitiveness via the

conduct of KM activities and means for ensuring sustainable

competitive advantage. There are also technical issues such

as to enable, facilitate, or enhance execution of the knowl-

edge chain activities.

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