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
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±9880
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
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±98 81
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
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±9882
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±98 83
Tab
le4
Kn
ow
led
ge
acq
uis
itio
np
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.
Leo
nar
d-B
arto
n,
19
95
Mo
nit
ori
ng
,ra
pid
lyac
quir
ing,
and
invokin
gnew
tech
nolo
gic
al
adv
ance
sfo
rst
eel
pro
duct
sat
Chap
arra
l.S
teel
Pro
duct
ivit
y,
reputa
tion,
agil
ity,
innovat
ion
Lit
tle
2.
Ken
ned
y,
1997
Usi
ng
Web
tech
nolo
gy
toid
enti
fy,
eval
uat
e,an
alyze
,qual
ify,
and
accu
mula
teex
tern
ally
crea
ted
know
ledge
conte
nt
atD
EC
Rep
uta
tion,
agil
ity
Exte
nsi
ve
3.
Ste
war
t,1
99
7C
ereg
enco
nti
nuo
usl
yac
quir
esco
mpan
ypro
®le
san
dnew
sfr
om
pu
bli
cso
urc
es,
kn
ow
led
ge
from
cust
om
ers
via
ques
tionnai
res,
kn
ow
led
ge
from
con
fere
nce
san
dco
nven
tions
via
emplo
yee
atte
nd
ance
,etc
.fo
rin
tern
aliz
atio
nin
adat
abas
e.T
his
hel
ps
Cer
egen
achie
ve
low
erco
stan
dra
pid
resp
onsi
ven
ess
ina
fast
-movin
g
bu
sin
ess
lik
eb
iote
ch
Pro
duct
ivit
y,
innovat
ion
Moder
ate
4.
Leo
nar
d-B
arto
n,
19
95
Ko
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
escu
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
and
captu
res
cust
om
erfe
edbac
k
via
ora
lfo
rm,
op
en-e
nded
ques
tions,
and
body
languag
e;an
d
tran
sfer
sk
no
wle
dg
eac
quir
edto
mar
ket
ing
and
pro
duct
dev
elo
pm
ent
for
kn
ow
ledge
gen
erat
ion.
This
has
hel
ped
Bla
ckan
d
Dec
ker
no
to
nly
tocr
eate
new
pro
duct
san
dse
rvic
esbut
also
to
enh
ance
its
imag
e.
Rep
uta
tion,
innovat
ion
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
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±9884
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±98 85
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
hra
pid
sale
sgro
wth
and
70
rapid
-®re
acqu
isit
ions,
use
sa
quer
yover
Intr
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
7A
man
agin
gd
irec
tor
of
Buck
man
Lab
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
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±9886
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±98 87
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,
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±9888
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
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±98 89
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
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±9890
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
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±98 91
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
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±9892
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.
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±98 93
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±9894
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
C.W. Holsapple, M. Singh / Expert Systems with Applications 20 (2001) 77±98 95
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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